Geographical Clusters of Rape in the United States: 2000-2012

PubMed Central

Amin, Raid; Nabors, Nicole S.; Nelson, Arlene M.; Saqlain, Murshid; Kulldorff, Martin

2016-01-01

Background While rape is a very serious crime and public health problem, no spatial mapping has been attempted for rape on the national scale. This paper addresses the three research questions: (1) Are reported rape cases randomly distributed across the USA, after being adjusted for population density and age, or are there geographical clusters of reported rape cases? (2) Are the geographical clusters of reported rapes still present after adjusting for differences in poverty levels? (3) Are there geographical clusters where the proportion of reported rape cases that lead to an arrest is exceptionally low or exceptionally high? Methods We studied the geographical variation of reported rape events (2003-2012) and rape arrests (2000-2012) in the 48 contiguous states of the USA. The disease Surveillance software SaTScan™ with its spatial scan statistic is used to evaluate the spatial variation in rapes. The spatial scan statistic has been widely used as a geographical surveillance tool for diseases, and we used it to identify geographical areas with clusters of reported rape and clusters of arrest rates for rape. Results The spatial scan statistic was used to identify geographical areas with exceptionally high rates of reported rape. The analyses were adjusted for age, and in secondary analyses, for both age and poverty level. We also identified geographical areas with either a low or a high proportion of reported rapes leading to an arrest. Conclusions We have identified geographical areas with exceptionally high (low) rates of reported rape. The geographical problem areas identified are prime candidates for more intensive preventive counseling and criminal prosecution efforts by public health, social service, and law enforcement agencies Geographical clusters of high rates of reported rape are prime areas in need of expanded implementation of preventive measures, such as changing attitudes in our society toward rape crimes, in addition to having the criminal justice system play an even larger role in preventing rape. PMID:28078318

Geographical Clusters of Rape in the United States: 2000-2012.

PubMed

Amin, Raid; Nabors, Nicole S; Nelson, Arlene M; Saqlain, Murshid; Kulldorff, Martin

2015-01-01

While rape is a very serious crime and public health problem, no spatial mapping has been attempted for rape on the national scale. This paper addresses the three research questions: (1) Are reported rape cases randomly distributed across the USA, after being adjusted for population density and age, or are there geographical clusters of reported rape cases? (2) Are the geographical clusters of reported rapes still present after adjusting for differences in poverty levels? (3) Are there geographical clusters where the proportion of reported rape cases that lead to an arrest is exceptionally low or exceptionally high? We studied the geographical variation of reported rape events (2003-2012) and rape arrests (2000-2012) in the 48 contiguous states of the USA. The disease Surveillance software SaTScan™ with its spatial scan statistic is used to evaluate the spatial variation in rapes. The spatial scan statistic has been widely used as a geographical surveillance tool for diseases, and we used it to identify geographical areas with clusters of reported rape and clusters of arrest rates for rape. The spatial scan statistic was used to identify geographical areas with exceptionally high rates of reported rape. The analyses were adjusted for age, and in secondary analyses, for both age and poverty level. We also identified geographical areas with either a low or a high proportion of reported rapes leading to an arrest. We have identified geographical areas with exceptionally high (low) rates of reported rape. The geographical problem areas identified are prime candidates for more intensive preventive counseling and criminal prosecution efforts by public health, social service, and law enforcement agencies Geographical clusters of high rates of reported rape are prime areas in need of expanded implementation of preventive measures, such as changing attitudes in our society toward rape crimes, in addition to having the criminal justice system play an even larger role in preventing rape.

Effects of multiple founder populations on spatial genetic structure of reintroduced American martens.

PubMed

Williams, Bronwyn W; Scribner, Kim T

2010-01-01

Reintroductions and translocations are increasingly used to repatriate or increase probabilities of persistence for animal and plant species. Genetic and demographic characteristics of founding individuals and suitability of habitat at release sites are commonly believed to affect the success of these conservation programs. Genetic divergence among multiple source populations of American martens (Martes americana) and well documented introduction histories permitted analyses of post-introduction dispersion from release sites and development of genetic clusters in the Upper Peninsula (UP) of Michigan <50 years following release. Location and size of spatial genetic clusters and measures of individual-based autocorrelation were inferred using 11 microsatellite loci. We identified three genetic clusters in geographic proximity to original release locations. Estimated distances of effective gene flow based on spatial autocorrelation varied greatly among genetic clusters (30-90 km). Spatial contiguity of genetic clusters has been largely maintained with evidence for admixture primarily in localized regions, suggesting recent contact or locally retarded rates of gene flow. Data provide guidance for future studies of the effects of permeabilities of different land-cover and land-use features to dispersal and of other biotic and environmental factors that may contribute to the colonization process and development of spatial genetic associations.

Spatial dynamics of invasion: the geometry of introduced species.

PubMed

Korniss, Gyorgy; Caraco, Thomas

2005-03-07

Many exotic species combine low probability of establishment at each introduction with rapid population growth once introduction does succeed. To analyse this phenomenon, we note that invaders often cluster spatially when rare, and consequently an introduced exotic's population dynamics should depend on locally structured interactions. Ecological theory for spatially structured invasion relies on deterministic approximations, and determinism does not address the observed uncertainty of the exotic-introduction process. We take a new approach to the population dynamics of invasion and, by extension, to the general question of invasibility in any spatial ecology. We apply the physical theory for nucleation of spatial systems to a lattice-based model of competition between plant species, a resident and an invader, and the analysis reaches conclusions that differ qualitatively from the standard ecological theories. Nucleation theory distinguishes between dynamics of single- and multi-cluster invasion. Low introduction rates and small system size produce single-cluster dynamics, where success or failure of introduction is inherently stochastic. Single-cluster invasion occurs only if the cluster reaches a critical size, typically preceded by a number of failed attempts. For this case, we identify the functional form of the probability distribution of time elapsing until invasion succeeds. Although multi-cluster invasion for sufficiently large systems exhibits spatial averaging and almost-deterministic dynamics of the global densities, an analytical approximation from nucleation theory, known as Avrami's law, describes our simulation results far better than standard ecological approximations.

Spatial analysis of dengue fever in Guangdong Province, China, 2001-2006.

PubMed

Liu, Chunxiao; Liu, Qiyong; Lin, Hualiang; Xin, Benqiang; Nie, Jun

2014-01-01

Guangdong Province is the area most seriously affected by dengue fever in China. In this study, we describe the spatial distribution of dengue fever in Guangdong Province from 2001 to 2006 with the objective of informing priority areas for public health planning and resource allocation. Annualized incidence at a county level was calculated and mapped to show crude incidence, excess hazard, and spatial smoothed incidence. Geographic information system-based spatial scan statistics was conducted to detect the spatial distribution pattern of dengue fever incidence at the county level. Spatial scan cluster analyses suggested that counties around Guangzhou City and Chaoshan Region were at increased risk for dengue fever (P < .01). Some spatial clusters of dengue fever were found in Guangdong Province, which allowed intervention measures to be targeted for maximum effect.

Race, deprivation, and immigrant isolation: The spatial demography of air-toxic clusters in the continental United States.

PubMed

Liévanos, Raoul S

2015-11-01

This article contributes to environmental inequality outcomes research on the spatial and demographic factors associated with cumulative air-toxic health risks at multiple geographic scales across the United States. It employs a rigorous spatial cluster analysis of census tract-level 2005 estimated lifetime cancer risk (LCR) of ambient air-toxic emissions from stationary (e.g., facility) and mobile (e.g., vehicular) sources to locate spatial clusters of air-toxic LCR risk in the continental United States. It then tests intersectional environmental inequality hypotheses on the predictors of tract presence in air-toxic LCR clusters with tract-level principal component factor measures of economic deprivation by race and immigrant status. Logistic regression analyses show that net of controls, isolated Latino immigrant-economic deprivation is the strongest positive demographic predictor of tract presence in air-toxic LCR clusters, followed by black-economic deprivation and isolated Asian/Pacific Islander immigrant-economic deprivation. Findings suggest scholarly and practical implications for future research, advocacy, and policy. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatial Analysis of HIV Positive Injection Drug Users in San Francisco, 1987 to 2005

PubMed Central

Martinez, Alexis N.; Mobley, Lee R.; Lorvick, Jennifer; Novak, Scott P.; Lopez, Andrea M.; Kral, Alex H.

2014-01-01

Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug users (IDUs) in San Francisco between 1987 and 2005 (N = 29,914). HIV testing was conducted for every participant. Participant residence was geocoded to the level of the United States Census tract for every observation in dataset. Local indicator of spatial autocorrelation (LISA) tests were used to identify univariate and bivariate Census tract clusters of HIV positive IDUs in two time periods. We further compared three tract level characteristics (% poverty, % African Americans, and % unemployment) across areas of clustered and non-clustered tracts. We identified significant spatial clustering of high numbers of HIV positive IDUs in the early period (1987–1995) and late period (1996–2005). We found significant bivariate clusters of Census tracts where HIV positive IDUs and tract level poverty were above average compared to the surrounding areas. Our data suggest that poverty, rather than race, was an important neighborhood characteristic associated with the spatial distribution of HIV in SF and its spatial diffusion over time. PMID:24722543

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation. Conclusion High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage. PMID:24314148

Measuring Spatial Dependence for Infectious Disease Epidemiology

PubMed Central

Grabowski, M. Kate; Cummings, Derek A. T.

2016-01-01

Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases. PMID:27196422

Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

PubMed Central

Sloan, Chantel D.; Nordsborg, Rikke B.; Jacquez, Geoffrey M.; Raaschou-Nielsen, Ole; Meliker, Jaymie R.

2015-01-01

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population. PMID:25756204

Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

PubMed

Sloan, Chantel D; Nordsborg, Rikke B; Jacquez, Geoffrey M; Raaschou-Nielsen, Ole; Meliker, Jaymie R

2015-01-01

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

Tree growth and competition in an old-growth Picea abies forest of boreal Sweden: influence of tree spatial patterning

USGS Publications Warehouse

Fraver, Shawn; D'Amato, Anthony W.; Bradford, John B.; Jonsson, Bengt Gunnar; Jönsson, Mari; Esseen, Per-Anders

2013-01-01

Question: What factors best characterize tree competitive environments in this structurally diverse old-growth forest, and do these factors vary spatially within and among stands? Location: Old-growth Picea abies forest of boreal Sweden. Methods: Using long-term, mapped permanent plot data augmented with dendrochronological analyses, we evaluated the effect of neighbourhood competition on focal tree growth by means of standard competition indices, each modified to include various metrics of trees size, neighbour mortality weighting (for neighbours that died during the inventory period), and within-neighbourhood tree clustering. Candidate models were evaluated using mixed-model linear regression analyses, with mean basal area increment as the response variable. We then analysed stand-level spatial patterns of competition indices and growth rates (via kriging) to determine if the relationship between these patterns could further elucidate factors influencing tree growth. Results: Inter-tree competition clearly affected growth rates, with crown volume being the size metric most strongly influencing the neighbourhood competitive environment. Including neighbour tree mortality weightings in models only slightly improved descriptions of competitive interactions. Although the within-neighbourhood clustering index did not improve model predictions, competition intensity was influenced by the underlying stand-level tree spatial arrangement: stand-level clustering locally intensified competition and reduced tree growth, whereas in the absence of such clustering, inter-tree competition played a lesser role in constraining tree growth. Conclusions: Our findings demonstrate that competition continues to influence forest processes and structures in an old-growth system that has not experienced major disturbances for at least two centuries. The finding that the underlying tree spatial pattern influenced the competitive environment suggests caution in interpreting traditional tree competition studies, in which tree spatial patterning is typically not taken into account. Our findings highlight the importance of forest structure – particularly the spatial arrangement of trees – in regulating inter-tree competition and growth in structurally diverse forests, and they provide insight into the causes and consequences of heterogeneity in this old-growth system.

Spatial clustering of childhood leukaemia with the integration of the Paediatric Environmental History.

PubMed

Cárceles-Álvarez, Alberto; Ortega-García, Juan A; López-Hernández, Fernando A; Orozco-Llamas, Mayra; Espinosa-López, Blanca; Tobarra-Sánchez, Esther; Alvarez, Lizbeth

2017-07-01

Leukaemia remains the most common type of paediatric cancer and its aetiology remains unknown, but considered to be multifactorial. It is suggested that the initiation in utero by relevant exposures and/or inherited genetic variants and, other promotional postnatal exposures are probably required to develop leukaemia. This study aimed to map the incidence and analyse possible clusters in the geographical distribution of childhood acute leukaemia during the critical periods and to evaluate the factors that may be involved in the aetiology by conducting community and individual risk assessments. We analysed all incident cases of acute childhood leukaemia (<15 years) diagnosed in a Spanish region during the period 1998-2013. At diagnosis, the addresses during pregnancy, early childhood and diagnosis were collected and codified to analyse the spatial distribution of acute leukaemia. Scan statistical test methodology was used for the identification of high-incidence spatial clusters. Once identified, individual and community risk assessments were conducted using the Paediatric Environmental History. A total of 158 cases of acute leukaemia were analysed. The crude rate for the period was 42.7 cases per million children. Among subtypes, acute lymphoblastic leukaemia had the highest incidence (31.9 per million children). A spatial cluster of acute lymphoblastic leukaemia was detected using the pregnancy address (p<0.05). The most common environmental risk factors related with the aetiology of acute lymphoblastic leukaemia, identified by the Paediatric Environmental History were: prenatal exposure to tobacco (75%) and alcohol (50%); residential and community exposure to pesticides (62.5%); prenatal or neonatal ionizing radiation (42.8%); and parental workplace exposure (37.5%) CONCLUSIONS: Our study suggests that environmental exposures in utero may be important in the development of childhood leukaemia. Due to the presence of high-incidence clusters using pregnancy address, it is necessary to introduce this address into the childhood cancer registers. The Paediatric Environmental History which includes pregnancy address and a careful and comprehensive evaluation of the environmental exposures will allow us to build the knowledge of the causes of childhood leukaemia. Copyright © 2017 Elsevier Inc. All rights reserved.

Identifying sighting clusters of endangered taxa with historical records.

PubMed

Duffy, Karl J

2011-04-01

The probability and time of extinction of taxa is often inferred from statistical analyses of historical records. Many of these analyses require the exclusion of multiple records within a unit of time (i.e., a month or a year). Nevertheless, spatially explicit, temporally aggregated data may be useful for identifying clusters of sightings (i.e., sighting clusters) in space and time. Identification of sighting clusters highlights changes in the historical recording of endangered taxa. I used two methods to identify sighting clusters in historical records: the Ederer-Myers-Mantel (EMM) test and the space-time permutation scan (STPS). I applied these methods to the spatially explicit sighting records of three species of orchids that are listed as endangered in the Republic of Ireland under the Wildlife Act (1976): Cephalanthera longifolia, Hammarbya paludosa, and Pseudorchis albida. Results with the EMM test were strongly affected by the choice of the time interval, and thus the number of temporal samples, used to examine the records. For example, sightings of P. albida clustered when the records were partitioned into 20-year temporal samples, but not when they were partitioned into 22-year temporal samples. Because the statistical power of EMM was low, it will not be useful when data are sparse. Nevertheless, the STPS identified regions that contained sighting clusters because it uses a flexible scanning window (defined by cylinders of varying size that move over the study area and evaluate the likelihood of clustering) to detect them, and it identified regions with high and regions with low rates of orchid sightings. The STPS analyses can be used to detect sighting clusters of endangered species that may be related to regions of extirpation and may assist in the categorization of threat status. ©2010 Society for Conservation Biology.

Assessing SaTScan ability to detect space-time clusters in wildfires

NASA Astrophysics Data System (ADS)

Costa, Ricardo; Pereira, Mário; Caramelo, Liliana; Vega Orozco, Carmen; Kanevski, Mikhail

2013-04-01

Besides classical cluster analysis techniques which are able to analyse spatial and temporal data, SaTScan software analyses space-time data using the spatial, temporal or space-time scan statistics. This software requires the spatial coordinates of the fire, but since in the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011) the location of each fire is the parish where the ignition occurs, the fire spatial coordinates were considered as coordinates of the centroid of the parishes. Moreover, in general, the northern region is characterized by a large number of small parishes while the southern comprises parish much larger. The objectives of this study are: (i) to test the ability of SaTScan to detect the correct space-time clusters, in what respects to spatial and temporal location and size; and, (ii) to evaluate the effect of the dimensions of the parishes and of aggregating all fires occurred in a parish in a single point. Results obtained with a synthetic database where clusters were artificially created with different densities, in different regions of the country and with different sizes and durations, allow to conclude: the ability of SaTScan to correctly identify the clusters (location, shape and spatial and temporal dimension); and objectively assess the influence of the size of the parishes and windows used in space-time detection. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South.

PubMed

Stopka, Thomas J; Brinkley-Rubinstein, Lauren; Johnson, Kendra; Chan, Philip A; Hutcheson, Marga; Crosby, Richard; Burke, Deirdre; Mena, Leandro; Nunn, Amy

2018-04-03

In recent years, more than half of new HIV infections in the United States occur among African Americans in the Southeastern United States. Spatial epidemiological analyses can inform public health responses in the Deep South by identifying HIV hotspots and community-level factors associated with clustering. The goal of this study was to identify and characterize HIV clusters in Mississippi through analysis of state-level HIV surveillance data. We used a combination of spatial epidemiology and statistical modeling to identify and characterize HIV hotspots in Mississippi census tracts (n=658) from 2008 to 2014. We conducted spatial analyses of all HIV infections, infections among men who have sex with men (MSM), and infections among African Americans. Multivariable logistic regression analyses identified community-level sociodemographic factors associated with HIV hotspots considering all cases. There were HIV hotspots for the entire population, MSM, and African American MSM identified in the Mississippi Delta region, Southern Mississippi, and in greater Jackson, including surrounding rural counties (P<.05). In multivariable models for all HIV cases, HIV hotspots were significantly more likely to include urban census tracts (adjusted odds ratio [AOR] 2.01, 95% CI 1.20-3.37) and census tracts that had a higher proportion of African Americans (AOR 3.85, 95% CI 2.23-6.65). The HIV hotspots were less likely to include census tracts with residents who had less than a high school education (AOR 0.95, 95% CI 0.92-0.98), census tracts with residents belonging to two or more racial/ethnic groups (AOR 0.46, 95% CI 0.30-0.70), and census tracts that had a higher percentage of the population living below the poverty level (AOR 0.51, 95% CI 0.28-0.92). We used spatial epidemiology and statistical modeling to identify and characterize HIV hotspots for the general population, MSM, and African Americans. HIV clusters concentrated in Jackson and the Mississippi Delta. African American race and urban location were positively associated with clusters, whereas having less than a high school education and having a higher percentage of the population living below the poverty level were negatively associated with clusters. Spatial epidemiological analyses can inform implementation science and public health response strategies, including improved HIV testing, targeted prevention and risk reduction education, and tailored preexposure prophylaxis to address HIV disparities in the South. ©Thomas J Stopka, Lauren Brinkley-Rubinstein, Kendra Johnson, Philip A Chan, Marga Hutcheson, Richard Crosby, Deirdre Burke, Leandro Mena, Amy Nunn. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.04.2018.

False Discovery Control in Large-Scale Spatial Multiple Testing

PubMed Central

Sun, Wenguang; Reich, Brian J.; Cai, T. Tony; Guindani, Michele; Schwartzman, Armin

2014-01-01

Summary This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US. PMID:25642138

X-ray and optical substructures of the DAFT/FADA survey clusters

NASA Astrophysics Data System (ADS)

Guennou, L.; Durret, F.; Adami, C.; Lima Neto, G. B.

2013-04-01

We have undertaken the DAFT/FADA survey with the double aim of setting constraints on dark energy based on weak lensing tomography and of obtaining homogeneous and high quality data for a sample of 91 massive clusters in the redshift range 0.4-0.9 for which there were HST archive data. We have analysed the XMM-Newton data available for 42 of these clusters to derive their X-ray temperatures and luminosities and search for substructures. Out of these, a spatial analysis was possible for 30 clusters, but only 23 had deep enough X-ray data for a really robust analysis. This study was coupled with a dynamical analysis for the 26 clusters having at least 30 spectroscopic galaxy redshifts in the cluster range. Altogether, the X-ray sample of 23 clusters and the optical sample of 26 clusters have 14 clusters in common. We present preliminary results on the coupled X-ray and dynamical analyses of these 14 clusters.

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

PubMed Central

Yin, Wenwu; Yu, Hongjie; Si, Yali; Li, Jianhui; Zhou, Yuanchun; Zhou, Xiaoyan; Magalhães, Ricardo J. Soares.

2013-01-01

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program. PMID:23991098

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Deriving spatial patterns from a novel database of volcanic rock geochemistry in the Virunga Volcanic Province, East African Rift

NASA Astrophysics Data System (ADS)

Poppe, Sam; Barette, Florian; Smets, Benoît; Benbakkar, Mhammed; Kervyn, Matthieu

2016-04-01

The Virunga Volcanic Province (VVP) is situated within the western branch of the East-African Rift. The geochemistry and petrology of its' volcanic products has been studied extensively in a fragmented manner. They represent a unique collection of silica-undersaturated, ultra-alkaline and ultra-potassic compositions, displaying marked geochemical variations over the area occupied by the VVP. We present a novel spatially-explicit database of existing whole-rock geochemical analyses of the VVP volcanics, compiled from international publications, (post-)colonial scientific reports and PhD theses. In the database, a total of 703 geochemical analyses of whole-rock samples collected from the 1950s until recently have been characterised with a geographical location, eruption source location, analytical results and uncertainty estimates for each of these categories. Comparative box plots and Kruskal-Wallis H tests on subsets of analyses with contrasting ages or analytical methods suggest that the overall database accuracy is consistent. We demonstrate how statistical techniques such as Principal Component Analysis (PCA) and subsequent cluster analysis allow the identification of clusters of samples with similar major-element compositions. The spatial patterns represented by the contrasting clusters show that both the historically active volcanoes represent compositional clusters which can be identified based on their contrasted silica and alkali contents. Furthermore, two sample clusters are interpreted to represent the most primitive, deep magma source within the VVP, different from the shallow magma reservoirs that feed the eight dominant large volcanoes. The samples from these two clusters systematically originate from locations which 1. are distal compared to the eight large volcanoes and 2. mostly coincide with the surface expressions of rift faults or NE-SW-oriented inherited Precambrian structures which were reactivated during rifting. The lava from the Mugogo eruption of 1957 belongs to these primitive clusters and is the only known to have erupted outside the current rift valley in historical times. We thus infer there is a distributed hazard of vent opening susceptibility additional to the susceptibility associated with the main Virunga edifices. This study suggests that the statistical analysis of such geochemical database may help to understand complex volcanic plumbing systems and the spatial distribution of volcanic hazards in active and poorly known volcanic areas such as the Virunga Volcanic Province.

Including foreshocks and aftershocks in time-independent probabilistic seismic hazard analyses

USGS Publications Warehouse

Boyd, Oliver S.

2012-01-01

Time‐independent probabilistic seismic‐hazard analysis treats each source as being temporally and spatially independent; hence foreshocks and aftershocks, which are both spatially and temporally dependent on the mainshock, are removed from earthquake catalogs. Yet, intuitively, these earthquakes should be considered part of the seismic hazard, capable of producing damaging ground motions. In this study, I consider the mainshock and its dependents as a time‐independent cluster, each cluster being temporally and spatially independent from any other. The cluster has a recurrence time of the mainshock; and, by considering the earthquakes in the cluster as a union of events, dependent events have an opportunity to contribute to seismic ground motions and hazard. Based on the methods of the U.S. Geological Survey for a high‐hazard site, the inclusion of dependent events causes ground motions that are exceeded at probability levels of engineering interest to increase by about 10% but could be as high as 20% if variations in aftershock productivity can be accounted for reliably.

Regional variation in the severity of pesticide exposure outcomes: applications of geographic information systems and spatial scan statistics.

PubMed

Sudakin, Daniel L; Power, Laura E

2009-03-01

Geographic information systems and spatial scan statistics have been utilized to assess regional clustering of symptomatic pesticide exposures reported to a state Poison Control Center (PCC) during a single year. In the present study, we analyzed five subsequent years of PCC data to test whether there are significant geographic differences in pesticide exposure incidents resulting in serious (moderate, major, and fatal) medical outcomes. A PCC provided the data on unintentional pesticide exposures for the time period 2001-2005. The geographic location of the caller, the location where the exposure occurred, the exposure route, and the medical outcome were abstracted. There were 273 incidents resulting in moderate effects (n = 261), major effects (n = 10), or fatalities (n = 2). Spatial scan statistics identified a geographic area consisting of two adjacent counties (one urban, one rural), where statistically significant clustering of serious outcomes was observed. The relative risk of moderate, major, and fatal outcomes was 2.0 in this spatial cluster (p = 0.0005). PCC data, geographic information systems, and spatial scan statistics can identify clustering of serious outcomes from human exposure to pesticides. These analyses may be useful for public health officials to target preventive interventions. Further investigation is warranted to understand better the potential explanations for geographical clustering, and to assess whether preventive interventions have an impact on reducing pesticide exposure incidents resulting in serious medical outcomes.

Space-time cluster analysis of sea lice infestation (Caligus clemensi and Lepeophtheirus salmonis) on wild juvenile Pacific salmon in the Broughton Archipelago of Canada.

PubMed

Patanasatienkul, Thitiwan; Sanchez, Javier; Rees, Erin E; Pfeiffer, Dirk; Revie, Crawford W

2015-06-15

Sea lice infestation levels on wild chum and pink salmon in the Broughton Archipelago region are known to vary spatially and temporally; however, the locations of areas associated with a high infestation level had not been investigated yet. In the present study, the multivariate spatial scan statistic based on a Poisson model was used to assess spatial clustering of elevated sea lice (Caligus clemensi and Lepeophtheirus salmonis) infestation levels on wild chum and pink salmon sampled between March and July of 2004 to 2012 in the Broughton Archipelago and Knight Inlet regions of British Columbia, Canada. Three covariates, seine type (beach and purse seining), fish size, and year effect, were used to provide adjustment within the analyses. The analyses were carried out across the five months/datasets and between two fish species to assess the consistency of the identified clusters. Sea lice stages were explored separately for the early life stages (non-motile) and the late life stages of sea lice (motile). Spatial patterns in fish migration were also explored using monthly plots showing the average number of each fish species captured per sampling site. The results revealed three clusters for non-motile C. clemensi, two clusters for non-motile L. salmonis, and one cluster for the motile stage in each of the sea lice species. In general, the location and timing of clusters detected for both fish species were similar. Early in the season, the clusters of elevated sea lice infestation levels on wild fish are detected in areas closer to the rivers, with decreasing relative risks as the season progresses. Clusters were detected further from the estuaries later in the season, accompanied by increasing relative risks. In addition, the plots for fish migration exhibit similar patterns for both fish species in that, as expected, the juveniles move from the rivers toward the open ocean as the season progresses The identification of space-time clustering of infestation on wild fish from this study can help in targeting investigations of factors associated with these infestations and thereby support the development of more effective sea lice control measures. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Data Analysis and Visualization; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii)more » evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.« less

Spatial Analysis of China Province-level Perinatal Mortality

PubMed Central

XIANG, Kun; SONG, Deyong

2016-01-01

Background: Using spatial analysis tools to determine the spatial patterns of China province-level perinatal mortality and using spatial econometric model to examine the impacts of health care resources and different socio-economic factors on perinatal mortality. Methods: The Global Moran’s I index is used to examine whether the spatial autocorrelation exists in selected regions and Moran’s I scatter plot to examine the spatial clustering among regions. Spatial econometric models are used to investigate the spatial relationships between perinatal mortality and contributing factors. Results: The overall Moran’s I index indicates that perinatal mortality displays positive spatial autocorrelation. Moran’s I scatter plot analysis implies that there is a significant clustering of mortality in both high-rate regions and low-rate regions. The spatial econometric models analyses confirm the existence of a direct link between perinatal mortality and health care resources, socio-economic factors. Conclusions: Since a positive spatial autocorrelation has been detected in China province-level perinatal mortality, the upgrading of regional economic development and medical service level will affect the mortality not only in region itself but also its adjacent regions. PMID:27398334

Galaxy clusters in the cosmic web

NASA Astrophysics Data System (ADS)

Acebrón, A.; Durret, F.; Martinet, N.; Adami, C.; Guennou, L.

2014-12-01

Simulations of large scale structure formation in the universe predict that matter is essentially distributed along filaments at the intersection of which lie galaxy clusters. We have analysed 9 clusters in the redshift range 0.4

Spatial Analysis of Great Lakes Regional Icing Cloud Liquid Water Content

NASA Technical Reports Server (NTRS)

Ryerson, Charles C.; Koenig, George G.; Melloh, Rae A.; Meese, Debra A.; Reehorst, Andrew L.; Miller, Dean R.

2003-01-01

Abstract Clustering of cloud microphysical conditions, such as liquid water content (LWC) and drop size, can affect the rate and shape of ice accretion and the airworthiness of aircraft. Clustering may also degrade the accuracy of cloud LWC measurements from radars and microwave radiometers being developed by the government for remotely mapping icing conditions ahead of aircraft in flight. This paper evaluates spatial clustering of LWC in icing clouds using measurements collected during NASA research flights in the Great Lakes region. We used graphical and analytical approaches to describe clustering. The analytical approach involves determining the average size of clusters and computing a clustering intensity parameter. We analyzed flight data composed of 1-s-frequency LWC measurements for 12 periods ranging from 17.4 minutes (73 km) to 45.3 minutes (190 km) in duration. Graphically some flight segments showed evidence of consistency with regard to clustering patterns. Cluster intensity varied from 0.06, indicating little clustering, to a high of 2.42. Cluster lengths ranged from 0.1 minutes (0.6 km) to 4.1 minutes (17.3 km). Additional analyses will allow us to determine if clustering climatologies can be developed to characterize cluster conditions by region, time period, or weather condition. Introduction

Employing a portable X-Ray fluorescence (P-XRF) analyser and GIS to identify and map heavy metal pollution in soils of a traditional bonfire site

NASA Astrophysics Data System (ADS)

Dao, Ligang; Zhang, Chaosheng; Morrison, Liam

2010-05-01

Soils in the vicinity of bonfires are recipients of metal contaminants from burning of metal-containing materials. In order to better understand the impacts of bonfires on soils, a total of 218 surface soil samples were collected from a traditional bonfire site in Galway City, Ireland. Concentrations of Cu, Pb and Zn were determined using a portable X-ray Fluorescence (P-XRF) analyser. Strong variations were observed for these metals, and several samples contained elevated Zn concentrations which exceeded the intervention threshold of the Dutch criteria (720 mg kg-1). Spatial clusters and spatial outliers were detected using the local Moran's I index and were mapped using GIS. Two clear high value spatial clusters could be observed on the upper left side and centre part of the study area for Cu, Pb and Zn. Results of variogram analyses showed high nugget-sill-ratios for Cu, Pb and Zn, indicating strong spatial variation over short distances which could be resulted from anthropogenic activities. The spatial interpolation method of ordinary kriging was applied to produce the spatial interpolation maps for Cu, Pb and Zn, and the areas with elevated concentrations were in line with historical locations of the bonfires. The hazard maps showed small parts of the study area with Zn concentrations exceeding the Dutch intervention values. In order to prevent further contamination from bonfires, it is advised that tyres and other metal-containing wastes should not be burnt. The results in this study provide useful information for management of bonfires.

Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010.

PubMed

Zulu, Leo C; Kalipeni, Ezekiel; Johannes, Eliza

2014-05-23

Although local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi's estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level. Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering. Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across 'sub-epidemics' while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV "hotspots" clustered among eleven southern districts/cities while a "coldspot" captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts. Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.

Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010

PubMed Central

2014-01-01

Background Although local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level. Methods Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering. Results Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts. Conclusions Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale. PMID:24886573

Temporal and spatial analysis of psittacosis in association with poultry farming in the Netherlands, 2000-2015.

PubMed

Hogerwerf, Lenny; Holstege, Manon M C; Benincà, Elisa; Dijkstra, Frederika; van der Hoek, Wim

2017-07-26

Human psittacosis is a highly under diagnosed zoonotic disease, commonly linked to psittacine birds. Psittacosis in birds, also known as avian chlamydiosis, is endemic in poultry, but the risk for people living close to poultry farms is unknown. Therefore, our study aimed to explore the temporal and spatial patterns of human psittacosis infections and identify possible associations with poultry farming in the Netherlands. We analysed data on 700 human cases of psittacosis notified between 01-01-2000 and 01-09-2015. First, we studied the temporal behaviour of psittacosis notifications by applying wavelet analysis. Then, to identify possible spatial patterns, we applied spatial cluster analysis. Finally, we investigated the possible spatial association between psittacosis notifications and data on the Dutch poultry sector at municipality level using a multivariable model. We found a large spatial cluster that covered a highly poultry-dense area but additional clusters were found in areas that had a low poultry density. There were marked geographical differences in the awareness of psittacosis and the amount and the type of laboratory diagnostics used for psittacosis, making it difficult to draw conclusions about the correlation between the large cluster and poultry density. The multivariable model showed that the presence of chicken processing plants and slaughter duck farms in a municipality was associated with a higher rate of human psittacosis notifications. The significance of the associations was influenced by the inclusion or exclusion of farm density in the model. Our temporal and spatial analyses showed weak associations between poultry-related variables and psittacosis notifications. Because of the low number of psittacosis notifications available for analysis, the power of our analysis was relative low. Because of the exploratory nature of this research, the associations found cannot be interpreted as evidence for airborne transmission of psittacosis from poultry to the general population. Further research is needed to determine the prevalence of C. psittaci in Dutch poultry. Also, efforts to promote PCR-based testing for C. psittaci and genotyping for source tracing are important to reduce the diagnostic deficit, and to provide better estimates of the human psittacosis burden, and the possible role of poultry.

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

Receptor signaling clusters in the immune synapse(in eng)

DOE PAGES

Dustin, Michael L.; Groves, Jay T.

2012-02-23

Signaling processes between various immune cells involve large-scale spatial reorganization of receptors and signaling molecules within the cell-cell junction. These structures, now collectively referred to as immune synapses, interleave physical and mechanical processes with the cascades of chemical reactions that constitute signal transduction systems. Molecular level clustering, spatial exclusion, and long-range directed transport are all emerging as key regulatory mechanisms. The study of these processes is drawing researchers from physical sciences to join the effort and represents a rapidly growing branch of biophysical chemistry. Furthermore, recent advances in physical and quantitative analyses of signaling within the immune synapses are reviewedmore » here.« less

Receptor signaling clusters in the immune synapse (in eng)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dustin, Michael L.; Groves, Jay T.

2012-02-23

Signaling processes between various immune cells involve large-scale spatial reorganization of receptors and signaling molecules within the cell-cell junction. These structures, now collectively referred to as immune synapses, interleave physical and mechanical processes with the cascades of chemical reactions that constitute signal transduction systems. Molecular level clustering, spatial exclusion, and long-range directed transport are all emerging as key regulatory mechanisms. The study of these processes is drawing researchers from physical sciences to join the effort and represents a rapidly growing branch of biophysical chemistry. Furthermore, recent advances in physical and quantitative analyses of signaling within the immune synapses are reviewedmore » here.« less

Using Cluster Analysis to Compartmentalize a Large Managed Wetland Based on Physical, Biological, and Climatic Geospatial Attributes.

PubMed

Hahus, Ian; Migliaccio, Kati; Douglas-Mankin, Kyle; Klarenberg, Geraldine; Muñoz-Carpena, Rafael

2018-04-27

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

Big Data Geo-Analytical Tool Development for Spatial Analysis Uncertainty Visualization and Quantification Needs

NASA Astrophysics Data System (ADS)

Rose, K.; Bauer, J. R.; Baker, D. V.

2015-12-01

As big data computing capabilities are increasingly paired with spatial analytical tools and approaches, there is a need to ensure uncertainty associated with the datasets used in these analyses is adequately incorporated and portrayed in results. Often the products of spatial analyses, big data and otherwise, are developed using discontinuous, sparse, and often point-driven data to represent continuous phenomena. Results from these analyses are generally presented without clear explanations of the uncertainty associated with the interpolated values. The Variable Grid Method (VGM) offers users with a flexible approach designed for application to a variety of analyses where users there is a need to study, evaluate, and analyze spatial trends and patterns while maintaining connection to and communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analysis through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom 'Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations. The presentation includes examples of the approach being applied to a range of subsurface, geospatial studies (e.g. induced seismicity risk).

Diversity in the stellar velocity dispersion profiles of a large sample of brightest cluster galaxies z ≤ 0.3

NASA Astrophysics Data System (ADS)

Loubser, S. I.; Hoekstra, H.; Babul, A.; O'Sullivan, E.

2018-06-01

We analyse spatially resolved deep optical spectroscopy of brightestcluster galaxies (BCGs) located in 32 massive clusters with redshifts of 0.05 ≤ z ≤ 0.30 to investigate their velocity dispersion profiles. We compare these measurements to those of other massive early-type galaxies, as well as central group galaxies, where relevant. This unique, large sample extends to the most extreme of massive galaxies, spanning MK between -25.7 and -27.8 mag, and host cluster halo mass M500 up to 1.7 × 1015 M⊙. To compare the kinematic properties between brightest group and cluster members, we analyse similar spatially resolved long-slit spectroscopy for 23 nearby brightest group galaxies (BGGs) from the Complete Local-Volume Groups Sample. We find a surprisingly large variety in velocity dispersion slopes for BCGs, with a significantly larger fraction of positive slopes, unique compared to other (non-central) early-type galaxies as well as the majority of the brightest members of the groups. We find that the velocity dispersion slopes of the BCGs and BGGs correlate with the luminosity of the galaxies, and we quantify this correlation. It is not clear whether the full diversity in velocity dispersion slopes that we see is reproduced in simulations.

Characterizing the spatial distribution of brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), populations in peach orchards

PubMed Central

Hahn, Noel G.

2017-01-01

Geospatial analyses were used to investigate the spatial distribution of populations of Halyomorpha halys, an important invasive agricultural pest in mid-Atlantic peach orchards. This spatial analysis will improve efficiency by allowing growers and farm managers to predict insect arrangement and target management strategies. Data on the presence of H. halys were collected from five peach orchards at four farms in New Jersey from 2012–2014 located in different land-use contexts. A point pattern analysis, using Ripley’s K function, was used to describe clustering of H. halys. In addition, the clustering of damage indicative of H. halys feeding was described. With low populations early in the growing season, H. halys did not exhibit signs of clustering in the orchards at most distances. At sites with low populations throughout the season, clustering was not apparent. However, later in the season, high infestation levels led to more evident clustering of H. halys. Damage, although present throughout the entire orchard, was found at low levels. When looking at trees with greater than 10% fruit damage, damage was shown to cluster in orchards. The Moran’s I statistic showed that spatial autocorrelation of H. halys was present within the orchards on the August sample dates, in relation to both populations density and levels of damage. Kriging the abundance of H. halys and the severity of damage to peaches revealed that the estimations of these are generally found in the same region of the orchards. This information on the clustering of H. halys populations will be useful to help predict presence of insects for use in management or scouting programs. PMID:28362797

SpatialEpiApp: A Shiny web application for the analysis of spatial and spatio-temporal disease data.

PubMed

Moraga, Paula

2017-11-01

During last years, public health surveillance has been facilitated by the existence of several packages implementing statistical methods for the analysis of spatial and spatio-temporal disease data. However, these methods are still inaccesible for many researchers lacking the adequate programming skills to effectively use the required software. In this paper we present SpatialEpiApp, a Shiny web application that integrate two of the most common approaches in health surveillance: disease mapping and detection of clusters. SpatialEpiApp is easy to use and does not require any programming knowledge. Given information about the cases, population and optionally covariates for each of the areas and dates of study, the application allows to fit Bayesian models to obtain disease risk estimates and their uncertainty by using R-INLA, and to detect disease clusters by using SaTScan. The application allows user interaction and the creation of interactive data visualizations and reports showing the analyses performed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing Genetic Structure in Common but Ecologically Distinct Carnivores: The Stone Marten and Red Fox.

PubMed

Basto, Mafalda P; Santos-Reis, Margarida; Simões, Luciana; Grilo, Clara; Cardoso, Luís; Cortes, Helder; Bruford, Michael W; Fernandes, Carlos

2016-01-01

The identification of populations and spatial genetic patterns is important for ecological and conservation research, and spatially explicit individual-based methods have been recognised as powerful tools in this context. Mammalian carnivores are intrinsically vulnerable to habitat fragmentation but not much is known about the genetic consequences of fragmentation in common species. Stone martens (Martes foina) and red foxes (Vulpes vulpes) share a widespread Palearctic distribution and are considered habitat generalists, but in the Iberian Peninsula stone martens tend to occur in higher quality habitats. We compared their genetic structure in Portugal to see if they are consistent with their differences in ecological plasticity, and also to illustrate an approach to explicitly delineate the spatial boundaries of consistently identified genetic units. We analysed microsatellite data using spatial Bayesian clustering methods (implemented in the software BAPS, GENELAND and TESS), a progressive partitioning approach and a multivariate technique (Spatial Principal Components Analysis-sPCA). Three consensus Bayesian clusters were identified for the stone marten. No consensus was achieved for the red fox, but one cluster was the most probable clustering solution. Progressive partitioning and sPCA suggested additional clusters in the stone marten but they were not consistent among methods and were geographically incoherent. The contrasting results between the two species are consistent with the literature reporting stricter ecological requirements of the stone marten in the Iberian Peninsula. The observed genetic structure in the stone marten may have been influenced by landscape features, particularly rivers, and fragmentation. We suggest that an approach based on a consensus clustering solution of multiple different algorithms may provide an objective and effective means to delineate potential boundaries of inferred subpopulations. sPCA and progressive partitioning offer further verification of possible population structure and may be useful for revealing cryptic spatial genetic patterns worth further investigation.

Assessing Genetic Structure in Common but Ecologically Distinct Carnivores: The Stone Marten and Red Fox

PubMed Central

Basto, Mafalda P.; Santos-Reis, Margarida; Simões, Luciana; Grilo, Clara; Cardoso, Luís; Cortes, Helder; Bruford, Michael W.; Fernandes, Carlos

2016-01-01

The identification of populations and spatial genetic patterns is important for ecological and conservation research, and spatially explicit individual-based methods have been recognised as powerful tools in this context. Mammalian carnivores are intrinsically vulnerable to habitat fragmentation but not much is known about the genetic consequences of fragmentation in common species. Stone martens (Martes foina) and red foxes (Vulpes vulpes) share a widespread Palearctic distribution and are considered habitat generalists, but in the Iberian Peninsula stone martens tend to occur in higher quality habitats. We compared their genetic structure in Portugal to see if they are consistent with their differences in ecological plasticity, and also to illustrate an approach to explicitly delineate the spatial boundaries of consistently identified genetic units. We analysed microsatellite data using spatial Bayesian clustering methods (implemented in the software BAPS, GENELAND and TESS), a progressive partitioning approach and a multivariate technique (Spatial Principal Components Analysis-sPCA). Three consensus Bayesian clusters were identified for the stone marten. No consensus was achieved for the red fox, but one cluster was the most probable clustering solution. Progressive partitioning and sPCA suggested additional clusters in the stone marten but they were not consistent among methods and were geographically incoherent. The contrasting results between the two species are consistent with the literature reporting stricter ecological requirements of the stone marten in the Iberian Peninsula. The observed genetic structure in the stone marten may have been influenced by landscape features, particularly rivers, and fragmentation. We suggest that an approach based on a consensus clustering solution of multiple different algorithms may provide an objective and effective means to delineate potential boundaries of inferred subpopulations. sPCA and progressive partitioning offer further verification of possible population structure and may be useful for revealing cryptic spatial genetic patterns worth further investigation. PMID:26727497

Swarm Intelligence for Urban Dynamics Modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.

2009-04-16

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Swarm Intelligence for Urban Dynamics Modelling

NASA Astrophysics Data System (ADS)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.

2009-04-01

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Shifting Patterns of Aedes aegypti Fine Scale Spatial Clustering in Iquitos, Peru

PubMed Central

LaCon, Genevieve; Morrison, Amy C.; Astete, Helvio; Stoddard, Steven T.; Paz-Soldan, Valerie A.; Elder, John P.; Halsey, Eric S.; Scott, Thomas W.; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M.

2014-01-01

Background Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1) quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2) determine overlap between clusters, (3) quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4) quantify the extent of clustering at the household and neighborhood levels. Methodologies/Principal Findings Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [p<0.05] high mosquito abundance) were calculated for each of the 9 entomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters) and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study. Conclusions/Significance Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than targeting Ae. aegypti hotspots. PMID:25102062

Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru.

PubMed

LaCon, Genevieve; Morrison, Amy C; Astete, Helvio; Stoddard, Steven T; Paz-Soldan, Valerie A; Elder, John P; Halsey, Eric S; Scott, Thomas W; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M

2014-08-01

Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1) quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2) determine overlap between clusters, (3) quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4) quantify the extent of clustering at the household and neighborhood levels. Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [p<0.05] high mosquito abundance) were calculated for each of the 9 entomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters) and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study. Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than targeting Ae. aegypti hotspots.

Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China.

PubMed

Xia, Congcong; Bergquist, Robert; Lynn, Henry; Hu, Fei; Lin, Dandan; Hao, Yuwan; Li, Shizhu; Hu, Yi; Zhang, Zhijie

2017-03-08

The Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important to analyse the clustering pattern of schistosomiasis and detect the hotspots of transmission risk. Based on annual surveillance data, at the village level in this region from 2009 to 2014, spatial and temporal cluster analyses were conducted to assess the pattern of schistosomiasis infection risk among humans through purely spatial (Local Moran's I, Kulldorff and Flexible scan statistic) and space-time scan statistics (Kulldorff). A dramatic decline was found in the infection rate during the study period, which was shown to be maintained at a low level. The number of spatial clusters declined over time and were concentrated in counties around Poyang Lake, including Yugan, Yongxiu, Nanchang, Xingzi, Xinjian, De'an as well as Pengze, situated along the Yangtze River and the most serious area found in this study. Space-time analysis revealed that the clustering time frame appeared between 2009 and 2011 and the most likely cluster with the widest range was particularly concentrated in Pengze County. This study detected areas at high risk for schistosomiasis both in space and time at the village level from 2009 to 2014 in Poyang Lake Region. The high-risk areas are now more concentrated and mainly distributed at the river inflows Poyang Lake and along Yangtze River in Pengze County. It was assumed that the water projects including reservoirs and a recently breached dyke in this area were partly to blame. This study points out that attempts to reduce the negative effects of water projects in China should focus on the Poyang Lake Region.

Cluster detection methods applied to the Upper Cape Cod cancer data.

PubMed

Ozonoff, Al; Webster, Thomas; Vieira, Veronica; Weinberg, Janice; Ozonoff, David; Aschengrau, Ann

2005-09-15

A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Spatial memory in foraging games.

PubMed

Kerster, Bryan E; Rhodes, Theo; Kello, Christopher T

2016-03-01

Foraging and foraging-like processes are found in spatial navigation, memory, visual search, and many other search functions in human cognition and behavior. Foraging is commonly theorized using either random or correlated movements based on Lévy walks, or a series of decisions to remain or leave proximal areas known as "patches". Neither class of model makes use of spatial memory, but search performance may be enhanced when information about searched and unsearched locations is encoded. A video game was developed to test the role of human spatial memory in a canonical foraging task. Analyses of search trajectories from over 2000 human players yielded evidence that foraging movements were inherently clustered, and that clustering was facilitated by spatial memory cues and influenced by memory for spatial locations of targets found. A simple foraging model is presented in which spatial memory is used to integrate aspects of Lévy-based and patch-based foraging theories to perform a kind of area-restricted search, and thereby enhance performance as search unfolds. Using only two free parameters, the model accounts for a variety of findings that individually support competing theories, but together they argue for the integration of spatial memory into theories of foraging. Copyright © 2015 Elsevier B.V. All rights reserved.

Control of clustered action potential firing in a mathematical model of entorhinal cortex stellate cells.

PubMed

Tait, Luke; Wedgwood, Kyle; Tsaneva-Atanasova, Krasimira; Brown, Jon T; Goodfellow, Marc

2018-07-14

The entorhinal cortex is a crucial component of our memory and spatial navigation systems and is one of the first areas to be affected in dementias featuring tau pathology, such as Alzheimer's disease and frontotemporal dementia. Electrophysiological recordings from principle cells of medial entorhinal cortex (layer II stellate cells, mEC-SCs) demonstrate a number of key identifying properties including subthreshold oscillations in the theta (4-12 Hz) range and clustered action potential firing. These single cell properties are correlated with network activity such as grid firing and coupling between theta and gamma rhythms, suggesting they are important for spatial memory. As such, experimental models of dementia have revealed disruption of organised dorsoventral gradients in clustered action potential firing. To better understand the mechanisms underpinning these different dynamics, we study a conductance based model of mEC-SCs. We demonstrate that the model, driven by extrinsic noise, can capture quantitative differences in clustered action potential firing patterns recorded from experimental models of tau pathology and healthy animals. The differential equation formulation of our model allows us to perform numerical bifurcation analyses in order to uncover the dynamic mechanisms underlying these patterns. We show that clustered dynamics can be understood as subcritical Hopf/homoclinic bursting in a fast-slow system where the slow sub-system is governed by activation of the persistent sodium current and inactivation of the slow A-type potassium current. In the full system, we demonstrate that clustered firing arises via flip bifurcations as conductance parameters are varied. Our model analyses confirm the experimentally suggested hypothesis that the breakdown of clustered dynamics in disease occurs via increases in AHP conductance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial location influences vocal interactions in bullfrog choruses

PubMed Central

Bates, Mary E.; Cropp, Brett F.; Gonchar, Marina; Knowles, Jeffrey; Simmons, James A.; Simmons, Andrea Megela

2010-01-01

A multiple sensor array was employed to identify the spatial locations of all vocalizing male bullfrogs (Rana catesbeiana) in five natural choruses. Patterns of vocal activity collected with this array were compared with computer simulations of chorus activity. Bullfrogs were not randomly spaced within choruses, but tended to cluster into closely spaced groups of two to five vocalizing males. There were nonrandom, differing patterns of vocal interactions within clusters of closely spaced males and between different clusters. Bullfrogs located within the same cluster tended to overlap or alternate call notes with two or more other males in that cluster. These near-simultaneous calling bouts produced advertisement calls with more pronounced amplitude modulation than occurred in nonoverlapping notes or calls. Bullfrogs located in different clusters more often alternated entire calls or overlapped only small segments of their calls. They also tended to respond sequentially to calls of their farther neighbors compared to their nearer neighbors. Results of computational analyses showed that the observed patterns of vocal interactions were significantly different than expected based on random activity. The use of a multiple sensor array provides a richer view of the dynamics of choruses than available based on single microphone techniques. PMID:20370047

Tempo-spatial analysis of Fennoscandian intraplate seismicity

NASA Astrophysics Data System (ADS)

Roberts, Roland; Lund, Björn

2017-04-01

Coupled spatial-temporal patterns of the occurrence of earthquakes in Fennoscandia are analysed using non-parametric methods. The occurrence of larger events is unambiguously and very strongly temporally clustered, with major implications for the assessment of seismic hazard in areas such as Fennoscandia. In addition, there is a clear pattern of geographical migration of activity. Data from the Swedish National Seismic Network and a collated international catalogue are analysed. Results show consistent patterns on different spatial and temporal scales. We are currently investigating these patterns in order to assess the statistical significance of the tempo-spatial patterns, and to what extent these may be consistent with stress transfer mechanism such as coulomb stress and pore fluid migration. Indications are that some further mechanism is necessary in order to explain the data, perhaps related to post-glacial uplift, which is up to 1cm/year.

From broadscale patterns to fine-scale processes: habitat structure influences genetic differentiation in the pitcher plant midge across multiple spatial scales.

PubMed

Rasic, Gordana; Keyghobadi, Nusha

2012-01-01

The spatial scale at which samples are collected and analysed influences the inferences that can be drawn from landscape genetic studies. We examined genetic structure and its landscape correlates in the pitcher plant midge, Metriocnemus knabi, an inhabitant of the purple pitcher plant, Sarracenia purpurea, across several spatial scales that are naturally delimited by the midge's habitat (leaf, plant, cluster of plants, bog and system of bogs). We analysed 11 microsatellite loci in 710 M. knabi larvae from two systems of bogs in Algonquin Provincial Park (Canada) and tested the hypotheses that variables related to habitat structure are associated with genetic differentiation in this midge. Up to 54% of variation in individual-based genetic distances at several scales was explained by broadscale landscape variables of bog size, pitcher plant density within bogs and connectivity of pitcher plant clusters. Our results indicate that oviposition behaviour of females at fine scales, as inferred from the spatial locations of full-sib larvae, and spatially limited gene flow at broad scales represent the important processes underlying observed genetic patterns in M. knabi. Broadscale landscape features (bog size and plant density) appear to influence oviposition behaviour of midges, which in turn influences the patterns of genetic differentiation observed at both fine and broad scales. Thus, we inferred linkages among genetic patterns, landscape patterns and ecological processes across spatial scales in M. knabi. Our results reinforce the value of exploring such links simultaneously across multiple spatial scales and landscapes when investigating genetic diversity within a species. © 2011 Blackwell Publishing Ltd.

Clustering of Multivariate Geostatistical Data

NASA Astrophysics Data System (ADS)

Fouedjio, Francky

2017-04-01

Multivariate data indexed by geographical coordinates have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations belonging to the same cluster have a certain degree of homogeneity while data locations in the different clusters have to be as different as possible. However, groups of data locations created through classical clustering techniques turn out to show poor spatial contiguity, a feature obviously inconvenient for many geoscience applications. In this work, we develop a clustering method that overcomes this problem by accounting the spatial dependence structure of data; thus reinforcing the spatial contiguity of resulting cluster. The capability of the proposed clustering method to provide spatially contiguous and meaningful clusters of data locations is assessed using both synthetic and real datasets. Keywords: clustering, geostatistics, spatial contiguity, spatial dependence.

Spatiotemporal multistage consensus clustering in molecular dynamics studies of large proteins.

PubMed

Kenn, Michael; Ribarics, Reiner; Ilieva, Nevena; Cibena, Michael; Karch, Rudolf; Schreiner, Wolfgang

2016-04-26

The aim of this work is to find semi-rigid domains within large proteins as reference structures for fitting molecular dynamics trajectories. We propose an algorithm, multistage consensus clustering, MCC, based on minimum variation of distances between pairs of Cα-atoms as target function. The whole dataset (trajectory) is split into sub-segments. For a given sub-segment, spatial clustering is repeatedly started from different random seeds, and we adopt the specific spatial clustering with minimum target function: the process described so far is stage 1 of MCC. Then, in stage 2, the results of spatial clustering are consolidated, to arrive at domains stable over the whole dataset. We found that MCC is robust regarding the choice of parameters and yields relevant information on functional domains of the major histocompatibility complex (MHC) studied in this paper: the α-helices and β-floor of the protein (MHC) proved to be most flexible and did not contribute to clusters of significant size. Three alleles of the MHC, each in complex with ABCD3 peptide and LC13 T-cell receptor (TCR), yielded different patterns of motion. Those alleles causing immunological allo-reactions showed distinct correlations of motion between parts of the peptide, the binding cleft and the complementary determining regions (CDR)-loops of the TCR. Multistage consensus clustering reflected functional differences between MHC alleles and yields a methodological basis to increase sensitivity of functional analyses of bio-molecules. Due to the generality of approach, MCC is prone to lend itself as a potent tool also for the analysis of other kinds of big data.

Maternal-child overweight/obesity and undernutrition in Kenya: a geographic analysis.

PubMed

Pawloski, Lisa R; Curtin, Kevin M; Gewa, Constance; Attaway, David

2012-11-01

The purpose of the study was to examine geographic relationships of nutritional status (BMI), including underweight, overweight and obesity, among Kenyan mothers and children. Spatial relationships were examined concerning BMI of the mothers and BMI-for-age percentiles of their children. These included spatial statistical measures of the clustering of segments of the population, in addition to inspection of co-location of significant clusters. Rural and urban areas of Kenya, including the cities of Nairobi and Mombasa, and the Kisumu region. Mother-child pairs from Demographic and Health Survey data including 1541 observations in 2003 and 1592 observations in 2009. These mother-child pairs were organized into 399 locational clusters. There is extremely strong evidence that high BMI values exhibit strong spatial clustering. There were co-locations of overweight mothers and overweight children only in the Nairobi region, while both underweight mothers and children tended to cluster in rural areas. In Mombasa clusters of overweight mothers were associated with normal-weight children, while in the Kisumu region clusters of overweight children were associated with normal-weight mothers. These findings show there is geographic variability as well as some defined patterns concerning the distribution of malnutrition among mothers and children in Kenya, and suggest the need for further geographic analyses concerning the potential factors which influence nutritional status in this population. In addition, the methods used in this research may be easily applied to other Demographic and Health Survey data in order to begin to understand the geographic determinants of health in low-income countries.

Detecting Genomic Clustering of Risk Variants from Sequence Data: Cases vs. Controls

PubMed Central

Schaid, Daniel J.; Sinnwell, Jason P.; McDonnell, Shannon K.; Thibodeau, Stephen N.

2013-01-01

As the ability to measure dense genetic markers approaches the limit of the DNA sequence itself, taking advantage of possible clustering of genetic variants in, and around, a gene would benefit genetic association analyses, and likely provide biological insights. The greatest benefit might be realized when multiple rare variants cluster in a functional region. Several statistical tests have been developed, one of which is based on the popular Kulldorff scan statistic for spatial clustering of disease. We extended another popular spatial clustering method – Tango’s statistic – to genomic sequence data. An advantage of Tango’s method is that it is rapid to compute, and when single test statistic is computed, its distribution is well approximated by a scaled chi-square distribution, making computation of p-values very rapid. We compared the Type-I error rates and power of several clustering statistics, as well as the omnibus sequence kernel association test (SKAT). Although our version of Tango’s statistic, which we call “Kernel Distance” statistic, took approximately half the time to compute than the Kulldorff scan statistic, it had slightly less power than the scan statistic. Our results showed that the Ionita-Laza version of Kulldorff’s scan statistic had the greatest power over a range of clustering scenarios. PMID:23842950

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

PubMed

Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

2018-01-01

In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

Mapping and simulating systematics due to spatially-varying observing conditions in DES science verification data

DOE PAGES

Leistedt, B.; Peiris, H. V.; Elsner, F.; ...

2016-10-17

Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. Wemore » illustrate the complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images. We then analyse the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially-varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and well-captured by the maps of observing conditions. The combined use of the maps, the SV data and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak lensing analyses. However, they will need to be carefully characterised in upcoming phases of DES in order to avoid biasing the inferred cosmological results. The framework presented is relevant to all multi-epoch surveys, and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null-tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky.« less

Mapping and simulating systematics due to spatially-varying observing conditions in DES science verification data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leistedt, B.; Peiris, H. V.; Elsner, F.

Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. Wemore » illustrate the complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images. We then analyse the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially-varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and well-captured by the maps of observing conditions. The combined use of the maps, the SV data and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak lensing analyses. However, they will need to be carefully characterised in upcoming phases of DES in order to avoid biasing the inferred cosmological results. The framework presented is relevant to all multi-epoch surveys, and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null-tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky.« less

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

PubMed Central

Zhang, Wei; Zhang, Xiaolong; Qiang, Yan; Tian, Qi; Tang, Xiaoxian

2017-01-01

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences. PMID:28880916

Regional heatwaves in china: a cluster analysis

NASA Astrophysics Data System (ADS)

Wang, Pinya; Tang, Jianping; Wang, Shuyu; Dong, Xinning; Fang, Juan

2018-03-01

With the consideration of spatial extension of heatwave events, two kind of regional heatwaves using absolute and relative thresholds, namely RHWs-A and RHWs-R, are investigated during 1959-2013. The temperature data is derived from the daily maximum temperatures (DMTs) of 587 stations in China. Totally 298 RHWs-A and 374 RHWs-R are identified during the past 55 years, and both of them are growing more frequent since the mid-1980s. By utilizing the cluster analysis, several typical spatial distributions of RHWs-A/RHWs-R are obtained. For RHWs-A, there are three clusters covering the southeastern, northwestern China and the lower reaches of Yangtze River, of which the southeastern cluster groups the most heatwaves. For RHWs-R, there are seven clusters distributed throughout the whole regions of China. The clusters in the northwestern and northeastern China are more stable than others for both RHWs-A and RHWs-R, and the northern clusters are of larger intensity than that of the southern ones. All RHWs-A/RHWs-R are accompanied by the anomalous high systems along with the reduced soil moisture. The southern clusters are controlled by Northwestern Pacific subtropical high (WPSH), and the northern ones are influenced by the mid-latitude high systems. The influences of atmospheric circulations and soil moisture on regional heatwaves are further demonstrated by two case analyses of the severe RHW-A in 2003 and the RHW-R in 2013.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

PubMed

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

PubMed Central

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters. PMID:26820646

Deep observation of A2163: studying a new bullet cluster

NASA Astrophysics Data System (ADS)

Bourdin, Herve

2011-10-01

Exhibiting a clear spatial separation between the gas and dark matter component of a fastly accreted subcluster, the `bullet cluster', 1E 0657-56, has provided us a unique laboratory to investigate the impact of violent cluster mergers on the Intra-Cluster Medium, galaxies and dark matter properties. In recent analyses of X-ray, optical and weak-lensing data, we show that the massive cluster A2163 also exhibits a crossing gas bullet separated from a galaxy and dark matter over-density, and suggest that both A2163 and 1E 0657-56 share a common merging scenario possibly just differing in the time elapsed after the closest cluster encounters. With this deeper XMM observation of A2163, we propose to refine our knowledge of the dynamics and geometry of the on-going subcluster accretion.

Exploring spatial patterns of farmland transactions and farmland use changes.

PubMed

Chang, Hsueh-Sheng; Chen, Tzu-Ling

2015-09-01

Strong economic incentives stimulate the conversion of farmland to non-farm uses possessing higher economic benefits, and rising land values can result in further conversions in the surrounding areas. However, previous studies focused exclusively on the analysis of attribute data, without concern for location or geographic information. Our study focuses on the application of spatial analysis method by exploring the magnitude and patterns of farmland use changes and farmland transactions in Tainan County in southwestern Taiwan. The results show that farmland use changes and transactions appear to cluster in specific locations-near urban planning areas, industrial parks, and science parks. Clustered farmland use changes indicate both excessive development of some farmland and possible protection of other farmland, while clustered farmland transactions indicate potential pressure for future conversion to non-farming uses. Overall, the spatial analyses indicate (without necessarily implying a cause-and-effect relationship) that the greater the farmland use change, the greater the number of farmland transactions. This approach to exploring the spatial patterns in and the interaction between farmland use change and farmland transactions can be applied to other regions facing increasing competition for farmland conversions and may be a useful tool for monitoring both urban expansion and increased farmland transactions. These occurrences should be closely monitored by governments to avoid excessive loss of farmland.

The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

NASA Astrophysics Data System (ADS)

Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

2017-07-01

Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

Population substructure and space use of Foxe Basin polar bears.

PubMed

Sahanatien, Vicki; Peacock, Elizabeth; Derocher, Andrew E

2015-07-01

Climate change has been identified as a major driver of habitat change, particularly for sea ice-dependent species such as the polar bear (Ursus maritimus). Population structure and space use of polar bears have been challenging to quantify because of their circumpolar distribution and tendency to range over large areas. Knowledge of movement patterns, home range, and habitat is needed for conservation and management. This is the first study to examine the spatial ecology of polar bears in the Foxe Basin management unit of Nunavut, Canada. Foxe Basin is in the mid-Arctic, part of the seasonal sea ice ecoregion and it is being negatively affected by climate change. Our objectives were to examine intrapopulation spatial structure, to determine movement patterns, and to consider how polar bear movements may respond to changing sea ice habitat conditions. Hierarchical and fuzzy cluster analyses were used to assess intrapopulation spatial structure of geographic position system satellite-collared female polar bears. Seasonal and annual movement metrics (home range, movement rates, time on ice) and home-range fidelity (static and dynamic overlap) were compared to examine the influence of regional sea ice on movements. The polar bears were distributed in three spatial clusters, and there were differences in the movement metrics between clusters that may reflect sea ice habitat conditions. Within the clusters, bears moved independently of each other. Annual and seasonal home-range fidelity was observed, and the bears used two movement patterns: on-ice range residency and annual migration. We predict that home-range fidelity may decline as the spatial and temporal predictability of sea ice changes. These new findings also provide baseline information for managing and monitoring this polar bear population.

An information theory analysis of spatial decisions in cognitive development

PubMed Central

Scott, Nicole M.; Sera, Maria D.; Georgopoulos, Apostolos P.

2015-01-01

Performance in a cognitive task can be considered as the outcome of a decision-making process operating across various knowledge domains or aspects of a single domain. Therefore, an analysis of these decisions in various tasks can shed light on the interplay and integration of these domains (or elements within a single domain) as they are associated with specific task characteristics. In this study, we applied an information theoretic approach to assess quantitatively the gain of knowledge across various elements of the cognitive domain of spatial, relational knowledge, as a function of development. Specifically, we examined changing spatial relational knowledge from ages 5 to 10 years. Our analyses consisted of a two-step process. First, we performed a hierarchical clustering analysis on the decisions made in 16 different tasks of spatial relational knowledge to determine which tasks were performed similarly at each age group as well as to discover how the tasks clustered together. We next used two measures of entropy to capture the gradual emergence of order in the development of relational knowledge. These measures of “cognitive entropy” were defined based on two independent aspects of chunking, namely (1) the number of clusters formed at each age group, and (2) the distribution of tasks across the clusters. We found that both measures of entropy decreased with age in a quadratic fashion and were positively and linearly correlated. The decrease in entropy and, therefore, gain of information during development was accompanied by improved performance. These results document, for the first time, the orderly and progressively structured “chunking” of decisions across the development of spatial relational reasoning and quantify this gain within a formal information-theoretic framework. PMID:25698915

Assessing the role of spatial correlations during collective cell spreading

PubMed Central

Treloar, Katrina K.; Simpson, Matthew J.; Binder, Benjamin J.; McElwain, D. L. Sean; Baker, Ruth E.

2014-01-01

Spreading cell fronts are essential features of development, repair and disease processes. Many mathematical models used to describe the motion of cell fronts, such as Fisher's equation, invoke a mean–field assumption which implies that there is no spatial structure, such as cell clustering, present. Here, we examine the presence of spatial structure using a combination of in vitro circular barrier assays, discrete random walk simulations and pair correlation functions. In particular, we analyse discrete simulation data using pair correlation functions to show that spatial structure can form in a spreading population of cells either through sufficiently strong cell–to–cell adhesion or sufficiently rapid cell proliferation. We analyse images from a circular barrier assay describing the spreading of a population of MM127 melanoma cells using the same pair correlation functions. Our results indicate that the spreading melanoma cell populations remain very close to spatially uniform, suggesting that the strength of cell–to–cell adhesion and the rate of cell proliferation are both sufficiently small so as not to induce any spatial patterning in the spreading populations. PMID:25026987

Deep and wide photometry of two open clusters NGC 1245 and NGC 2506: dynamical evolution and halo

NASA Astrophysics Data System (ADS)

Lee, S. H.; Kang, Y.-W.; Ann, H. B.

2013-06-01

We studied the structure of two old open clusters, NGC 1245 and NGC 2506, from a wide and deep VI photometry data acquired using the CFH12K CCD camera at Canada-France-Hawaii Telescope. We devised a new method for assigning cluster membership probability to individual stars using both spatial positions and positions in the colour-magnitude diagram. From analyses of the luminosity functions at several cluster-centric radii and the radial surface density profiles derived from stars with different luminosity ranges, we found that the two clusters are dynamically relaxed to drive significant mass segregation and evaporation of some fraction of low-mass stars. There seems to be a signature of tidal tail in NGC 1245 but the signal is too low to be confirmed.

Intracluster age gradients in numerous young stellar clusters

NASA Astrophysics Data System (ADS)

Getman, K. V.; Feigelson, E. D.; Kuhn, M. A.; Bate, M. R.; Broos, P. S.; Garmire, G. P.

2018-05-01

The pace and pattern of star formation leading to rich young stellar clusters is quite uncertain. In this context, we analyse the spatial distribution of ages within 19 young (median t ≲ 3 Myr on the Siess et al. time-scale), morphologically simple, isolated, and relatively rich stellar clusters. Our analysis is based on young stellar object (YSO) samples from the Massive Young Star-Forming Complex Study in Infrared and X-ray and Star Formation in Nearby Clouds surveys, and a new estimator of pre-main sequence (PMS) stellar ages, AgeJX, derived from X-ray and near-infrared photometric data. Median cluster ages are computed within four annular subregions of the clusters. We confirm and extend the earlier result of Getman et al. (2014): 80 per cent of the clusters show age trends where stars in cluster cores are younger than in outer regions. Our cluster stacking analyses establish the existence of an age gradient to high statistical significance in several ways. Time-scales vary with the choice of PMS evolutionary model; the inferred median age gradient across the studied clusters ranges from 0.75 to 1.5 Myr pc-1. The empirical finding reported in the present study - late or continuing formation of stars in the cores of star clusters with older stars dispersed in the outer regions - has a strong foundation with other observational studies and with the astrophysical models like the global hierarchical collapse model of Vázquez-Semadeni et al.

Spatial and Statistical Analysis of Leptospirosis in Guilan Province, Iran

NASA Astrophysics Data System (ADS)

Nia, A. Mohammadi; Alimohammadi, A.; Habibi, R.; Shirzadi, M. R.

2015-12-01

The most underdiagnosed water-borne bacterial zoonosis in the world is Leptospirosis which especially impacts tropical and humid regions. According to World Health Organization (WHO), the number of human cases is not known precisely. Available reports showed that worldwide incidences vary from 0.1-1 per 100 000 per year in temperate climates to 10-100 per 100 000 in the humid tropics. Pathogenic bacteria that is spread by the urines of rats is the main reason of water and soil infections. Rice field farmers who are in contact with infected water or soil, contain the most burden of leptospirosis prevalence. In recent years, this zoonotic disease have been occurred in north of Iran endemically. Guilan as the second rice production province (average=750 000 000 Kg, 40% of country production) after Mazandaran, has one of the most rural population (Male=487 679, Female=496 022) and rice workers (47 621 insured workers) among Iran provinces. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial clusters of leptospirosis to better understand epidemiological aspects of them in the province. Survey was performed during the period of 2009-2013 at rural district level throughout the study area. Global clustering methods including the average nearest neighbour distance, Moran's I and General G indices were utilized to investigate the annual spatial distribution of diseases. At the end, significant spatial clusters have been detected with the objective of informing priority areas for public health planning and resource allocation.

Geospatial Characterization of Fluvial Wood Arrangement in a Semi-confined Alluvial River

NASA Astrophysics Data System (ADS)

Martin, D. J.; Harden, C. P.; Pavlowsky, R. T.

2014-12-01

Large woody debris (LWD) has become universally recognized as an integral component of fluvial systems, and as a result, has become increasingly common as a river restoration tool. However, "natural" processes of wood recruitment and the subsequent arrangement of LWD within the river network are poorly understood. This research used a suite of spatial statistics to investigate longitudinal arrangement patterns of LWD in a low-gradient, Midwestern river. First, a large-scale GPS inventory of LWD, performed on the Big River in the eastern Missouri Ozarks, resulted in over 4,000 logged positions of LWD along seven river segments that covered nearly 100 km of the 237 km river system. A global Moran's I analysis indicates that LWD density is spatially autocorrelated and displays a clustering tendency within all seven river segments (P-value range = 0.000 to 0.054). A local Moran's I analysis identified specific locations along the segments where clustering occurs and revealed that, on average, clusters of LWD density (high or low) spanned 400 m. Spectral analyses revealed that, in some segments, LWD density is spatially periodic. Two segments displayed strong periodicity, while the remaining segments displayed varying degrees of noisiness. Periodicity showed a positive association with gravel bar spacing and meander wavelength, although there were insufficient data to statistically confirm the relationship. A wavelet analysis was then performed to investigate periodicity relative to location along the segment. The wavelet analysis identified significant (α = 0.05) periodicity at discrete locations along each of the segments. Those reaches yielding strong periodicity showed stronger relationships between LWD density and the geomorphic/riparian independent variables tested. Analyses consistently identified valley width and sinuosity as being associated with LWD density. The results of these analyses contribute a new perspective on the longitudinal distribution of LWD in a river system, which should help identify physical and/or riparian control mechanisms of LWD arrangement and support the development of models of LWD arrangement. Additionally, the spatial statistical tools presented here have shown to be valuable for identifying longitudinal patterns in river system components.

Spatial Variation of Soil Respiration in a Cropland under Winter Wheat and Summer Maize Rotation in the North China Plain.

PubMed

Huang, Ni; Wang, Li; Hu, Yongsen; Tian, Haifeng; Niu, Zheng

2016-01-01

Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson's correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales.

Spatial temporal clustering for hotspot using kulldorff scan statistic method (KSS): A case in Riau Province

NASA Astrophysics Data System (ADS)

Hudjimartsu, S. A.; Djatna, T.; Ambarwari, A.; Apriliantono

2017-01-01

The forest fires in Indonesia occurs frequently in the dry season. Almost all the causes of forest fires are caused by the human activity itself. The impact of forest fires is the loss of biodiversity, pollution hazard and harm the economy of surrounding communities. To prevent fires required the method, one of them with spatial temporal clustering. Spatial temporal clustering formed grouping data so that the results of these groupings can be used as initial information on fire prevention. To analyze the fires, used hotspot data as early indicator of fire spot. Hotspot data consists of spatial and temporal dimensions can be processed using the Spatial Temporal Clustering with Kulldorff Scan Statistic (KSS). The result of this research is to the effectiveness of KSS method to cluster spatial hotspot in a case within Riau Province and produces two types of clusters, most cluster and secondary cluster. This cluster can be used as an early fire warning information.

Bagging Voronoi classifiers for clustering spatial functional data

NASA Astrophysics Data System (ADS)

Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria

2013-06-01

We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, …). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient.

Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices.

PubMed

Anholt, R M; Berezowski, J; Robertson, C; Stephen, C

2015-09-01

There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.

Analysing malaria incidence at the small area level for developing a spatial decision support system: A case study in Kalaburagi, Karnataka, India.

PubMed

Shekhar, S; Yoo, E-H; Ahmed, S A; Haining, R; Kadannolly, S

2017-02-01

Spatial decision support systems have already proved their value in helping to reduce infectious diseases but to be effective they need to be designed to reflect local circumstances and local data availability. We report the first stage of a project to develop a spatial decision support system for infectious diseases for Karnataka State in India. The focus of this paper is on malaria incidence and we draw on small area data on new cases of malaria analysed in two-monthly time intervals over the period February 2012 to January 2016 for Kalaburagi taluk, a small area in Karnataka. We report the results of data mapping and cluster detection (identifying areas of excess risk) including evaluating the temporal persistence of excess risk and the local conditions with which high counts are statistically associated. We comment on how this work might feed into a practical spatial decision support system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Network-based spatial clustering technique for exploring features in regional industry

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Huang, Pi-Hui; Yang, Lung-Shih; Lin, Wen-Tzu

2008-10-01

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

MAPPING AND SIMULATING SYSTEMATICS DUE TO SPATIALLY VARYING OBSERVING CONDITIONS IN DES SCIENCE VERIFICATION DATA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leistedt, B.; Peiris, H. V.; Elsner, F.

Spatially varying depth and the characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, particularly in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES-SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. Wemore » illustrate the complementary nature of these two approaches by comparing the SV data with BCC-UFig, a synthetic sky catalog generated by forward-modeling of the DES-SV images. We analyze the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and are well-captured by the maps of observing conditions. The combined use of the maps, the SV data, and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak-lensing analyses. However, they will need to be carefully characterized in upcoming phases of DES in order to avoid biasing the inferred cosmological results. The framework presented here is relevant to all multi-epoch surveys and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky« less

Do Sexually Oriented Massage Parlors Cluster in Specific Neighborhoods? A Spatial Analysis of Indoor Sex Work in Los Angeles and Orange Counties, California.

PubMed

Chin, John J; Kim, Anna J; Takahashi, Lois; Wiebe, Douglas J

2015-01-01

Social determinants of health may be substantially affected by spatial factors, which together may explain the persistence of health inequities. Clustering of possible sources of negative health and social outcomes points to a spatial focus for future interventions. We analyzed the spatial clustering of sex work businesses in Southern California to examine where and why they cluster. We explored economic and legal factors as possible explanations of clustering. We manually coded data from a website used by paying members to post reviews of female massage parlor workers. We identified clusters of sexually oriented massage parlor businesses using spatial autocorrelation tests. We conducted spatial regression using census tract data to identify predictors of clustering. A total of 889 venues were identified. Clusters of tracts having higher-than-expected numbers of sexually oriented massage parlors ("hot spots") were located outside downtowns. These hot spots were characterized by a higher proportion of adult males, a higher proportion of households below the federal poverty level, and a smaller average household size. Sexually oriented massage parlors in Los Angeles and Orange counties cluster in particular neighborhoods. More research is needed to ascertain the causal factors of such clusters and how interventions can be designed to leverage these spatial factors.

Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters

PubMed Central

Kim, Jiyu; Jung, Inkyung

2017-01-01

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. PMID:28129368

Accounting for Subgroup Structure in Line-Transect Abundance Estimates of False Killer Whales (Pseudorca crassidens) in Hawaiian Waters

PubMed Central

Bradford, Amanda L.; Forney, Karin A.; Oleson, Erin M.; Barlow, Jay

2014-01-01

For biological populations that form aggregations (or clusters) of individuals, cluster size is an important parameter in line-transect abundance estimation and should be accurately measured. Cluster size in cetaceans has traditionally been represented as the total number of individuals in a group, but group size may be underestimated if group members are spatially diffuse. Groups of false killer whales (Pseudorca crassidens) can comprise numerous subgroups that are dispersed over tens of kilometers, leading to a spatial mismatch between a detected group and the theoretical framework of line-transect analysis. Three stocks of false killer whales are found within the U.S. Exclusive Economic Zone of the Hawaiian Islands (Hawaiian EEZ): an insular main Hawaiian Islands stock, a pelagic stock, and a Northwestern Hawaiian Islands (NWHI) stock. A ship-based line-transect survey of the Hawaiian EEZ was conducted in the summer and fall of 2010, resulting in six systematic-effort visual sightings of pelagic (n = 5) and NWHI (n = 1) false killer whale groups. The maximum number and spatial extent of subgroups per sighting was 18 subgroups and 35 km, respectively. These sightings were combined with data from similar previous surveys and analyzed within the conventional line-transect estimation framework. The detection function, mean cluster size, and encounter rate were estimated separately to appropriately incorporate data collected using different methods. Unlike previous line-transect analyses of cetaceans, subgroups were treated as the analytical cluster instead of groups because subgroups better conform to the specifications of line-transect theory. Bootstrap values (n = 5,000) of the line-transect parameters were randomly combined to estimate the variance of stock-specific abundance estimates. Hawai’i pelagic and NWHI false killer whales were estimated to number 1,552 (CV = 0.66; 95% CI = 479–5,030) and 552 (CV = 1.09; 95% CI = 97–3,123) individuals, respectively. Subgroup structure is an important factor to consider in line-transect analyses of false killer whales and other species with complex grouping patterns. PMID:24587372

Accounting for subgroup structure in line-transect abundance estimates of false killer whales (Pseudorca crassidens) in Hawaiian waters.

PubMed

Bradford, Amanda L; Forney, Karin A; Oleson, Erin M; Barlow, Jay

2014-01-01

For biological populations that form aggregations (or clusters) of individuals, cluster size is an important parameter in line-transect abundance estimation and should be accurately measured. Cluster size in cetaceans has traditionally been represented as the total number of individuals in a group, but group size may be underestimated if group members are spatially diffuse. Groups of false killer whales (Pseudorca crassidens) can comprise numerous subgroups that are dispersed over tens of kilometers, leading to a spatial mismatch between a detected group and the theoretical framework of line-transect analysis. Three stocks of false killer whales are found within the U.S. Exclusive Economic Zone of the Hawaiian Islands (Hawaiian EEZ): an insular main Hawaiian Islands stock, a pelagic stock, and a Northwestern Hawaiian Islands (NWHI) stock. A ship-based line-transect survey of the Hawaiian EEZ was conducted in the summer and fall of 2010, resulting in six systematic-effort visual sightings of pelagic (n = 5) and NWHI (n = 1) false killer whale groups. The maximum number and spatial extent of subgroups per sighting was 18 subgroups and 35 km, respectively. These sightings were combined with data from similar previous surveys and analyzed within the conventional line-transect estimation framework. The detection function, mean cluster size, and encounter rate were estimated separately to appropriately incorporate data collected using different methods. Unlike previous line-transect analyses of cetaceans, subgroups were treated as the analytical cluster instead of groups because subgroups better conform to the specifications of line-transect theory. Bootstrap values (n = 5,000) of the line-transect parameters were randomly combined to estimate the variance of stock-specific abundance estimates. Hawai'i pelagic and NWHI false killer whales were estimated to number 1,552 (CV = 0.66; 95% CI = 479-5,030) and 552 (CV = 1.09; 95% CI = 97-3,123) individuals, respectively. Subgroup structure is an important factor to consider in line-transect analyses of false killer whales and other species with complex grouping patterns.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

NASA Astrophysics Data System (ADS)

Huang, W.; Li, S.; Xu, S.

2016-06-01

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.

Self-Reinforcing Spatial Clusters of Migration and Socio-Economic Conditions in Finland in 1998-2006

ERIC Educational Resources Information Center

Lehtonen, Olli; Tykkylainen, Markku

2010-01-01

All the time the long economic growth period of 1992-2008 prevailed in Finland, the economic landscape was changing constantly and markedly. We analyse in this article how net migration plagued certain less competitive, mainly rural push-lose municipalities, and boosted in-migration to the pull-win municipalities in metropolitan environments…

Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

PubMed

Bi, Qifang; Azman, Andrew S; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S; Lessler, Justin

2016-02-01

Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic population suggests a possible role for highly targeted interventions. Studies with cluster designs in areas with strong spatial clustering of exposures should increase sample size to account for the correlation of these exposures.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

PubMed Central

Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J

2009-01-01

Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. Results By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. Conclusion An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity. PMID:19772590

Spatial, Temporal and Spatio-Temporal Patterns of Maritime Piracy.

PubMed

Marchione, Elio; Johnson, Shane D

2013-11-01

To examine patterns in the timing and location of incidents of maritime piracy to see whether, like many urban crimes, attacks cluster in space and time. Data for all incidents of maritime piracy worldwide recorded by the National Geospatial Intelligence Agency are analyzed using time-series models and methods originally developed to detect disease contagion. At the macro level, analyses suggest that incidents of pirate attacks are concentrated in five subregions of the earth's oceans and that the time series for these different subregions differ. At the micro level, analyses suggest that for the last 16 years (or more), pirate attacks appear to cluster in space and time suggesting that patterns are not static but are also not random. Much like other types of crime, pirate attacks cluster in space, and following an attack at one location the risk of others at the same location or nearby is temporarily elevated. The identification of such regularities has implications for the understanding of maritime piracy and for predicting the future locations of attacks.

A pastoral landscape for millennia: Investigating pastoral mobility in northeastern Jordan using quantitative spatial analyses

NASA Astrophysics Data System (ADS)

Meister, Julia; Knitter, Daniel; Krause, Jan; Müller-Neuhof, Bernd; Schütt, Brigitta

2017-04-01

Northeastern Jordan is one of the few remaining regions in the Middle East where pastoral nomadism is still practiced. In this desert region, pastoral mobility is an adapted land use—able to cope with low rainfall rates, great seasonal and annual rainfall variations and thus heterogeneous vegetation and water availability. During winter, herders and their livestock move into the desert; in summer they move to the desert margins to places with perennial water supply. The system of mobile pastoralism was introduced during the Early Late Neolithic. Within the basaltic region of northeastern Jordan, there is a dense distribution of archeological remains; some of them can be linked to pastoral groups due to the herders' ancient practice of building agglomerations of sub-circular enclosures ('clustered enclosures') made of basalt boulders for corralling their flocks and domestic activities. The resulting features provide an excellent opportunity to investigate a pastoral landscape that has been frequently used by herders during the last eight to nine millennia. In this study, 9118 clustered enclosures in the northeastern Jordanian basalt desert have been systematically recorded using satellite imagery. In order to investigate potential migration or communication routes, grazing lands and social interactions of former pastoralists, we examine their first- and second-order characteristics using distance and density based approaches of point pattern analyses by integrating geomorphometric and geomorphological site properties. The results of this spatial analysis are combined with available archaeological data and a review on traditional herding practices in northeastern Jordan. Overall, the results demonstrate that the observed spatial distribution of clustered enclosures is influenced locally by natural characteristics but regionally by cultural practices.

Small-area distribution of multiple sclerosis incidence in western France: in search of environmental triggers.

PubMed

Hammas, Karima; Yaouanq, Jacqueline; Lannes, Morgane; Edan, Gilles; Viel, Jean-François

2017-09-21

Despite intensive research over several decades, the etiology of multiple sclerosis (MS) remains poorly understood, although environmental factors are supposedly implicated. Our goal was to identify spatial clusters of MS incident cases at the small-area level to provide clues to local environmental risk factors that might cause or trigger the disease. A population-based and multi-stage study was performed in the French Brittany region to accurately ascertain the clinical onset of disease during the 2000-2004 period. The municipality of residence at the time of clinical onset was geocoded. To test for the presence of MS incidence clusters and to identify their approximate locations, we used a spatial scan statistic. We adjusted for socioeconomic deprivation, known to be strongly associated with increased MS incident rates, and scanned simultaneously for areas with either high or low rates. Sensitivity analyses (focusing on relapsing-remitting forms and/or places of residence available within the year following clinical onset) were performed. A total of 848 incident cases of MS were registered in Brittany, corresponding to a crude annual incidence rate of 5.8 per 100,000 inhabitants. The spatial scan statistic did not find a significant cluster of MS incidence in either the primary analysis (p value ≥ 0.56) or in the sensitivity analyses (p value ≥ 0.16). The findings of this study indicate that MS incidence is not markedly affected across space, suggesting that in the years preceding the first clinical expression of the disease, no environmental trigger is operative at the small-area population level in the French Brittany region.

The cluster-cluster correlation function. [of galaxies

NASA Technical Reports Server (NTRS)

Postman, M.; Geller, M. J.; Huchra, J. P.

1986-01-01

The clustering properties of the Abell and Zwicky cluster catalogs are studied using the two-point angular and spatial correlation functions. The catalogs are divided into eight subsamples to determine the dependence of the correlation function on distance, richness, and the method of cluster identification. It is found that the Corona Borealis supercluster contributes significant power to the spatial correlation function to the Abell cluster sample with distance class of four or less. The distance-limited catalog of 152 Abell clusters, which is not greatly affected by a single system, has a spatial correlation function consistent with the power law Xi(r) = 300r exp -1.8. In both the distance class four or less and distance-limited samples the signal in the spatial correlation function is a power law detectable out to 60/h Mpc. The amplitude of Xi(r) for clusters of richness class two is about three times that for richness class one clusters. The two-point spatial correlation function is sensitive to the use of estimated redshifts.

Application of a clustering-remote sensing method in analyzing security patterns

NASA Astrophysics Data System (ADS)

López-Caloca, Alejandra; Martínez-Viveros, Elvia; Chapela-Castañares, José Ignacio

2009-04-01

In Mexican academic and government circles, research on criminal spatial behavior has been neglected. Only recently has there been an interest in criminal data geo-reference. However, more sophisticated spatial analyses models are needed to disclose spatial patterns of crime and pinpoint their changes overtime. The main use of these models lies in supporting policy making and strategic intelligence. In this paper we present a model for finding patterns associated with crime. It is based on a fuzzy logic algorithm which finds the best fit within cluster numbers and shapes of groupings. We describe the methodology for building the model and its validation. The model was applied to annual data for types of felonies from 2005 to 2006 in the Mexican city of Hermosillo. The results are visualized as a standard deviational ellipse computed for the points identified to be a "cluster". These areas indicate a high to low demand for public security, and they were cross-related to urban structure analyzed by SPOT images and statistical data such as population, poverty levels, urbanization, and available services. The fusion of the model results with other geospatial data allows detecting obstacles and opportunities for crime commission in specific high risk zones and guide police activities and criminal investigations.

Human Embryonic Stem Cell-Derived Cardiomyocytes Self-Arrange with Areas of Different Subtypes During Differentiation.

PubMed

Vestergaard, Maj Linea; Grubb, Søren; Koefoed, Karen; Anderson-Jenkins, Zoe; Grunnet-Lauridsen, Kristina; Calloe, Kirstine; Clausen, Christian; Christensen, Søren Tvorup; Møllgård, Kjeld; Andersen, Claus Yding

2017-11-01

The derivation of functional cardiomyocytes (CMs) from human embryonic stem cells (hESCs) represents a unique way of studying human cardiogenesis, including the development of CM subtypes. In this study, we investigated the development and organization of hESC-derived cardiomyocytes (hESC-CMs) and examined how the expression levels of CM subtypes correspond to human in vivo cardiogenesis. Beating clusters were used to determine cardiac differentiation, which was evaluated by the expression of cardiac genes GATA4 and TNNT2 and subcellular localization of GATA4 and NKX2.5. Sharp electrode recordings to determine action potentials (APs) further revealed spatial organization of intracluster CM subtypes (ie, complex clusters). Nodal-, atrial-, and ventricular-like AP morphologies were detected within distinct regions of complex clusters. The ability of different CM subtypes to self-organize was documented by immunohistochemical analyses and a differential spatial expression of β-III tubulin, myosin light chain 2v (MLC-2V), and α-smooth muscle actin (α-SMA). Furthermore, all hESC-CM subtypes formed expressed primary cilia, which are known to coordinate cellular signaling pathways during cardiomyogenesis and heart development. This study expands the foundation for studying regulatory pathways for spatial and temporal CM differentiation during human cardiogenesis.

Probing the History of Galaxy Clusters with Metallicity and Entropy Measurements

NASA Astrophysics Data System (ADS)

Elkholy, Tamer Yohanna

Galaxy clusters are the largest gravitationally bound objects found today in our Universe. The gas they contain, the intra-cluster medium (ICM), is heated to temperatures in the approximate range of 1 to 10 keV, and thus emits X-ray radiation. Studying the ICM through the spatial and spectral analysis of its emission returns the richest information about both the overall cosmological context which governs the formation of clusters, as well as the physical processes occurring within. The aim of this thesis is to learn about the history of the physical processes that drive the evolution of galaxy clusters, through careful, spatially resolved measurements of their metallicity and entropy content. A sample of 45 nearby clusters observed with Chandra is analyzed to produce radial density, temperature, entropy and metallicity profiles. The entropy profiles are computed to larger radial extents than in previous Chandra analyses. The results of this analysis are made available to the scientific community in an electronic database. Comparing metallicity and entropy in the outskirts of clusters, we find no signature on the entropy profiles of the ensemble of supernovae that produced the observed metals. In the centers of clusters, we find that the metallicities of high-mass clusters are much less dispersed than those of low-mass clusters. A comparison of metallicity with the regularity of the X-ray emission morphology suggests that metallicities in low-mass clusters are more susceptible to increase from violent events such as mergers. We also find that the variation in the stellar-to-gas mass ratio as a function of cluster mass can explain the variation of central metallicity with cluster mass, only if we assume that there is a constant level of metallicity for clusters of all masses, above which the observed galaxies add more metals in proportion to their mass. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs mit.edu)

Identification of Volcanic Landforms and Processes on Earth and Mars using Geospatial Analysis (Invited)

NASA Astrophysics Data System (ADS)

Fagents, S. A.; Hamilton, C. W.

2009-12-01

Nearest neighbor (NN) analysis enables the identification of landforms using non-morphological parameters and can be useful for constraining the geological processes contributing to observed patterns of spatial distribution. Explosive interactions between lava and water can generate volcanic rootless cone (VRC) groups that are well suited to geospatial analyses because they consist of a large number of landforms that share a common formation mechanism. We have applied NN analysis tools to quantitatively compare the spatial distribution of VRCs in the Laki lava flow in Iceland to analogous landforms in the Tartarus Colles Region of eastern Elysium Planitia, Mars. Our results show that rootless eruption sites on both Earth and Mars exhibit systematic variations in spatial organization that are related to variations in the distribution of resources (lava and water) at different scales. Field observations in Iceland reveal that VRC groups are composite structures formed by the emplacement of chronologically and spatially distinct domains. Regionally, rootless cones cluster into groups and domains, but within domains NN distances exhibit random to repelled distributions. This suggests that on regional scales VRCs cluster in locations that contain sufficient resources, whereas on local scales rootless eruption sites tend to self-organize into distributions that maximize the utilization of limited resources (typically groundwater). Within the Laki lava flow, near-surface water is abundant and pre-eruption topography appears to exert the greatest control on both lava inundation regions and clustering of rootless eruption sites. In contrast, lava thickness appears to be the controlling factor in the formation of rootless eruption sites in the Tartarus Colles Region. A critical lava thickness may be required to initiate rootless eruptions on Mars because the lava flows must contain sufficient heat for transferred thermal energy to reach the underlying cryosphere and volatilize buried ground ice. In both environments, the spatial distribution of rootless eruption sites on local scales may either be random, which indicates that rootless eruption sites form independently of one another, or repelled, which implies resource limitation. Where competition for limited groundwater causes rootless eruption sites to develop greater than random NN separation, rootless eruption sites can be modeled as a system of pumping wells that extract water from a shared aquifer, thereby generating repelled distributions due to non-initiation or early cessation of rootless explosive activity at sites with insufficient access to groundwater. Thus statistical NN analyses can be combined with field observations and remote sensing to obtain information about self-organization processes within geological systems and the effects of environmental resource limitation on the spatial distribution of volcanic landforms. NN analyses may also be used to quantitatively compare the spatial distribution of landforms in different planetary environments and for supplying non-morphological evidence to discriminate between feature identities and geological formation mechanisms.

Do Sexually Oriented Massage Parlors Cluster in Specific Neighborhoods? A Spatial Analysis of Indoor Sex Work in Los Angeles and Orange Counties, California

PubMed Central

Kim, Anna J.; Takahashi, Lois; Wiebe, Douglas J.

2015-01-01

Objective Social determinants of health may be substantially affected by spatial factors, which together may explain the persistence of health inequities. Clustering of possible sources of negative health and social outcomes points to a spatial focus for future interventions. We analyzed the spatial clustering of sex work businesses in Southern California to examine where and why they cluster. We explored economic and legal factors as possible explanations of clustering. Methods We manually coded data from a website used by paying members to post reviews of female massage parlor workers. We identified clusters of sexually oriented massage parlor businesses using spatial autocorrelation tests. We conducted spatial regression using census tract data to identify predictors of clustering. Results A total of 889 venues were identified. Clusters of tracts having higher-than-expected numbers of sexually oriented massage parlors (“hot spots”) were located outside downtowns. These hot spots were characterized by a higher proportion of adult males, a higher proportion of households below the federal poverty level, and a smaller average household size. Conclusion Sexually oriented massage parlors in Los Angeles and Orange counties cluster in particular neighborhoods. More research is needed to ascertain the causal factors of such clusters and how interventions can be designed to leverage these spatial factors. PMID:26327731

Localization Microscopy Analyses of MRE11 Clusters in 3D-Conserved Cell Nuclei of Different Cell Lines.

PubMed

Eryilmaz, Marion; Schmitt, Eberhard; Krufczik, Matthias; Theda, Franziska; Lee, Jin-Ho; Cremer, Christoph; Bestvater, Felix; Schaufler, Wladimir; Hausmann, Michael; Hildenbrand, Georg

2018-01-22

In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell's decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM) allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex), a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h) after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair.

Localization Microscopy Analyses of MRE11 Clusters in 3D-Conserved Cell Nuclei of Different Cell Lines

PubMed Central

Eryilmaz, Marion; Schmitt, Eberhard; Krufczik, Matthias; Theda, Franziska; Lee, Jin-Ho; Cremer, Christoph; Bestvater, Felix; Schaufler, Wladimir; Hildenbrand, Georg

2018-01-01

In radiation biophysics, it is a subject of nowadays research to investigate DNA strand break repair in detail after damage induction by ionizing radiation. It is a subject of debate as to what makes up the cell’s decision to use a certain repair pathway and how the repair machinery recruited in repair foci is spatially and temporarily organized. Single-molecule localization microscopy (SMLM) allows super-resolution analysis by precise localization of single fluorescent molecule tags, resulting in nuclear structure analysis with a spatial resolution in the 10 nm regime. Here, we used SMLM to study MRE11 foci. MRE11 is one of three proteins involved in the MRN-complex (MRE11-RAD50-NBS1 complex), a prominent DNA strand resection and broken end bridging component involved in homologous recombination repair (HRR) and alternative non-homologous end joining (a-NHEJ). We analyzed the spatial arrangements of antibody-labelled MRE11 proteins in the nuclei of a breast cancer and a skin fibroblast cell line along a time-course of repair (up to 48 h) after irradiation with a dose of 2 Gy. Different kinetics for cluster formation and relaxation were determined. Changes in the internal nano-scaled structure of the clusters were quantified and compared between the two cell types. The results indicate a cell type-dependent DNA damage response concerning MRE11 recruitment and cluster formation. The MRE11 data were compared to H2AX phosphorylation detected by γH2AX molecule distribution. These data suggested modulations of MRE11 signal frequencies that were not directly correlated to DNA damage induction. The application of SMLM in radiation biophysics offers new possibilities to investigate spatial foci organization after DNA damaging and during subsequent repair. PMID:29361783

Targeting regional pediatric congenital hearing loss using a spatial scan statistic.

PubMed

Bush, Matthew L; Christian, Warren Jay; Bianchi, Kristin; Lester, Cathy; Schoenberg, Nancy

2015-01-01

Congenital hearing loss is a common problem, and timely identification and intervention are paramount for language development. Patients from rural regions may have many barriers to timely diagnosis and intervention. The purpose of this study was to examine the spatial and hospital-based distribution of failed infant hearing screening testing and pediatric congenital hearing loss throughout Kentucky. Data on live births and audiological reporting of infant hearing loss results in Kentucky from 2009 to 2011 were analyzed. The authors used spatial scan statistics to identify high-rate clusters of failed newborn screening tests and permanent congenital hearing loss (PCHL), based on the total number of live births per county. The authors conducted further analyses on PCHL and failed newborn hearing screening tests, based on birth hospital data and method of screening. The authors observed four statistically significant (p < 0.05) high-rate clusters with failed newborn hearing screenings in Kentucky, including two in the Appalachian region. Hospitals using two-stage otoacoustic emission testing demonstrated higher rates of failed screening (p = 0.009) than those using two-stage automated auditory brainstem response testing. A significant cluster of high rate of PCHL was observed in Western Kentucky. Five of the 54 birthing hospitals were found to have higher relative risk of PCHL, and two of those hospitals are located in a very rural region of Western Kentucky within the cluster. This spatial analysis in children in Kentucky has identified specific regions throughout the state with high rates of congenital hearing loss and failed newborn hearing screening tests. Further investigation regarding causative factors is warranted. This method of analysis can be useful in the setting of hearing health disparities to focus efforts on regions facing high incidence of congenital hearing loss.

Phylogeographic analysis of rabies viruses in the Philippines.

PubMed

Tohma, Kentaro; Saito, Mariko; Kamigaki, Taro; Tuason, Laarni T; Demetria, Catalino S; Orbina, Jun Ryan C; Manalo, Daria L; Miranda, Mary E; Noguchi, Akira; Inoue, Satoshi; Suzuki, Akira; Quiambao, Beatriz P; Oshitani, Hitoshi

2014-04-01

Rabies still remains a public health threat in the Philippines. A significant number of human rabies cases, about 200-300 cases annually, have been reported, and the country needs an effective strategy for rabies control. To develop an effective control strategy, it is important to understand the transmission patterns of the rabies viruses. We conducted phylogenetic analyses by considering the temporal and spatial evolution of rabies viruses to reveal the transmission dynamics in the Philippines. After evaluating the molecular clock and phylogeographic analysis, we estimated that the Philippine strains were introduced from China around the beginning of 20th century. Upon this introduction, the rabies viruses evolved within the Philippines to form three major clades, and there was no indication of introduction of other rabies viruses from any other country. However, within the Philippines, island-to-island migrations were observed. Since then, the rabies viruses have diffused and only evolved within each island group. The evolutionary pattern of these viruses was strongly shaped by geographical boundaries. The association index statistics demonstrated a strong spatial structure within the island group, indicating that the seas were a significant geographical barrier for viral dispersal. Strong spatial structure was also observed even at a regional level, and most of the viral migrations (79.7% of the total median number) in Luzon were observed between neighboring regions. Rabies viruses were genetically clustered at a regional level, and this strong spatial structure suggests a geographical clustering of transmission chains and the potential effectiveness of rabies control that targets geographical clustering. Dog vaccination campaigns have been conducted independently by local governments in the Philippines, but it could be more effective to implement a coordinated vaccination campaign among neighboring areas to eliminate geographically-clustered rabies transmission chains. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Spatial Analysis of Hospital Incidence and in Hospital Mortality of Abdominal Aortic Aneurysms in Germany: Secondary Data Analysis of Nationwide Hospital Episode (DRG) Data.

PubMed

Kuehnl, Andreas; Salvermoser, Michael; Erk, Alexander; Trenner, Matthias; Schmid, Volker; Eckstein, Hans-Henning

2018-06-01

This study aimed to analyze the spatial distribution and regional variation of the hospital incidence and in hospital mortality of abdominal aortic aneurysms (AAA) in Germany. German DRG statistics (2011-2014) were analysed. Patients with ruptured AAA (rAAA, I71.3, treated or not) and patients with non-ruptured AAA (nrAAA, I71.4, treated by open or endovascular aneurysm repair) were included. Age, sex, and risk standardisation was done using standard statistical procedures. Regional variation was quantified using systematic component of variation. To analyse spatial auto-correlation and spatial pattern, global Moran's I and Getis-Ord Gi* were calculated. A total of 50,702 cases were included. Raw hospital incidence of AAA was 15.7 per 100,000 inhabitants (nrAAA 13.1; all rAAA 2.7; treated rAAA 1.6). The standardised hospital incidence of AAA ranged from 6.3 to 30.3 per 100,000. Systematic component of variation proportion was 96% in nrAAA and 55% in treated rAAA. Incidence rates of all AAA were significantly clustered with above average values in the northwestern parts of Germany and below average values in the south and eastern regions. Standardised mortality of nrAAA ranged from 1.7% to 4.3%, with that of treated rAAA ranging from 28% to 52%. Regional variation and spatial distribution of standardised mortality was not different from random. There was significant regional variation and clustering of the hospital incidence of AAA in Germany, with higher rates in the northwest and lower rates in the southeast. There was no significant variation in standardised (age/sex/risk) mortality between counties. Copyright © 2018. Published by Elsevier B.V.

Spatial Clustering of Aedes aegypti Related to Breeding Container Characteristics in Coastal Ecuador: Implications for Dengue Control

PubMed Central

Schafrick, Nathaniel H.; Milbrath, Meghan O.; Berrocal, Veronica J.; Wilson, Mark L.; Eisenberg, Joseph N. S.

2013-01-01

Mosquito management within households remains central to the control of dengue virus transmission. An important factor in these management decisions is the spatial clustering of Aedes aegypti. We measured spatial clustering of Ae. aegypti in the town of Borbón, Ecuador and assessed what characteristics of breeding containers influenced the clustering. We used logistic regression to assess the spatial extent of that clustering. We found strong evidence for juvenile mosquito clustering within 20 m and for adult mosquito clustering within 10 m, and stronger clustering associations for containers ≥ 40 L than those < 40 L. Aedes aegypti clusters persisted after adjusting for various container characteristics, suggesting that patterns are likely attributable to short dispersal distances rather than shared characteristics of containers in cluster areas. These findings have implications for targeting Ae. aegypti control efforts. PMID:24002483

Temporal Dynamics and Persistence of Spatial Patterns: from Groundwater to Soil Moisture to Transpiration

NASA Astrophysics Data System (ADS)

Blume, T.; Hassler, S. K.; Weiler, M.

2017-12-01

Hydrological science still struggles with the fact that while we wish for spatially continuous images or movies of state variables and fluxes at the landscape scale, most of our direct measurements are point measurements. To date regional measurements resolving landscape scale patterns can only be obtained by remote sensing methods, with the common drawback that they remain near the earth surface and that temporal resolution is generally low. However, distributed monitoring networks at the landscape scale provide the opportunity for detailed and time-continuous pattern exploration. Even though measurements are spatially discontinuous, the large number of sampling points and experimental setups specifically designed for the purpose of landscape pattern investigation open up new avenues of regional hydrological analyses. The CAOS hydrological observatory in Luxembourg offers a unique setup to investigate questions of temporal stability, pattern evolution and persistence of certain states. The experimental setup consists of 45 sensor clusters. These sensor clusters cover three different geologies, two land use classes, five different landscape positions, and contrasting aspects. At each of these sensor clusters three soil moisture/soil temperature profiles, basic climate variables, sapflow, shallow groundwater, and stream water levels were measured continuously for the past 4 years. We will focus on characteristic landscape patterns of various hydrological state variables and fluxes, studying their temporal stability on the one hand and the dependence of patterns on hydrological states on the other hand (e.g. wet vs dry). This is extended to time-continuous pattern analysis based on time series of spatial rank correlation coefficients. Analyses focus on the absolute values of soil moisture, soil temperature, groundwater levels and sapflow, but also investigate the spatial pattern of the daily changes of these variables. The analysis aims at identifying hydrologic signatures of the processes or landscape characteristics acting as major controls. While groundwater, soil water and transpiration are closely linked by the water cycle, they are controlled by different processes and we expect this to be reflected in interlinked but not necessarily congruent patterns and responses.

Design of trials for interrupting the transmission of endemic pathogens.

PubMed

Silkey, Mariabeth; Homan, Tobias; Maire, Nicolas; Hiscox, Alexandra; Mukabana, Richard; Takken, Willem; Smith, Thomas A

2016-06-06

Many interventions against infectious diseases have geographically diffuse effects. This leads to contamination between arms in cluster-randomized trials (CRTs). Pathogen elimination is the goal of many intervention programs against infectious agents, but contamination means that standard CRT designs and analyses do not provide inferences about the potential of interventions to interrupt pathogen transmission at maximum scale-up. A generic model of disease transmission was used to simulate infections in stepped wedge cluster-randomized trials (SWCRTs) of a transmission-reducing intervention, where the intervention has spatially diffuse effects. Simulations of such trials were then used to examine the potential of such designs for providing generalizable causal inferences about the impact of such interventions, including measurements of the contamination effects. The simulations were applied to the geography of Rusinga Island, Lake Victoria, Kenya, the site of the SolarMal trial on the use of odor-baited mosquito traps to eliminate Plasmodium falciparum malaria. These were used to compare variants in the proposed SWCRT designs for the SolarMal trial. Measures of contamination effects were found that could be assessed in the simulated trials. Inspired by analyses of trials of insecticide-treated nets against malaria when applied to the geography of the SolarMal trial, these measures were found to be robust to different variants of SWCRT design. Analyses of the likely extent of contamination effects supported the choice of cluster size for the trial. The SWCRT is an appropriate design for trials that assess the feasibility of local elimination of a pathogen. The effects of incomplete coverage can be estimated by analyzing the extent of contamination between arms in such trials, and the estimates also support inferences about causality. The SolarMal example illustrates how generic transmission models incorporating spatial smoothing can be used to simulate such trials for a power calculation and optimization of cluster size and randomization strategies. The approach is applicable to a range of infectious diseases transmitted via environmental reservoirs or via arthropod vectors.

Clusters in irregular areas and lattices.

PubMed

Wieczorek, William F; Delmerico, Alan M; Rogerson, Peter A; Wong, David W S

2012-01-01

Geographic areas of different sizes and shapes of polygons that represent counts or rate data are often encountered in social, economic, health, and other information. Often political or census boundaries are used to define these areas because the information is available only for those geographies. Therefore, these types of boundaries are frequently used to define neighborhoods in spatial analyses using geographic information systems and related approaches such as multilevel models. When point data can be geocoded, it is possible to examine the impact of polygon shape on spatial statistical properties, such as clustering. We utilized point data (alcohol outlets) to examine the issue of polygon shape and size on visualization and statistical properties. The point data were allocated to regular lattices (hexagons and squares) and census areas for zip-code tabulation areas and tracts. The number of units in the lattices was set to be similar to the number of tract and zip-code areas. A spatial clustering statistic and visualization were used to assess the impact of polygon shape for zip- and tract-sized units. Results showed substantial similarities and notable differences across shape and size. The specific circumstances of a spatial analysis that aggregates points to polygons will determine the size and shape of the areal units to be used. The irregular polygons of census units may reflect underlying characteristics that could be missed by large regular lattices. Future research to examine the potential for using a combination of irregular polygons and regular lattices would be useful.

Geographic distribution of HIV stigma among women of childbearing age in rural Kenya

PubMed Central

Akullian, Adam; Kohler, Pamela; Kinuthia, John; Laserson, Kayla; Mills, Lisa A.; Okanda, John; Olilo, George; Ombok, Maurice; Odhiambo, Frank; Rao, Deepa; Wakefield, Jonathan; John-Stewart, Grace

2015-01-01

Objective(s) HIV stigma is considered to be a major driver of the HIV/AIDS pandemic, yet there is a limited understanding of its occurrence. We describe the geographic patterns of two forms of HIV stigma in a cross-sectional sample of women of childbearing age from western Kenya: internalized stigma (associated with shame) and externalized stigma (associated with blame). Design Geographic studies of HIV stigma provide a first step in generating hypotheses regarding potential community-level causes of stigma and may lead to more effective community-level interventions. Methods Spatial regression using generalized additive models and point pattern analyses using K-functions were used to assess the spatial scale(s) at which each form of HIV stigma clusters, and to assess whether the spatial clustering of each stigma indicator was present after adjustment for individual-level characteristics. Results There was evidence that externalized stigma (blame) was geographically heterogeneous across the study area, even after controlling for individual-level factors (P=0.01). In contrast, there was less evidence (P=0.70) of spatial trend or clustering of internalized stigma (shame). Conclusion Our results may point to differences in the underlying social processes motivating each form of HIV stigma. Externalized stigma may be driven more by cultural beliefs disseminated within communities, whereas internalized stigma may be the result of individual-level characteristics outside the domain of community influence. These data may inform community-level interventions to decrease HIV-related stigma, and thus impact the HIV epidemic. PMID:24835356

Spatial effects on hybrid electric vehicle adoption

DOE PAGES

Liu, Xiaoli; Roberts, Matthew C.; Sioshansi, Ramteen

2017-03-08

This paper examines spatial effects on hybrid-electric vehicle (HEV) adoption. This is in contrast to most existing analyses, which concentrate on analyzing socioeconomic factors and demographics. This paper uses a general spatial model to estimate the strength of ‘neighbor effects’ on HEV adoption—namely that each consumer’s HEV-adoption decision can be influenced by the HEV-adoption decisions of geographic neighbors. We use detailed census tract-level demographic data from the 2010 United States Census and the 2012 American Community Survey and vehicle registration data collected by the Ohio Bureau of Motor Vehicles. We find that HEV adoption exhibits significant spatial effects. We furthermore » conduct a time-series analysis and show that historical HEV adoption has a spatial effect on future adoption. Lastly, these results suggest that HEVs may appear in more dense clusters than models that do not consider spatial effects predict.« less

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Mapping the bycatch seascape: multispecies and multi-scale spatial patterns of fisheries bycatch.

PubMed

Lewison, Rebecca L; Soykan, Candan U; Franklin, Janet

2009-06-01

Fisheries bycatch is a worldwide conservation issue. Despite a growing awareness of bycatch problems in particular ocean regions, there have been few efforts to identify spatial patterns in bycatch events. Furthermore, many studies of fisheries bycatch have been myopic, focusing on a single species or a single region. Using a range of analytical approaches to identify spatial patterns in bycatch data, we demonstrate the utility and applications of area and point pattern analyses to single and multispecies bycatch seascapes of pelagic longline fisheries in the Atlantic and Pacific Oceans. We find clear evidence of spatial clustering within bycatch species in both ocean basins, both in terms of the underlying pattern of the locations of bycatch events relative to fishing locations and for areas of high bycatch rates. Furthermore, we find significant spatial overlap in the pattern of bycatch across species relative to the spatial distribution in fishing effort and target catch. These results point to the importance of considering spatial patterns of both single and multispecies bycatch to meet the ultimate goal of reducing bycatch encounters. These analyses also highlight the importance of considering bycatch relative to target catch as a way of identifying areas where fishing effort reduction may help to reduce multispecies bycatch with minimal impact on target catch.

Geographic clustering of elderly people with above-norm anthropometric measurements and blood chemistry.

PubMed

Mena, Carlos; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2017-05-11

The global percentage of people over 60 is strongly increasing and estimated to exceed 20% by 20,150, which means that there will be an increase in many pathological conditions related to aging. Mapping of the location of aging people and identification of their needs can be extremely valuable from a social-economic point of view. Participants in this study were 148 randomly selected adults from Talca City, Chile aged 60-74 at baseline. Geographic information systems (GIS) analyses were performed using ArcGIS software through its module Spatial Autocorrelation. In this study, we demonstrated that elderly people show geographic clustering according to above-norm results of anthropometric measurements and blood chemistry. The spatial identifications found would facilitate exploring the impact of treatment programmes in communities where many aging people live, thereby improving their quality of life as well as reducing overall costs.

Deconvolving the Fate of Carbon in Coastal Sediments

NASA Astrophysics Data System (ADS)

Van der Voort, Tessa S.; Mannu, Utsav; Blattmann, Thomas M.; Bao, Rui; Zhao, Meixun; Eglinton, Timothy I.

2018-05-01

Coastal oceans play a crucial role in the global carbon cycle, and are increasingly affected by anthropogenic forcing. Understanding carbon cycling in coastal environments is hindered by convoluted sources and myriad processes that vary over a range of spatial and temporal scales. In this study, we deconvolve the complex mosaic of organic carbon manifested in Chinese Marginal Sea (CMS) sediments using a novel numerical clustering algorithm based on 14C and total OC content. Results reveal five regions that encompass geographically distinct depositional settings. Complementary statistical analyses reveal contrasting region-dependent controls on carbon dynamics and composition. Overall, clustering is shown to be highly effective in demarcating areas of distinct organic facies by disentangling intertwined organic geochemical patterns resulting from superimposed effects of OC provenance, reworking and deposition on a shelf region exhibiting pronounced spatial heterogeneity. This information will aid in constraining region-specific budgets of carbon burial and carbon cycle processes.

Spatio-temporal patterns of Campylobacter colonization in Danish broilers.

PubMed

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K

2013-05-01

Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified in the same areas in the summer months from 2007 to 2009. Range of influence between broiler farms were estimated at distances of 9.6 km and 13.5 km in different years. Identification of areas and time with greater risk indicates variable presence of risk factors with space and time. Implementation of safety measures on farms within high-risk clusters during summer could have an impact in reducing prevalence.

Earliest evidence for the structure of Homo sapiens populations in Africa

NASA Astrophysics Data System (ADS)

Scerri, Eleanor M. L.; Drake, Nick A.; Jennings, Richard; Groucutt, Huw S.

2014-10-01

Understanding the structure and variation of Homo sapiens populations in Africa is critical for interpreting multiproxy evidence of their subsequent dispersals into Eurasia. However, there is no consensus on early H. sapiens demographic structure, or its effects on intra-African dispersals. Here, we show how a patchwork of ecological corridors and bottlenecks triggered a successive budding of populations across the Sahara. Using a temporally and spatially explicit palaeoenvironmental model, we found that the Sahara was not uniformly ameliorated between ∼130 and 75 thousand years ago (ka), as has been stated. Model integration with multivariate analyses of corresponding stone tools then revealed several spatially defined technological clusters which correlated with distinct palaeobiomes. Similarities between technological clusters were such that they decreased with distance except where connected by palaeohydrological networks. These results indicate that populations at the Eurasian gateway were strongly structured, which has implications for refining the demographic parameters of dispersals out of Africa.

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey.

PubMed

Erdogan, Saffet

2009-10-01

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey. Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P<0.05 with spatial autocorrelation analyses. Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P<0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R(2) values. Geographically weighted regression provided values of 0.89-0.99 adjusted R(2) for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions. Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.

Spatial clustering of childhood leukaemia in Switzerland: A nationwide study.

PubMed

Konstantinoudis, Garyfallos; Kreis, Christian; Ammann, Roland A; Niggli, Felix; Kuehni, Claudia E; Spycher, Ben D

2017-10-01

The aetiology of childhood leukaemia remains largely unknown. Several hypotheses involve environmental exposures that could implicate spatial clustering of cases. The evidence from previous clustering studies is inconclusive. Most of them used areal data and thus had limited spatial resolution. We investigated whether childhood leukaemia tends to cluster in space using exact geocodes of place of residence both at the time of birth or diagnosis. We included 1,871 leukaemia cases diagnosed between 1985 and 2015 at age 0-15 years from the Swiss Childhood Cancer Registry. For each case, we randomly sampled 10 age and sex matched controls from national censuses closest in time. We used the difference of k-functions, Cuzick-Edwards' test and Tango's index for point data to assess spatial clustering and Kulldorff's circular scan to detect clusters. We separately investigated acute lymphoid leukaemia (ALL), acute myeloid leukaemia (AML), different age groups at diagnosis (0-4, 5-15 years) and adjusted for multiple testing. After adjusting for multiple testing, we found no evidence of spatial clustering of childhood leukaemia neither around time of birth (p = 0.52) nor diagnosis (p = 0.51). Individual tests indicated spatial clustering for leukaemia diagnosed at age 5-15 years, p k-functions = 0.05 and p Cuzick-Edwards' = 0.04 and a cluster of ALL cases diagnosed at age 0-4 years in a small rural area (p = 0.05). This study provides little evidence of spatial clustering of childhood leukaemia in Switzerland and highlights the importance of accounting for multiple testing in clustering studies. © 2017 UICC.

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm. PMID:22443103

A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

PubMed

Tango, Toshiro; Takahashi, Kunihiko

2012-12-30

Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh

PubMed Central

Bi, Qifang; Azman, Andrew S.; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S.; Lessler, Justin

2016-01-01

Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic population suggests a possible role for highly targeted interventions. Studies with cluster designs in areas with strong spatial clustering of exposures should increase sample size to account for the correlation of these exposures. PMID:26866926

Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study

PubMed Central

Vergara, María; Basto, Mafalda P.; Madeira, María José; Gómez-Moliner, Benjamín J.; Santos-Reis, Margarida; Fernandes, Carlos; Ruiz-González, Aritz

2015-01-01

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species. PMID:26222680

Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study.

PubMed

Vergara, María; Basto, Mafalda P; Madeira, María José; Gómez-Moliner, Benjamín J; Santos-Reis, Margarida; Fernandes, Carlos; Ruiz-González, Aritz

2015-01-01

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

Privacy protection versus cluster detection in spatial epidemiology.

PubMed

Olson, Karen L; Grannis, Shaun J; Mandl, Kenneth D

2006-11-01

Patient data that includes precise locations can reveal patients' identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Sparsely-distributed organization of face and limb activations in human ventral temporal cortex

PubMed Central

Weiner, Kevin S.; Grill-Spector, Kalanit

2011-01-01

Functional magnetic resonance imaging (fMRI) has identified face- and body part-selective regions, as well as distributed activation patterns for object categories across human ventral temporal cortex (VTC), eliciting a debate regarding functional organization in VTC and neural coding of object categories. Using high-resolution fMRI, we illustrate that face- and limb-selective activations alternate in a series of largely nonoverlapping clusters in lateral VTC along the inferior occipital gyrus (IOG), fusiform gyrus (FG), and occipitotemporal sulcus (OTS). Both general linear model (GLM) and multivoxel pattern (MVP) analyses show that face- and limb-selective activations minimally overlap and that this organization is consistent across experiments and days. We provide a reliable method to separate two face-selective clusters on the middle and posterior FG (mFus and pFus), and another on the IOG using their spatial relation to limb-selective activations and retinotopic areas hV4, VO-1/2, and hMT+. Furthermore, these activations show a gradient of increasing face selectivity and decreasing limb selectivity from the IOG to the mFus. Finally, MVP analyses indicate that there is differential information for faces in lateral VTC (containing weakly- and highly-selective voxels) relative to non-selective voxels in medial VTC. These findings suggest a sparsely-distributed organization where sparseness refers to the presence of several face- and limb-selective clusters in VTC, and distributed refers to the presence of different amounts of information in highly-, weakly-, and non-selective voxels. Consequently, theories of object recognition should consider the functional and spatial constraints of neural coding across a series of nonoverlapping category-selective clusters that are themselves distributed. PMID:20457261

cluster trials v. 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, John; Castillo, Andrew

2016-09-21

This software contains a set of python modules – input, search, cluster, analysis; these modules read input files containing spatial coordinates and associated attributes which can be used to perform nearest neighbor search (spatial indexing via kdtree), cluster analysis/identification, and calculation of spatial statistics for analysis.

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

NASA Astrophysics Data System (ADS)

Zourlidou, S.; Sester, M.

2016-06-01

The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location - thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

A spatial scan statistic for nonisotropic two-level risk cluster.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Spatial structure and electronic spectrum of TiSi{/n -} clusters ( n = 6-18)

NASA Astrophysics Data System (ADS)

Borshch, N. A.; Pereslavtseva, N. S.; Kurganskii, S. I.

2014-10-01

Results from optimizing the spatial structure and calculated electronic spectra of anion clusters TiSi{/n -} ( n = 6-18) are presented. Calculations are performed within the density functional theory. Spatial structures of clusters detected experimentally are established by comparing the calculated and experimental data. It is shown that prismatic and fullerene-like structures are the ones most energetically favorable for clusters TiSi{/n -}. It is concluded that these structures are basic when building clusters with close numbers of silicon atoms.

Investigating the internal structure of galaxies and clusters through strong gravitational lensing

NASA Astrophysics Data System (ADS)

Jigish Gandhi, Pratik; Grillo, Claudio; Bonamigo, Mario

2018-01-01

Gravitational lensing studies have radically improved our understanding of the internal structure of galaxies and cluster-scale systems. In particular, the combination of strong lensing and stellar dynamics or stellar population synthesis models have made it possible to characterize numerous fundamental properties of the galaxies as well as dark matter halos and subhalos with unprecedented robustness and accuracy. Here we demonstrate the usefulness and accuracy of strong lensing as a probe for characterising the properties of cluster members as well as dark matter halos, to show that such characterisation carried out via lensing analyses alone is as viable as those carried out through a combination of spectroscopy and lensing analyses.Our study uses focuses on the early-type galaxy cluster MACS J1149.5+2223 at redshift 0.54 in the Hubble Frontier Fields (HFF) program, where the first magnified and spatially resolved multiple images of supernova (SN) “Refsdal” and its late-type host galaxy at redshift 1.489 were detected. The Refsdal system is unique in being the first ever multiply-imaged supernova, with it’s first four images appearing in an Einstein Cross configuration around one of the cluster members in 2015. In our lensing analyses we use HST data of the multiply-imaged SN Refsdal to constrain the dynamical masses, velocity dispersions, and virial radii of individual galaxies and dark matter halos in the MACS J1149.5+2223 cluster. For our lensing models we select a sample of 300 cluster members within approximately 500 kpc from the BCG, and a set of reliable multiple images associated with 18 distinct knots in the SN host spiral galaxy, as well as multiple images of the supernova itself. Our results provide accurate measurements of the masses, velocity dispersions, and radii of the cluster’s dark matter halo as well as three chosen members galaxies, in strong agreement with those obtained by Grillo et al 2015, demonstrating the usefulness of strong lensing in characterising the properties of cluster-scale systems.

Local bladder cancer clusters in southeastern Michigan accounting for risk factors, covariates and residential mobility.

PubMed

Jacquez, Geoffrey M; Shi, Chen; Meliker, Jaymie R

2015-01-01

In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation. Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race. Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed. Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of Oakland and Ingham counties, and to the City of Jackson.

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

PubMed

Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji

2014-01-01

An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

PATTERNS OF MYCOBACTERIUM LEPRAE INFECTION IN WILD NINE-BANDED ARMADILLOS (DASYPUS NOVEMCINCTUS) IN MISSISSIPPI, USA.

PubMed

Perez-Heydrich, Carolina; Loughry, W J; Anderson, Corey Devin; Oli, Madan K

2016-07-01

The nine-banded armadillo ( Dasypus novemcinctus ) is the only known nonhuman reservoir of Mycobacterium leprae , the causative agent of Hansen's disease or leprosy. We conducted a 6-yr study on a wild population of armadillos in western Mississippi that was exposed to M. leprae to evaluate the importance of demographic and spatial risk factors on individual antibody status. We found that spatially derived covariates were not predictive of antibody status. Furthermore, analyses revealed no evidence of clustering by antibody-positive individuals. Lactating females and adult males had higher odds of being antibody positive than did nonlactating females. No juveniles or yearlings were antibody positive. Results of these analyses support the hypothesis that M. leprae infection patterns are spatially homogeneous within this armadillo population. Further research related to movement patterns, contact among individuals, antibody status, and environmental factors could help address hypotheses related to the role of environmental transmission on M. leprae infection and the mechanisms underlying the differential infection patterns among demographic groups.

Spatial cluster for clustering the influence factor of birth and death child in Bogor Regency, West Java

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Rachmawati, Ro'fah

2014-03-01

The number of birth and death child is the benchmarks to determine and monitor the health and welfare in Indonesia. It can be used to identify groups of people who have a high mortality risk. Identifying group is important to compare the characteristics of human that have high and low risk. These characteristics can be seen from the factors that influenced it. Furthermore, there are factors which influence of birth and death child, such us economic, health facility, education, and others. The influence factors of every individual are different, but there are similarities some individuals which live close together or in the close locations. It means there was spatial effect. To identify group in this research, clustering is done by spatial cluster method, which is view to considering the influence of the location or the relationship between locations. One of spatial cluster method is Spatial 'K'luster Analysis by Tree Edge Removal (SKATER). The research was conducted in Bogor Regency, West Java. The goal was to get a cluster of districts based on the factors that influence birth and death child. SKATER build four number of cluster respectively consists of 26, 7, 2, and 5 districts. SKATER has good performance for clustering which include spatial effect. If it compare by other cluster method, Kmeans has good performance by MANOVA test.

Privacy Protection Versus Cluster Detection in Spatial Epidemiology

PubMed Central

Olson, Karen L.; Grannis, Shaun J.; Mandl, Kenneth D.

2006-01-01

Objectives. Patient data that includes precise locations can reveal patients’ identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. Methods. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. Results. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. Conclusions. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies. PMID:17018828

Substructures in Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Lehodey, Brigitte Tome

2000-01-01

This dissertation presents two methods for the detection of substructures in clusters of galaxies and the results of their application to a group of four clusters. In chapters 2 and 3, we remember the main properties of clusters of galaxies and give the definition of substructures. We also try to show why the study of substructures in clusters of galaxies is so important for Cosmology. Chapters 4 and 5 describe these two methods, the first one, the adaptive Kernel, is applied to the study of the spatial and kinematical distribution of the cluster galaxies. The second one, the MVM (Multiscale Vision Model), is applied to analyse the cluster diffuse X-ray emission, i.e., the intracluster gas distribution. At the end of these two chapters, we also present the results of the application of these methods to our sample of clusters. In chapter 6, we draw the conclusions from the comparison of the results we obtain with each method. In the last chapter, we present the main conclusions of this work trying to point out possible developments. We close with two appendices in which we detail some questions raised in this work not directly linked to the problem of substructures detection.

A high-significance measurement of correlation between unresolved IRAS sources and optically-selected galaxy clusters

NASA Astrophysics Data System (ADS)

Hincks, Adam D.; Hajian, Amir; Addison, Graeme E.

2013-05-01

We cross-correlate the 100 μm Improved Reprocessing of the IRAS Survey (IRIS) map and galaxy clusters at 0.1 < z < 0.3 in the maxBCG catalogue taken from the Sloan Digital Sky Survey, measuring an angular cross-power spectrum over multipole moments 150 < l < 3000 at a total significance of over 40σ. The cross-spectrum, which arises from the spatial correlation between unresolved dusty galaxies that make up the cosmic infrared background (CIB) in the IRIS map and the galaxy clusters, is well-fit by a single power law with an index of -1.28±0.12, similar to the clustering of unresolved galaxies from cross-correlating far-infrared and submillimetre maps at longer wavelengths. Using a recent, phenomenological model for the spectral and clustering properties of the IRIS galaxies, we constrain the large-scale bias of the maxBCG clusters to be 2.6±1.4, consistent with existing analyses of the real-space cluster correlation function. The success of our method suggests that future CIB-optical cross-correlations using Planck and Herschel data will significantly improve our understanding of the clustering and redshift distribution of the faint CIB sources.

Spatial, Temporal and Spatio-Temporal Patterns of Maritime Piracy

PubMed Central

Marchione, Elio

2013-01-01

Objectives: To examine patterns in the timing and location of incidents of maritime piracy to see whether, like many urban crimes, attacks cluster in space and time. Methods: Data for all incidents of maritime piracy worldwide recorded by the National Geospatial Intelligence Agency are analyzed using time-series models and methods originally developed to detect disease contagion. Results: At the macro level, analyses suggest that incidents of pirate attacks are concentrated in five subregions of the earth’s oceans and that the time series for these different subregions differ. At the micro level, analyses suggest that for the last 16 years (or more), pirate attacks appear to cluster in space and time suggesting that patterns are not static but are also not random. Conclusions: Much like other types of crime, pirate attacks cluster in space, and following an attack at one location the risk of others at the same location or nearby is temporarily elevated. The identification of such regularities has implications for the understanding of maritime piracy and for predicting the future locations of attacks. PMID:25076796

The Detection of Clusters with Spatial Heterogeneity

ERIC Educational Resources Information Center

Zhang, Zuoyi

2011-01-01

This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdispersion and Chapter 3 is devoted to the randomized permutation test for identifying local patterns of spatial association. The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection. To apply it, a…

Population Structure of Two Rabies Hosts Relative to the Known Distribution of Rabies Virus Variants in Alaska

PubMed Central

Goldsmith, Elizabeth W.; Renshaw, Benjamin; Clement, Christopher J.; Himschoot, Elizabeth A.; Hundertmark, Kris J.; Hueffer, Karsten

2015-01-01

For pathogens that infect multiple species the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We test the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (V. vulpes) in order to possibly distinguish reservoir and spill over hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found 2 groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising 2 regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the artic fox Therefore we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches. PMID:26661691

Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska.

PubMed

Goldsmith, Elizabeth W; Renshaw, Benjamin; Clement, Christopher J; Himschoot, Elizabeth A; Hundertmark, Kris J; Hueffer, Karsten

2016-02-01

For pathogens that infect multiple species, the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We tested the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (Vulpes vulpes) to possibly distinguish reservoir and spillover hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found two groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising two regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the arctic fox. Therefore, we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches. © 2015 John Wiley & Sons Ltd.

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

PubMed

Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

2007-01-01

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

Genome-Wide Transcriptome Analyses of Silicon Metabolism in Phaeodactylum tricornutum Reveal the Multilevel Regulation of Silicic Acid Transporters

PubMed Central

Sapriel, Guillaume; Quinet, Michelle; Heijde, Marc; Jourdren, Laurent; Tanty, Véronique; Luo, Guangzuo; Le Crom, Stéphane; Lopez, Pascal Jean

2009-01-01

Background Diatoms are largely responsible for production of biogenic silica in the global ocean. However, in surface seawater, Si(OH)4 can be a major limiting factor for diatom productivity. Analyzing at the global scale the genes networks involved in Si transport and metabolism is critical in order to elucidate Si biomineralization, and to understand diatoms contribution to biogeochemical cycles. Methodology/Principal Findings Using whole genome expression analyses we evaluated the transcriptional response to Si availability for the model species Phaeodactylum tricornutum. Among the differentially regulated genes we found genes involved in glutamine-nitrogen pathways, encoding putative extracellular matrix components, or involved in iron regulation. Some of these compounds may be good candidates for intracellular intermediates involved in silicic acid storage and/or intracellular transport, which are very important processes that remain mysterious in diatoms. Expression analyses and localization studies gave the first picture of the spatial distribution of a silicic acid transporter in a diatom model species, and support the existence of transcriptional and post-transcriptional regulations. Conclusions/Significance Our global analyses revealed that about one fourth of the differentially expressed genes are organized in clusters, underlying a possible evolution of P. tricornutum genome, and perhaps other pennate diatoms, toward a better optimization of its response to variable environmental stimuli. High fitness and adaptation of diatoms to various Si levels in marine environments might arise in part by global regulations from gene (expression level) to genomic (organization in clusters, dosage compensation by gene duplication), and by post-transcriptional regulation and spatial distribution of SIT proteins. PMID:19829693

Managing distance and covariate information with point-based clustering.

PubMed

Whigham, Peter A; de Graaf, Brandon; Srivastava, Rashmi; Glue, Paul

2016-09-01

Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley's K and applied to the problem of clustering with deliberate self-harm (DSH), is presented. Point-based Monte-Carlo simulation of Ripley's K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years' emergency hospital presentations (n = 136) in a New Zealand town (population ~50,000). Study area was defined by residential (housing) land parcels representing a finite set of possible point addresses. Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley's K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for covariate measures that exhibit spatial clustering, such as deprivation, are crucial when assessing point-based clustering.

Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut.

PubMed

Kéchichian, Razmig; Valette, Sébastien; Desvignes, Michel; Prost, Rémy

2013-11-01

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

Genetic diversity among air yam (Dioscorea bulbifera) varieties based on single sequence repeat markers.

PubMed

Silva, D M; Siqueira, M V B M; Carrasco, N F; Mantello, C C; Nascimento, W F; Veasey, E A

2016-05-23

Dioscorea is the largest genus in the Dioscoreaceae family, and includes a number of economically important species including the air yam, D. bulbifera L. This study aimed to develop new single sequence repeat primers and characterize the genetic diversity of local varieties that originated in several municipalities of Brazil. We developed an enriched genomic library for D. bulbifera resulting in seven primers, six of which were polymorphic, and added four polymorphic loci developed for other Dioscorea species. This resulted in 10 polymorphic primers to evaluate 42 air yam accessions. Thirty-three alleles (bands) were found, with an average of 3.3 alleles per locus. The discrimination power ranged from 0.113 to 0.834, with an average of 0.595. Both principal coordinate and cluster analyses (using the Jaccard Index) failed to clearly separate the accessions according to their origins. However, the 13 accessions from Conceição dos Ouros, Minas Gerais State were clustered above zero on the principal coordinate 2 axis, and were also clustered into one subgroup in the cluster analysis. Accessions from Ubatuba, São Paulo State were clustered below zero on the same principal coordinate 2 axis, except for one accession, although they were scattered in several subgroups in the cluster analysis. Therefore, we found little spatial structure in the accessions, although those from Conceição dos Ouros and Ubatuba exhibited some spatial structure, and that there is a considerable level of genetic diversity in D. bulbifera maintained by traditional farmers in Brazil.

A spatial scan statistic for compound Poisson data.

PubMed

Rosychuk, Rhonda J; Chang, Hsing-Ming

2013-12-20

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

Innovating Big Data Computing Geoprocessing for Analysis of Engineered-Natural Systems

NASA Astrophysics Data System (ADS)

Rose, K.; Baker, V.; Bauer, J. R.; Vasylkivska, V.

2016-12-01

Big data computing and analytical techniques offer opportunities to improve predictions about subsurface systems while quantifying and characterizing associated uncertainties from these analyses. Spatial analysis, big data and otherwise, of subsurface natural and engineered systems are based on variable resolution, discontinuous, and often point-driven data to represent continuous phenomena. We will present examples from two spatio-temporal methods that have been adapted for use with big datasets and big data geo-processing capabilities. The first approach uses regional earthquake data to evaluate spatio-temporal trends associated with natural and induced seismicity. The second algorithm, the Variable Grid Method (VGM), is a flexible approach that presents spatial trends and patterns, such as those resulting from interpolation methods, while simultaneously visualizing and quantifying uncertainty in the underlying spatial datasets. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analyses to efficiently consume and utilize large geospatial data in these custom analytical algorithms through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom `Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations.

Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland.

PubMed

Zhang, Chaosheng; Luo, Lin; Xu, Weilin; Ledwith, Valerie

2008-07-15

Pollution hotspots in urban soils need to be identified for better environmental management. It is important to know if there are hotspots and if the hotspots are statistically significant. In this study identification of pollution hotspots was investigated using Pb concentrations in urban soils of Galway City in Ireland as an example, and the influencing factors on results of hotspot identification were investigated. The index of local Moran's I is a useful tool for identifying pollution hotspots of Pb pollution in urban soils, and for classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values. Compared with the results for the positively skewed raw data, the transformed data and data with extreme values excluded revealed a larger area for the high value spatial clusters in the city centre. While it is hard to decide the best way of using this index, it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. GIS mapping can be applied to help evaluate the results via visualization of the spatial patterns. Meanwhile, selected pollution hotspots (extreme values) in this study were confirmed by re-analyses and re-sampling.

Cancer Cluster Investigations: Review of the Past and Proposals for the Future

PubMed Central

Goodman, Michael; LaKind, Judy S.; Fagliano, Jerald A.; Lash, Timothy L.; Wiemels, Joseph L.; Winn, Deborah M.; Patel, Chirag; Van Eenwyk, Juliet; Kohler, Betsy A.; Schisterman, Enrique F.; Albert, Paul; Mattison, Donald R.

2014-01-01

Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and “omics” approaches. PMID:24477211

Spatial clustering and local risk of leprosy in São Paulo, Brazil.

PubMed

Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre

2017-02-01

Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

PubMed

Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

2018-01-15

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fractal analysis of earthquake swarms of Vogtland/NW-Bohemia intraplate seismicity

NASA Astrophysics Data System (ADS)

Mittag, Reinhard J.

2003-03-01

The special type of intraplate microseismicity with swarm-like occurrence of earthquakes within the Vogtland/NW-Bohemian Region is analysed to reveal the nature and the origin of the seismogenic regime. The long-term data set of continuous seismic monitoring since 1962, including more than 26000 events within a range of about 5 units of local magnitude, provides an unique database for statistical investigations. Most earthquakes occur in narrow hypocentral volumes (clusters) within the lower part of the upper crust, but also single event occurrence outside of spatial clusters is observed. Temporal distribution of events is concentrated in clusters (swarms), which last some days until few month in dependence of intensity. Since 1962 three strong swarms occurred (1962, 1985/86, 2000), including two seismic cycles. Spatial clusters are distributed along a fault system of regional extension (Leipzig-Regensburger Störung), which is supposed to act as the joint tectonic fracture zone for the whole seismogenic region. Seismicity is analysed by fractal analysis, suggesting a unifractal behaviour of seismicity and uniform character of seismotectonic regime for the whole region. A tendency of decreasing fractal dimension values is observed for temporal distribution of earthquakes, indicating an increasing degree of temporal clustering from swarm to swarm. Following the idea of earthquake triggering by magma intrusions and related fluid and gas release into the tectonically pre-stressed parts of the crust, a steady increased intensity of intrusion and/or fluid and gas release might account for that observation. Additionally, seismic parameters for Vogtland/NW-Bohemia intraplate seismicity are compared with an adequate data set of mining-induced seismicity in a nearby mine of Lubin/Poland and with synthetic data sets to evaluate parameter estimation. Due to different seismogenic regime of tectonic and induced seismicity, significant differences between b-values and temporal dimension values are observed. Most significant for intraplate seismicity are relatively low fractal dimension values for temporal distribution. That observation reflects the strong degree of temporal earthquake clustering, which might explain the episodic character of earthquake swarms and support the idea of push-like triggering of earthquake avalanches by intruding magma.

Spatial clustering of mental disorders and associated characteristics of the neighbourhood context in Malmö, Sweden, in 2001

PubMed Central

Chaix, Basile; Leyland, Alastair H; Sabel, Clive E; Chauvin, Pierre; Råstam, Lennart; Kristersson, Håkan; Merlo, Juan

2006-01-01

Study objective Previous research provides preliminary evidence of spatial variations of mental disorders and associations between neighbourhood social context and mental health. This study expands past literature by (1) using spatial techniques, rather than multilevel models, to compare the spatial distributions of two groups of mental disorders (that is, disorders due to psychoactive substance use, and neurotic, stress related, and somatoform disorders); and (2) investigating the independent impact of contextual deprivation and neighbourhood social disorganisation on mental health, while assessing both the magnitude and the spatial scale of these effects. Design Using different spatial techniques, the study investigated mental disorders due to psychoactive substance use, and neurotic disorders. Participants All 89 285 persons aged 40–69 years residing in Malmö, Sweden, in 2001, geolocated to their place of residence. Main results The spatial scan statistic identified a large cluster of increased prevalence in a similar location for the two mental disorders in the northern part of Malmö. However, hierarchical geostatistical models showed that the two groups of disorders exhibited a different spatial distribution, in terms of both magnitude and spatial scale. Mental disorders due to substance consumption showed larger neighbourhood variations, and varied in space on a larger scale, than neurotic disorders. After adjustment for individual factors, the risk of substance related disorders increased with neighbourhood deprivation and neighbourhood social disorganisation. The risk of neurotic disorders only increased with contextual deprivation. Measuring contextual factors across continuous space, it was found that these associations operated on a local scale. Conclusions Taking space into account in the analyses permitted deeper insight into the contextual determinants of mental disorders. PMID:16614334

Grassland management regimens reduce small-scale heterogeneity and species diversity of beta-proteobacterial ammonia pxidizer populations.

PubMed

Webster, Gordon; Embley, T Martin; Prosser, James I

2002-01-01

The impact of soil management practices on ammonia oxidizer diversity and spatial heterogeneity was determined in improved (addition of N fertilizer), unimproved (no additions), and semi-improved (intermediate management) grassland pastures at the Sourhope Research Station in Scotland. Ammonia oxidizer diversity within each grassland soil was assessed by PCR amplification of microbial community DNA with both ammonia oxidizer-specific, 16S rRNA gene (rDNA) and functional, amoA, gene primers. PCR products were analysed by denaturing gradient gel electrophoresis, phylogenetic analysis of partial 16S rDNA and amoA sequences, and hybridization with ammonia oxidizer-specific oligonucleotide probes. Ammonia oxidizer populations in unimproved soils were more diverse than those in improved soils and were dominated by organisms representing Nitrosospira clusters 1 and 3 and Nitrosomonas cluster 7 (closely related phylogenetically to Nitrosomonas europaea). Improved soils were only dominated by Nitrosospira cluster 3 and Nitrosomonas cluster 7. These differences were also reflected in functional gene (amoA) diversity, with amoA gene sequences of both Nitrosomonas and Nitrosospira species detected. Replicate 0.5-g samples of unimproved soil demonstrated significant spatial heterogeneity in 16S rDNA-defined ammonia oxidizer clusters, which was reflected in heterogeneity in ammonium concentration and pH. Heterogeneity in soil characteristics and ammonia oxidizer diversity were lower in improved soils. The results therefore demonstrate significant effects of soil management on diversity and heterogeneity of ammonia oxidizer populations that are related to similar changes in relevant soil characteristics.

Antimicrobial susceptibility of Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from a diagnostic veterinary laboratory and recommendations for a surveillance system

PubMed Central

Glass-Kaastra, Shiona K.; Pearl, David L.; Reid-Smith, Richard J.; McEwen, Beverly; Slavic, Durda; McEwen, Scott A.; Fairles, Jim

2014-01-01

Antimicrobial susceptibility data on Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from Ontario swine (January 1998 to October 2010) were acquired from a comprehensive diagnostic veterinary laboratory in Ontario, Canada. In relation to the possible development of a surveillance system for antimicrobial resistance, data were assessed for ease of management, completeness, consistency, and applicability for temporal and spatial statistical analyses. Limited farm location data precluded spatial analyses and missing demographic data limited their use as predictors within multivariable statistical models. Changes in the standard panel of antimicrobials used for susceptibility testing reduced the number of antimicrobials available for temporal analyses. Data consistency and quality could improve over time in this and similar diagnostic laboratory settings by encouraging complete reporting with sample submission and by modifying database systems to limit free-text data entry. These changes could make more statistical methods available for disease surveillance and cluster detection. PMID:24688133

Antimicrobial susceptibility of Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from a diagnostic veterinary laboratory and recommendations for a surveillance system.

PubMed

Glass-Kaastra, Shiona K; Pearl, David L; Reid-Smith, Richard J; McEwen, Beverly; Slavic, Durda; McEwen, Scott A; Fairles, Jim

2014-04-01

Antimicrobial susceptibility data on Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from Ontario swine (January 1998 to October 2010) were acquired from a comprehensive diagnostic veterinary laboratory in Ontario, Canada. In relation to the possible development of a surveillance system for antimicrobial resistance, data were assessed for ease of management, completeness, consistency, and applicability for temporal and spatial statistical analyses. Limited farm location data precluded spatial analyses and missing demographic data limited their use as predictors within multivariable statistical models. Changes in the standard panel of antimicrobials used for susceptibility testing reduced the number of antimicrobials available for temporal analyses. Data consistency and quality could improve over time in this and similar diagnostic laboratory settings by encouraging complete reporting with sample submission and by modifying database systems to limit free-text data entry. These changes could make more statistical methods available for disease surveillance and cluster detection.

Small-area spatiotemporal analysis of heatwave impacts on elderly mortality in Paris: A cluster analysis approach.

PubMed

Benmarhnia, Tarik; Kihal-Talantikite, Wahida; Ragettli, Martina S; Deguen, Séverine

2017-08-15

Heat-waves have a substantial public health burden. Understanding spatial heterogeneity at a fine spatial scale in relation to heat and related mortality is central to target interventions towards vulnerable communities. To determine the spatial variability of heat-wave-related mortality risk among elderly in Paris, France at the census block level. We also aimed to assess area-level social and environmental determinants of high mortality risk within Paris. We used daily mortality data from 2004 to 2009 among people aged >65 at the French census block level within Paris. We used two heat wave days' definitions that were compared to non-heat wave days. A Bernoulli cluster analysis method was applied to identify high risk clusters of mortality during heat waves. We performed random effects meta-regression analyses to investigate factors associated with the magnitude of the mortality risk. The spatial approach revealed a spatial aggregation of death cases during heat wave days. We found that small scale chronic PM 10 exposure was associated with a 0.02 (95% CI: 0.001; 0.045) increase of the risk of dying during a heat wave episode. We also found a positive association with the percentage of foreigners and the percentage of labor force, while the proportion of elderly people living in the neighborhood was negatively associated. We also found that green space density had a protective effect and inversely that the density of constructed feature increased the risk of dying during a heat wave episode. We showed that a spatial variation in terms of heat-related vulnerability exists within Paris and that it can be explained by some contextual factors. This study can be useful for designing interventions targeting more vulnerable areas and reduce the burden of heat waves. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

PubMed

Cook, Andrea J; Gold, Diane R; Li, Yi

2009-10-01

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

Spatial and temporal patterns in preterm birth in the United States.

PubMed

Byrnes, John; Mahoney, Richard; Quaintance, Cele; Gould, Jeffrey B; Carmichael, Suzan; Shaw, Gary M; Showen, Amy; Phibbs, Ciaran; Stevenson, David K; Wise, Paul H

2015-06-01

Despite years of research, the etiologies of preterm birth remain unclear. In order to help generate new research hypotheses, this study explored spatial and temporal patterns of preterm birth in a large, total-population dataset. Data on 145 million US births in 3,000 counties from the Natality Files of the National Center for Health Statistics for 1971-2011 were examined. State trends in early (<34 wk) and late (34-36 wk) preterm birth rates were compared. K-means cluster analyses were conducted to identify gestational age distribution patterns for all US counties over time. A weak association was observed between state trends in <34 wk birth rates and the initial absolute <34 wk birth rate. Significant associations were observed between trends in <34 wk and 34-36 wk birth rates and between white and African American <34 wk births. Periodicity was observed in county-level trends in <34 wk birth rates. Cluster analyses identified periods of significant heterogeneity and homogeneity in gestational age distributional trends for US counties. The observed geographic and temporal patterns suggest periodicity and complex, shared influences among preterm birth rates in the United States. These patterns could provide insight into promising hypotheses for further research.

Genetic Structure of Bluefin Tuna in the Mediterranean Sea Correlates with Environmental Variables

PubMed Central

Riccioni, Giulia; Stagioni, Marco; Landi, Monica; Ferrara, Giorgia; Barbujani, Guido; Tinti, Fausto

2013-01-01

Background Atlantic Bluefin Tuna (ABFT) shows complex demography and ecological variation in the Mediterranean Sea. Genetic surveys have detected significant, although weak, signals of population structuring; catch series analyses and tagging programs identified complex ABFT spatial dynamics and migration patterns. Here, we tested the hypothesis that the genetic structure of the ABFT in the Mediterranean is correlated with mean surface temperature and salinity. Methodology We used six samples collected from Western and Central Mediterranean integrated with a new sample collected from the recently identified easternmost reproductive area of Levantine Sea. To assess population structure in the Mediterranean we used a multidisciplinary framework combining classical population genetics, spatial and Bayesian clustering methods and a multivariate approach based on factor analysis. Conclusions FST analysis and Bayesian clustering methods detected several subpopulations in the Mediterranean, a result also supported by multivariate analyses. In addition, we identified significant correlations of genetic diversity with mean salinity and surface temperature values revealing that ABFT is genetically structured along two environmental gradients. These results suggest that a preference for some spawning habitat conditions could contribute to shape ABFT genetic structuring in the Mediterranean. However, further studies should be performed to assess to what extent ABFT spawning behaviour in the Mediterranean Sea can be affected by environmental variation. PMID:24260341

The Role of Viral Introductions in Sustaining Community-Based HIV Epidemics in Rural Uganda: Evidence from Spatial Clustering, Phylogenetics, and Egocentric Transmission Models

PubMed Central

Grabowski, Mary K.; Lessler, Justin; Redd, Andrew D.; Kagaayi, Joseph; Laeyendecker, Oliver; Ndyanabo, Anthony; Nelson, Martha I.; Cummings, Derek A. T.; Bwanika, John Baptiste; Mueller, Amy C.; Reynolds, Steven J.; Munshaw, Supriya; Ray, Stuart C.; Lutalo, Tom; Manucci, Jordyn; Tobian, Aaron A. R.; Chang, Larry W.; Beyrer, Chris; Jennings, Jacky M.; Nalugoda, Fred; Serwadda, David; Wawer, Maria J.; Quinn, Thomas C.; Gray, Ronald H.

2014-01-01

Background It is often assumed that local sexual networks play a dominant role in HIV spread in sub-Saharan Africa. The aim of this study was to determine the extent to which continued HIV transmission in rural communities—home to two-thirds of the African population—is driven by intra-community sexual networks versus viral introductions from outside of communities. Methods and Findings We analyzed the spatial dynamics of HIV transmission in rural Rakai District, Uganda, using data from a cohort of 14,594 individuals within 46 communities. We applied spatial clustering statistics, viral phylogenetics, and probabilistic transmission models to quantify the relative contribution of viral introductions into communities versus community- and household-based transmission to HIV incidence. Individuals living in households with HIV-incident (n = 189) or HIV-prevalent (n = 1,597) persons were 3.2 (95% CI: 2.7–3.7) times more likely to be HIV infected themselves compared to the population in general, but spatial clustering outside of households was relatively weak and was confined to distances <500 m. Phylogenetic analyses of gag and env genes suggest that chains of transmission frequently cross community boundaries. A total of 95 phylogenetic clusters were identified, of which 44% (42/95) were two individuals sharing a household. Among the remaining clusters, 72% (38/53) crossed community boundaries. Using the locations of self-reported sexual partners, we estimate that 39% (95% CI: 34%–42%) of new viral transmissions occur within stable household partnerships, and that among those infected by extra-household sexual partners, 62% (95% CI: 55%–70%) are infected by sexual partners from outside their community. These results rely on the representativeness of the sample and the quality of self-reported partnership data and may not reflect HIV transmission patterns outside of Rakai. Conclusions Our findings suggest that HIV introductions into communities are common and account for a significant proportion of new HIV infections acquired outside of households in rural Uganda, though the extent to which this is true elsewhere in Africa remains unknown. Our results also suggest that HIV prevention efforts should be implemented at spatial scales broader than the community and should target key populations likely responsible for introductions into communities. Please see later in the article for the Editors' Summary PMID:24595023

Constraining the Mass of the Spectacular Pandora's Cluster, Abell 2744

NASA Astrophysics Data System (ADS)

Carrasco, Rodrigo; Frye, Brenda; Coe, Dan; Dupke, Renato; Merten, Julian; Sodre, Laerte; Massey, Richard; Braglia, Filberto; Cypriano, Eduardo; Zitrin, Adi; Krick, Jessica; Benitez, Narciso

2011-08-01

Violent cluster mergers provide a unique opportunity to study the interplay between dark matter (DM) and ICM and to set constraints on the nature of DM. In particular, cluster mergers near first core passage allow us to ``see'' DM by comparing the spatial distribution of the intra-cluster gas (baryonic) to that of DM. We have recently finished a lensing analysis of the particularly interesting merging system, A2744, the Pandora cluster. We found that it is the result of a spectacular merging event, significantly more complex than the "Bullet Cluster", that produced a wide variety of new phenomenologies, among them, a Bullet, a Dark sub-cluster (no gas), a Ghost sub-cluster (no DM), which can provide fundamental insights to the physics of the ICM, and begs further observations. Our analyses revealed 34 arcs produced by strong gravitational lensing, none of which had been published to date. Spectroscopic redshifts of these arcs are essential to determine precise masses of the main merging system providing crucial information for further numerical simulations and to set stronger constraints on the DM self-interaction cross-section. Therefore we are requesting 17.2 hours on Gemini+GMOS-S, primarily to obtain spectroscopic redshifts of multiply strongly lensed arcs produced by this impressive cluster.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Macente, A.; Fusseis, F.; Menegon, L.

Reaction and deformation microfabrics provide key information to understand the thermodynamic and kinetic controls of tectono-metamorphic processes, however they are usually analysed in two dimensions, omitting important information regarding the third spatial dimension. We applied synchrotron-based X-ray microtomography to document the evolution of a pristine olivine gabbro into a deformed omphacite-garnet eclogite in four dimensions, where the 4th dimension is represented by the degree of strain. In the investigated samples, which cover a strain gradient into a shear zone from the Western Gneiss Region (Norway), we focused on the spatial transformation of garnet coronas into elongated clusters of garnets withmore » increasing strain. Our microtomographic data allowed quantification of garnet volume, shape and spatial arrangement evolution with increasing strain. We combined microtomographic observations with light microscope- and backscatter electron images as well as electron microprobe- (EMPA) and electron backscatter diffraction (EBSD) analysis to correlate mineral composition and orientation data with the X-ray absorption signal of the same mineral grains. With increasing deformation, the garnet volume almost triples. In the low strain domain, garnets form a well interconnected large garnet aggregate that develops throughout the entire Page 1 of 52 sample. We also observed that garnet coronas in the gabbros never completely encapsulate olivine grains. In the most highly deformed eclogites, the oblate shapes of garnet clusters reflect a deformational origin of the microfabrics. We interpret the aligned garnet aggregates to direct synkinematic fluid flow and consequently influence the transport of dissolved chemical components. EBSD analyses reveal that garnet show a near-random crystal preferred orientation that testifies no evidence for crystal plasticity. There is, however evidence for minor fracturing, neo-nucleation and overgrowth. Microprobe chemical analysis revealed that garnet compositions progressively equilibrate to eclogite facies, becoming more almandine-rich. We interpret these observations as pointing to a mechanical disintegration of the garnet coronas during strain localisation, and their rearrangement into individual garnet clusters through a combination of garnet coalescence and overgrowth while the rock was deforming.« less

Probing the X-ray Emission from the Massive Star Cluster Westerlund 2

NASA Astrophysics Data System (ADS)

Lopez, Laura

2017-09-01

We propose a 300 ks Chandra ACIS-I observation of the massive star cluster Westerlund 2 (Wd2). This region is teeming with high-energy emission from a variety of sources: colliding wind binaries, OB and Wolf-Rayet stars, two young pulsars, and an unidentified source of very high-energy (VHE) gamma-rays. Our Chandra program is designed to achieve several goals: 1) to take a complete census of Wd2 X-ray point sources and monitor variability; 2) to probe the conditions of the colliding winds in the binary WR 20a; 3) to search for an X-ray counterpart of the VHE gamma-rays; 4) to identify diffuse X-ray emission; 5) to compare results to other massive star clusters observed by Chandra. Only Chandra has the spatial resolution and sensitivity necessary for our proposed analyses.

Unexpected materials in a Rembrandt painting characterized by high spatial resolution cluster-TOF-SIMS imaging.

PubMed

Sanyova, Jana; Cersoy, Sophie; Richardin, Pascale; Laprévote, Olivier; Walter, Philippe; Brunelle, Alain

2011-02-01

The painting materials of the Portrait of Nicolaes van Bambeeck (Royal Museums of Fine Arts of Belgium, Brussels, inv. 155) painted by Rembrandt van Rijn in 1641 has been studied using high resolution cluster-TOF-SIMS imaging. In the first step, a moderate spatial resolution (2 μm) was used to characterize the layer structure and the chemical composition of each layer on account of a high mass resolution. Then, in the second step, and despite a low mass resolution, the cluster primary ion beam was focused well below 1 μm in order to reveal smaller structures in the painting sample. The study confirmed the presence of starch in the second ground layer, which is quite surprising and, at least for Rembrandt paintings, has never been reported before. TOF-SIMS also indicated the presence of proteins, which, added to the size and shape of lake particles, suggests that it was manufactured from shearings (waste of textile manufacturing) of dyed wool, used as the source of the dyestuff. The analyses have also shown various lead carboxylates, being the products of the interaction between lead white and the oil of the binding medium. These findings considerably contribute to the understanding of Rembrandt's studio practice and thus demonstrate the importance and potential of cluster-TOF-SIMS imaging in the characterization on a submicrometer scale of artist painting materials.

Spatial analysis of under-5 mortality and potential risk factors in the Basse Health and Demographic Surveillance System, the Gambia.

PubMed

Quattrochi, John; Jasseh, Momodou; Mackenzie, Grant; Castro, Marcia C

2015-07-01

To describe the spatial pattern in under-5 mortality rates in the Basse Health and Demographic Surveillance System (BHDSS) and to test for associations between under-5 deaths and biodemographic and socio-economic risk factors. Using data on child survival from 2007 to 2011 in the BHDSS, we mapped under-5 mortality by km(2) . We tested for spatial clustering of high or low death rates using Kulldorff's spatial scan statistic. Associations between child death and a variety of biodemographic and socio-economic factors were assessed with Cox proportional hazards models, and deviance residuals from the best-fitting model were tested for spatial clustering. The overall death rate among children under 5 was 0.0195 deaths per child-year. We found two spatial clusters of high death rates and one spatial cluster of low death rates; children in the two high clusters died at a rate of 0.0264 and 0.0292 deaths per child-year, while in the low cluster, the rate was 0.0144 deaths per child-year. We also found that children born to Fula mothers experienced, on average, a higher hazard of death, whereas children born in the households in the upper two quintiles of asset ownership experienced, on average, a lower hazard of death. After accounting for the spatial distribution of biodemographic and socio-economic characteristics, we found no residual spatial pattern in child mortality risk. This study demonstrates that significant inequality in under-5 death rates can occur within a relatively small area (1100 km(2) ). Risks of under-5 mortality were associated with mother's ethnicity and household wealth. If high mortality clusters persist, then equity concerns may require additional public health efforts in those areas. © 2015 John Wiley & Sons Ltd.

[A spatially explicit analysis of traffic accidents involving pedestrians and cyclists in Berlin].

PubMed

Lakes, Tobia

2017-12-01

In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive. 1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions. Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS). While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area. In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.

Emergence of a Novel Avian Pox Disease in British Tit Species

PubMed Central

Lawson, Becki; Lachish, Shelly; Colvile, Katie M.; Durrant, Chris; Peck, Kirsi M.; Toms, Mike P.; Sheldon, Ben C.; Cunningham, Andrew A.

2012-01-01

Avian pox is a viral disease with a wide host range. In Great Britain, avian pox in birds of the Paridae family was first diagnosed in a great tit (Parus major) from south-east England in 2006. An increasing number of avian pox incidents in Paridae have been reported each year since, indicative of an emergent infection. Here, we utilise a database of opportunistic reports of garden bird mortality and morbidity to analyse spatial and temporal patterns of suspected avian pox throughout Great Britain, 2006–2010. Reports of affected Paridae (211 incidents) outnumbered reports in non-Paridae (91 incidents). The majority (90%) of Paridae incidents involved great tits. Paridae pox incidents were more likely to involve multiple individuals (77.3%) than were incidents in non-Paridae hosts (31.9%). Unlike the small wart-like lesions usually seen in non-Paridae with avian pox in Great Britain, lesions in Paridae were frequently large, often with an ulcerated surface and caseous core. Spatial analyses revealed strong clustering of suspected avian pox incidents involving Paridae hosts, but only weak, inconsistent clustering of incidents involving non-Paridae hosts. There was no spatial association between Paridae and non-Paridae incidents. We documented significant spatial spread of Paridae pox from an origin in south-east England; no spatial spread was evident for non-Paridae pox. For both host clades, there was an annual peak of reports in August/September. Sequencing of the avian poxvirus 4b core protein produced an identical viral sequence from each of 20 great tits tested from Great Britain. This sequence was identical to that from great tits from central Europe and Scandinavia. In contrast, sequence variation was evident amongst virus tested from 17 non-Paridae hosts of 5 species. Our findings show Paridae pox to be an emerging infectious disease in wild birds in Great Britain, apparently originating from viral incursion from central Europe or Scandinavia. PMID:23185231

Emergence of a novel avian pox disease in British tit species.

PubMed

Lawson, Becki; Lachish, Shelly; Colvile, Katie M; Durrant, Chris; Peck, Kirsi M; Toms, Mike P; Sheldon, Ben C; Cunningham, Andrew A

2012-01-01

Avian pox is a viral disease with a wide host range. In Great Britain, avian pox in birds of the Paridae family was first diagnosed in a great tit (Parus major) from south-east England in 2006. An increasing number of avian pox incidents in Paridae have been reported each year since, indicative of an emergent infection. Here, we utilise a database of opportunistic reports of garden bird mortality and morbidity to analyse spatial and temporal patterns of suspected avian pox throughout Great Britain, 2006-2010. Reports of affected Paridae (211 incidents) outnumbered reports in non-Paridae (91 incidents). The majority (90%) of Paridae incidents involved great tits. Paridae pox incidents were more likely to involve multiple individuals (77.3%) than were incidents in non-Paridae hosts (31.9%). Unlike the small wart-like lesions usually seen in non-Paridae with avian pox in Great Britain, lesions in Paridae were frequently large, often with an ulcerated surface and caseous core. Spatial analyses revealed strong clustering of suspected avian pox incidents involving Paridae hosts, but only weak, inconsistent clustering of incidents involving non-Paridae hosts. There was no spatial association between Paridae and non-Paridae incidents. We documented significant spatial spread of Paridae pox from an origin in south-east England; no spatial spread was evident for non-Paridae pox. For both host clades, there was an annual peak of reports in August/September. Sequencing of the avian poxvirus 4b core protein produced an identical viral sequence from each of 20 great tits tested from Great Britain. This sequence was identical to that from great tits from central Europe and Scandinavia. In contrast, sequence variation was evident amongst virus tested from 17 non-Paridae hosts of 5 species. Our findings show Paridae pox to be an emerging infectious disease in wild birds in Great Britain, apparently originating from viral incursion from central Europe or Scandinavia.

Malaria control and prevention towards elimination: data from an eleven-year surveillance in Shandong Province, China.

PubMed

Kong, Xiangli; Liu, Xin; Tu, Hong; Xu, Yan; Niu, Jianbing; Wang, Yongbin; Zhao, Changlei; Kou, Jingxuan; Feng, Jun

2017-01-31

Shandong Province experienced a declining malaria trend of local-acquired transmission, but the increasing imported malaria remains a challenge. Therefore, understanding the epidemiological characteristics of malaria and the control and elimination strategy and interventions is needed for better planning to achieve the overall elimination goal in Shandong Province. A retrospective study was conducted and all individual cases from a web-based reporting system were reviewed and analysed to explore malaria-endemic characteristics in Shandong from 2005 to 2015. Annual malaria incidence reported in 2005-2015 were geo-coded and matched to the county-level. Spatial cluster analysis was performed to evaluate any identified spatial disease clusters for statistical significance. The space-time cluster was detected with high rates through the retrospective space-time analysis scanning using the discrete Poisson model. The overall malaria incidence decreased to a low level during 2005-2015. In total, 1564 confirmed malaria cases were reported, 27.1% of which (n = 424) were indigenous cases. Most of the indigenous case (n = 339, 80.0%) occurred from June to October. However, the number and scale of imported cases have been increased but no significant difference was observed during months. Shandong is endemic for both Plasmodium vivax (n = 730) and Plasmodium falciparum (n = 674). The disease is mainly distributed in Southern (n = 710) and Eastern region (n = 424) of Shandong, such as Jinning (n = 214 [13.7%]), Weihai (n = 151 [9.7%]), and Yantai (n = 107 [6.8%]). Furthermore, the spatial cluster analysis of malaria cases from 2005 to 2015 indicated that the diseased was not randomly distributed. For indigenous cases, a total of 15 and 2 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. For imported cases, a total of 26 and 29 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. The method of spatial scan statistics identified different 13 significant spatial clusters between 2005 and 2015. The space-time clustering analysis determined that the most likely cluster included 14 and 19 counties for indigenous and imported, respectively. In order to cope with the requirements of malaria elimination phase, the surveillance system should be strengthened particularity on the frequent migration regions as well as the effective multisectoral cooperation and coordination mechanisms. Specific response packages should be tailored among different types of cities and capacity building should also be improved mainly focus on the emergence response and case management. Fund guarantees for scientific research should be maintained both during the elimination and post-elimination phase to consolidate the achievements of malaria elimination.

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Spatio-temporal pattern of schistosomiasis in Anhui Province, East China: Potential effect of the Yangtze River - Huaihe River Water Transfer Project.

PubMed

Cao, Zhi-Guo; Li, Si; Zhao, Ya-E; Wang, Tian-Ping; Bergquist, Robert; Huang, Yin-Yin; Gao, Feng-Hua; Hu, Yi; Zhang, Zhi-Jie

2018-05-09

Anhui Province has been one of typical epidemic areas of schistosomiasis in East China as a wide range of large lake and marshland regions provide an ideal environment for growth and reproduction of the intermediate snail host. With the completion of the Yangtze River-Huaihe River Water Transfer Project (YHWTP), launched by the end of 2016, the epidemic areas are expected to expand and controlling schistosomiasis remains a challenge. Based on annual surveillance data at the county level in Anhui for the period 2006-2015, spatial and temporal cluster analyses were conducted to assess the pattern of risk through spatial (Local Moran's I and flexible scan statistic) and space-time scan statistic (Kulldorff). It was found that schistosomiasis sero-prevalence was dramatically reduced and maintained at a low level. Cluster results showed that spatial extent of schistosomiasis contracted, but snail distribution remained geographically stable across the study area. Clusters, both for schistosomiasis and snail presence, were common along the Yangtze River. Considering the effect of the ongoing YHWTP on the potential spread of schistosomiasis, Zongyang County and Anqing, which will be transected by the new water-transfer route, should be given a priority for strengthened surveillance and control. Attention should also be paid to Guichi since it is close to one of the planned inlets of the YHWTP. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatio-temporal pattern of sylvatic rabies in the Sultanate of Oman, 2006-2010.

PubMed

Hussain, Muhammad Hammad; Ward, Michael P; Body, Mohammed; Al-Rawahi, Abdulmajeed; Wadir, Ali Awlad; Al-Habsi, Saif; Saqib, Muhammad; Ahmed, Mohammed Sayed; Almaawali, Mahir Gharib

2013-07-01

Rabies was first reported in the Sultanate of Oman is 1990. We analysed passive surveillance data (444 samples) collected and reported between 2006 and 2010. During this period, between 45 and 75% of samples submitted from suspect animals were subsequently confirmed (fluorescent antibody test, histopathology and reverse transcription PCR) as rabies cases. Overall, 63% of submitted samples were confirmed as rabies cases. The spatial distribution of species-specific cases were similar (centred in north-central Oman with a northeast-southwest distribution), although fox cases had a wider distribution and an east-west orientation. Clustering of cases was detected using interpolation, local spatial autocorrelation and scan statistical analysis. Several local government areas (wilayats) in north-central Oman were identified where higher than expected numbers of laboratory-confirmed rabies cases were reported. For fox rabies, more clusters (local spatial autocorrelation analysis) and a larger clustered area (scan statistical analysis) were detected. In Oman, monthly reports of fox rabies cases were highly correlated (rSP>0.5) with reports of camel, cattle, sheep and goat rabies. The best-fitting ARIMA model included a seasonality component. Fox rabies cases reported 6 months previously best explained rabies reported cases in other animal species. Despite likely reporting bias, results suggest that rabies exists as a sylvatic cycle of transmission in Oman and an opportunity still exists to prevent establishment of dog-mediated rabies. Copyright © 2013 Elsevier B.V. All rights reserved.

Changing patterns of spatial clustering of schistosomiasis in Southwest China between 1999-2001 and 2007-2008: assessing progress toward eradication after the World Bank Loan Project.

PubMed

Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Cohen, Ted; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

2014-01-03

We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999-2001 and again in 2007-2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin's Local Moran's I test and Kulldorff's spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China.

Rolling epidemic of Legionnaires' disease outbreaks in small geographic areas.

PubMed

MacIntyre, C Raina; Dyda, Amalie; Bui, Chau Minh; Chughtai, Abrar Ahmad

2018-03-21

Legionnaires' disease (LD) is reported from many parts of the world, mostly linked to drinking water sources or cooling towers. We reviewed two unusual rolling outbreaks in Sydney and New York, each clustered in time and space. Data on these outbreaks were collected from public sources and compared to previous outbreaks in Australia and the US. While recurrent outbreaks of LD over time linked to an identified single source have been described, multiple unrelated outbreaks clustered in time and geography have not been previously described. We describe unusual geographic and temporal clustering of Legionella outbreaks in two cities, each of which experienced multiple different outbreaks within a small geographic area and within a short timeframe. The explanation for this temporal and spatial clustering of LD outbreaks in two cities is not clear, but climate variation and deteriorating water sanitation are two possible explanations. There is a need to critically analyse LD outbreaks and better understand changing trends to effectively prevent disease.

Using exploratory data analysis to identify and predict patterns of human Lyme disease case clustering within a multistate region, 2010-2014.

PubMed

Hendricks, Brian; Mark-Carew, Miguella

2017-02-01

Lyme disease is the most commonly reported vectorborne disease in the United States. The objective of our study was to identify patterns of Lyme disease reporting after multistate inclusion to mitigate potential border effects. County-level human Lyme disease surveillance data were obtained from Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia state health departments. Rate smoothing and Local Moran's I was performed to identify clusters of reporting activity and identify spatial outliers. A logistic generalized estimating equation was performed to identify significant associations in disease clustering over time. Resulting analyses identified statistically significant (P=0.05) clusters of high reporting activity and trends over time. High reporting activity aggregated near border counties in high incidence states, while low reporting aggregated near shared county borders in non-high incidence states. Findings highlight the need for exploratory surveillance approaches to describe the extent to which state level reporting affects accurate estimation of Lyme disease progression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Parallel and Scalable Clustering and Classification for Big Data in Geosciences

NASA Astrophysics Data System (ADS)

Riedel, M.

2015-12-01

Machine learning, data mining, and statistical computing are common techniques to perform analysis in earth sciences. This contribution will focus on two concrete and widely used data analytics methods suitable to analyse 'big data' in the context of geoscience use cases: clustering and classification. From the broad class of available clustering methods we focus on the density-based spatial clustering of appliactions with noise (DBSCAN) algorithm that enables the identification of outliers or interesting anomalies. A new open source parallel and scalable DBSCAN implementation will be discussed in the light of a scientific use case that detects water mixing events in the Koljoefjords. The second technique we cover is classification, with a focus set on the support vector machines algorithm (SVMs), as one of the best out-of-the-box classification algorithm. A parallel and scalable SVM implementation will be discussed in the light of a scientific use case in the field of remote sensing with 52 different classes of land cover types.

Plasmodium falciparum sulfadoxine resistance is geographically and genetically clustered within the DR Congo

PubMed Central

Taylor, Steve M.; Antonia, Alejandro L.; Parobek, Christian M.; Juliano, Jonathan J.; Janko, Mark; Emch, Michael; Alam, Md Tauqeer; Udhayakumar, Venkatachalam; Tshefu, Antoinette K.; Meshnick, Steven R.

2013-01-01

Understanding the spatial clustering of Plasmodium falciparum populations can assist efforts to contain drug-resistant parasites and maintain the efficacy of future drugs. We sequenced single nucleotide polymorphisms (SNPs) in the dihydropteroate synthase gene (dhps) associated with sulfadoxine resistance and 5 microsatellite loci flanking dhps in order to investigate the genetic backgrounds, genetic relatedness, and geographic clustering of falciparum parasites in the Democratic Republic of the Congo (DRC). Resistant haplotypes were clustered into subpopulations: one in the northeast DRC, and the other in the balance of the DRC. Network and clonal lineage analyses of the flanking microsatellites indicate that geographically-distinct mutant dhps haplotypes derive from separate lineages. The DRC is therefore a watershed for haplotypes associated with sulfadoxine resistance. Given the importance of central Africa as a corridor for the spread of antimalarial resistance, the identification of the mechanisms of this transit can inform future policies to contain drug-resistant parasite strains. PMID:23372922

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

PubMed

Kim, Sehwi; Jung, Inkyung

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data

PubMed Central

Kim, Sehwi

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns. PMID:28753674

Large Scale Structure Studies: Final Results from a Rich Cluster Redshift Survey

NASA Astrophysics Data System (ADS)

Slinglend, K.; Batuski, D.; Haase, S.; Hill, J.

1995-12-01

The results from the COBE satellite show the existence of structure on scales on the order of 10% or more of the horizon scale of the universe. Rich clusters of galaxies from the Abell-ACO catalogs show evidence of structure on scales of 100 Mpc and hold the promise of confirming structure on the scale of the COBE result. Unfortunately, until now, redshift information has been unavailable for a large percentage of these clusters, so present knowledge of their three dimensional distribution has quite large uncertainties. Our approach in this effort has been to use the MX multifiber spectrometer on the Steward 2.3m to measure redshifts of at least ten galaxies in each of 88 Abell cluster fields with richness class R>= 1 and mag10 <= 16.8 (estimated z<= 0.12) and zero or one measured redshifts. This work has resulted in a deeper, 95% complete and more reliable sample of 3-D positions of rich clusters. The primary intent of this survey has been to constrain theoretical models for the formation of the structure we see in the universe today through 2-pt. spatial correlation function and other analyses of the large scale structures traced by these clusters. In addition, we have obtained enough redshifts per cluster to greatly improve the quality and size of the sample of reliable cluster velocity dispersions available for use in other studies of cluster properties. This new data has also allowed the construction of an updated and more reliable supercluster candidate catalog. Our efforts have resulted in effectively doubling the volume traced by these clusters. Presented here is the resulting 2-pt. spatial correlation function, as well as density plots and several other figures quantifying the large scale structure from this much deeper and complete sample. Also, with 10 or more redshifts in most of our cluster fields, we have investigated the extent of projection effects within the Abell catalog in an effort to quantify and understand how this may effect the Abell sample.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.

PubMed

Wimberly, Michael C; Midekisa, Alemayehu; Semuniguse, Paulos; Teka, Hiwot; Henebry, Geoffrey M; Chuang, Ting-Wu; Senay, Gabriel B

2012-10-01

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts. © 2012 Blackwell Publishing Ltd.

Cluster Physics with Merging Galaxy Clusters

NASA Astrophysics Data System (ADS)

Molnar, Sandor

Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard ΛCDM model, where the total density is dominated by the cosmological constant (Λ) and the matter density by cold dark matter (CDM), structure formation is hierarchical, and clusters grow mostly by merging. Mergers of two massive clusters are the most energetic events in the universe after the Big Bang, hence they provide a unique laboratory to study cluster physics. The two main mass components in clusters behave differently during collisions: the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulence are developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thus our review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clusters is to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses. New high spatial and spectral resolution ground and space based telescopes will come online in the near future. Motivated by these new opportunities, we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Hierarchical clustering using correlation metric and spatial continuity constraint

DOEpatents

Stork, Christopher L.; Brewer, Luke N.

2012-10-02

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia.

PubMed

Alemu, Kassahun; Worku, Alemayehu; Berhane, Yemane; Kumie, Abera

2014-06-06

Malaria attacks are not evenly distributed in space and time. In highland areas with low endemicity, malaria transmission is highly variable and malaria acquisition risk for individuals is unevenly distributed even within a neighbourhood. Characterizing the spatiotemporal distribution of malaria cases in high-altitude villages is necessary to prioritize the risk areas and facilitate interventions. Spatial scan statistics using the Bernoulli method were employed to identify spatial and temporal clusters of malaria in high-altitude villages. Daily malaria data were collected, using a passive surveillance system, from patients visiting local health facilities. Georeference data were collected at villages using hand-held global positioning system devices and linked to patient data. Bernoulli model using Bayesian approaches and Marcov Chain Monte Carlo (MCMC) methods were used to identify the effects of factors on spatial clusters of malaria cases. The deviance information criterion (DIC) was used to assess the goodness-of-fit of the different models. The smaller the DIC, the better the model fit. Malaria cases were clustered in both space and time in high-altitude villages. Spatial scan statistics identified a total of 56 spatial clusters of malaria in high-altitude villages. Of these, 39 were the most likely clusters (LLR = 15.62, p < 0.00001) and 17 were secondary clusters (LLR = 7.05, p < 0.03). The significant most likely temporal malaria clusters were detected between August and December (LLR = 17.87, p < 0.001). Travel away home, males and age above 15 years had statistically significant effect on malaria clusters at high-altitude villages. The study identified spatial clusters of malaria cases occurring at high elevation villages within the district. A patient who travelled away from home to a malaria-endemic area might be the most probable source of malaria infection in a high-altitude village. Malaria interventions in high altitude villages should address factors associated with malaria clustering.

A Context-sensitive Approach to Anonymizing Spatial Surveillance Data: Impact on Outbreak Detection

PubMed Central

Cassa, Christopher A.; Grannis, Shaun J.; Overhage, J. Marc; Mandl, Kenneth D.

2006-01-01

Objective: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic. Design: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density. Measurements: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured. Results: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%. Conclusion: A population-density–based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance. PMID:16357353

Space-time analysis of pneumonia hospitalisations in the Netherlands.

PubMed

Benincà, Elisa; van Boven, Michiel; Hagenaars, Thomas; van der Hoek, Wim

2017-01-01

Community acquired pneumonia is a major global public health problem. In the Netherlands there are 40,000-50,000 hospital admissions for pneumonia per year. In the large majority of these hospital admissions the etiologic agent is not determined and a real-time surveillance system is lacking. Localised and temporal increases in hospital admissions for pneumonia are therefore only detected retrospectively and the etiologic agents remain unknown. Here, we perform spatio-temporal analyses of pneumonia hospital admission data in the Netherlands. To this end, we scanned for spatial clusters on yearly and seasonal basis, and applied wavelet cluster analysis on the time series of five main regions. The pneumonia hospital admissions show strong clustering in space and time superimposed on a regular yearly cycle with high incidence in winter and low incidence in summer. Cluster analysis reveals a heterogeneous pattern, with most significant clusters occurring in the western, highly urbanised, and in the eastern, intensively farmed, part of the Netherlands. Quantitatively, the relative risk (RR) of the significant clusters for the age-standardised incidence varies from a minimum of 1.2 to a maximum of 2.2. We discuss possible underlying causes for the patterns observed, such as variations in air pollution.

Application of Scan Statistics to Detect Suicide Clusters in Australia

PubMed Central

Cheung, Yee Tak Derek; Spittal, Matthew J.; Williamson, Michelle Kate; Tung, Sui Jay; Pirkis, Jane

2013-01-01

Background Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example. Methods and Findings Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection. Conclusions These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters. PMID:23342098

Spatial pattern recognition of seismic events in South West Colombia

NASA Astrophysics Data System (ADS)

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

NASA Astrophysics Data System (ADS)

Wu, Xiaolan; Grubesic, Tony H.

2010-12-01

Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

THE SIZE DIFFERENCE BETWEEN RED AND BLUE GLOBULAR CLUSTERS IS NOT DUE TO PROJECTION EFFECTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Webb, Jeremy J.; Harris, William E.; Sills, Alison, E-mail: webbjj@mcmaster.ca

Metal-rich (red) globular clusters in massive galaxies are, on average, smaller than metal-poor (blue) globular clusters. One of the possible explanations for this phenomenon is that the two populations of clusters have different spatial distributions. We test this idea by comparing clusters observed in unusually deep, high signal-to-noise images of M87 with a simulated globular cluster population in which the red and blue clusters have different spatial distributions, matching the observations. We compare the overall distribution of cluster effective radii as well as the relationship between effective radius and galactocentric distance for both the observed and simulated red and bluemore » sub-populations. We find that the different spatial distributions does not produce a significant size difference between the red and blue sub-populations as a whole or at a given galactocentric distance. These results suggest that the size difference between red and blue globular clusters is likely due to differences during formation or later evolution.« less

Cosmology with void-galaxy correlations.

PubMed

Hamaus, Nico; Wandelt, Benjamin D; Sutter, P M; Lavaux, Guilhem; Warren, Michael S

2014-01-31

Galaxy bias, the unknown relationship between the clustering of galaxies and the underlying dark matter density field is a major hurdle for cosmological inference from large-scale structure. While traditional analyses focus on the absolute clustering amplitude of high-density regions mapped out by galaxy surveys, we propose a relative measurement that compares those to the underdense regions, cosmic voids. On the basis of realistic mock catalogs we demonstrate that cross correlating galaxies and voids opens up the possibility to calibrate galaxy bias and to define a static ruler thanks to the observable geometric nature of voids. We illustrate how the clustering of voids is related to mass compensation and show that volume-exclusion significantly reduces the degree of stochasticity in their spatial distribution. Extracting the spherically averaged distribution of galaxies inside voids from their cross correlations reveals a remarkable concordance with the mass-density profile of voids.

GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.

PubMed

Schulz, Tizian; Stoye, Jens; Doerr, Daniel

2018-05-08

Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.

Molecular detection of influenza A(H1N1)pdm09 viruses with M genes from human pandemic strains among Nigerian pigs, 2013-2015: implications and associated risk factors.

PubMed

Adeola, O A; Olugasa, B O; Emikpe, B O

2017-12-01

In the post-pandemic period, influenza A(H1N1)pdm09 virus has been detected in swine populations in different parts of the world. This study was conducted to determine the presence and spatial patterns of this human pandemic virus among Nigerian pigs and identify associated risk factors. Using a two-stage stratified random sampling method, nasal swab specimens were obtained from pigs in Ibadan, Nigeria during the 2013-2014 and 2014-2015 influenza seasons, and the virus was detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Purified RT-PCR products were sequenced in both directions, and sequences were aligned using MUSCLE. Phylogenetic analysis was conducted in MEGA6. Purely spatial scan statistics and a spatial lag regression model were used to identify spatial clusters and associated risk factors. The virus was detected in both seasons, with an overall prevalence of 8·7%. Phylogenetic analyses revealed that the M genes were similar to those of pandemic strains which circulated in humans prior to and during the study. Cluster analysis revealed a significant primary spatial cluster (RR = 4·71, LLR = 5·66, P = 0·0046), while 'hours spent with pigs (R 2 = 0·90, P = 0·0018)' and 'hours spent with pigs from different farms (R 2 = 0·91, P = 0·0001)' were identified as significant risk factors (P < 0·05). These findings reveal that there is considerable risk of transmission of the pandemic virus, either directly from pig handlers or through fomites, to swine herds in Ibadan, Nigeria. Active circulation of the virus among Nigerian pigs could enhance its reassortment with endemic swine influenza viruses. Campaigns for adoption of biosecurity measures in West African piggeries and abattoirs should be introduced and sustained in order to prevent the emergence of a new influenza epicentre in the sub-region.

Spatio-Temporal Analysis of Smear-Positive Tuberculosis in the Sidama Zone, Southern Ethiopia

PubMed Central

Dangisso, Mesay Hailu; Datiko, Daniel Gemechu; Lindtjørn, Bernt

2015-01-01

Background Tuberculosis (TB) is a disease of public health concern, with a varying distribution across settings depending on socio-economic status, HIV burden, availability and performance of the health system. Ethiopia is a country with a high burden of TB, with regional variations in TB case notification rates (CNRs). However, TB program reports are often compiled and reported at higher administrative units that do not show the burden at lower units, so there is limited information about the spatial distribution of the disease. We therefore aim to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 10 years in the Sidama Zone in southern Ethiopia. Methods A retrospective space–time and spatial analysis were carried out at the kebele level (the lowest administrative unit within a district) to identify spatial and space-time clusters of smear-positive pulmonary TB (PTB). Scan statistics, Global Moran’s I, and Getis and Ordi (Gi*) statistics were all used to help analyze the spatial distribution and clusters of the disease across settings. Results A total of 22,545 smear-positive PTB cases notified over 10 years were used for spatial analysis. In a purely spatial analysis, we identified the most likely cluster of smear-positive PTB in 192 kebeles in eight districts (RR= 2, p<0.001), with 12,155 observed and 8,668 expected cases. The Gi* statistic also identified the clusters in the same areas, and the spatial clusters showed stability in most areas in each year during the study period. The space-time analysis also detected the most likely cluster in 193 kebeles in the same eight districts (RR= 1.92, p<0.001), with 7,584 observed and 4,738 expected cases in 2003-2012. Conclusion The study found variations in CNRs and significant spatio-temporal clusters of smear-positive PTB in the Sidama Zone. The findings can be used to guide TB control programs to devise effective TB control strategies for the geographic areas characterized by the highest CNRs. Further studies are required to understand the factors associated with clustering based on individual level locations and investigation of cases. PMID:26030162

Spatio-Temporal Structure, Path Characteristics, and Perceptual Grouping in Immediate Serial Spatial Recall

PubMed Central

De Lillo, Carlo; Kirby, Melissa; Poole, Daniel

2016-01-01

Immediate serial spatial recall measures the ability to retain sequences of locations in short-term memory and is considered the spatial equivalent of digit span. It is tested by requiring participants to reproduce sequences of movements performed by an experimenter or displayed on a monitor. Different organizational factors dramatically affect serial spatial recall but they are often confounded or underspecified. Untangling them is crucial for the characterization of working-memory models and for establishing the contribution of structure and memory capacity to spatial span. We report five experiments assessing the relative role and independence of factors that have been reported in the literature. Experiment 1 disentangled the effects of spatial clustering and path-length by manipulating the distance of items displayed on a touchscreen monitor. Long-path sequences segregated by spatial clusters were compared with short-path sequences not segregated by clusters. Recall was more accurate for sequences segregated by clusters independently from path-length. Experiment 2 featured conditions where temporal pauses were introduced between or within cluster boundaries during the presentation of sequences with the same paths. Thus, the temporal structure of the sequences was either consistent or inconsistent with a hierarchical representation based on segmentation by spatial clusters but the effect of structure could not be confounded with effects of path-characteristics. Pauses at cluster boundaries yielded more accurate recall, as predicted by a hierarchical model. In Experiment 3, the systematic manipulation of sequence structure, path-length, and presence of path-crossings of sequences showed that structure explained most of the variance, followed by the presence/absence of path-crossings, and path-length. Experiments 4 and 5 replicated the results of the previous experiments in immersive virtual reality navigation tasks where the viewpoint of the observer changed dynamically during encoding and recall. This suggested that the effects of structure in spatial span are not dependent on perceptual grouping processes induced by the aerial view of the stimulus array typically afforded by spatial recall tasks. These results demonstrate the independence of coding strategies based on structure from effects of path characteristics and perceptual grouping in immediate serial spatial recall. PMID:27891101

Data-driven inference for the spatial scan statistic.

PubMed

Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

2011-08-02

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Spatial heterogeneity of type I error for local cluster detection tests

PubMed Central

2014-01-01

Background Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs’ type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect. Methods A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff’s spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect. Results The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated. Conclusions In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance. PMID:24885343

Spatiotemporal Analysis of the Ebola Hemorrhagic Fever in West Africa in 2014

NASA Astrophysics Data System (ADS)

Xu, M.; Cao, C. X.; Guo, H. F.

2017-09-01

Ebola hemorrhagic fever (EHF) is an acute hemorrhagic diseases caused by the Ebola virus, which is highly contagious. This paper aimed to explore the possible gathering area of EHF cases in West Africa in 2014, and identify endemic areas and their tendency by means of time-space analysis. We mapped distribution of EHF incidences and explored statistically significant space, time and space-time disease clusters. We utilized hotspot analysis to find the spatial clustering pattern on the basis of the actual outbreak cases. spatial-temporal cluster analysis is used to analyze the spatial or temporal distribution of agglomeration disease, examine whether its distribution is statistically significant. Local clusters were investigated using Kulldorff's scan statistic approach. The result reveals that the epidemic mainly gathered in the western part of Africa near north Atlantic with obvious regional distribution. For the current epidemic, we have found areas in high incidence of EVD by means of spatial cluster analysis.

Meteorology-induced variations in the spatial behavior of summer ozone pollution in Central California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Ling; Harley, Robert A.; Brown, Nancy J.

Cluster analysis was applied to daily 8 h ozone maxima modeled for a summer season to characterize meteorology-induced variations in the spatial distribution of ozone. Principal component analysis is employed to form a reduced dimension set to describe and interpret ozone spatial patterns. The first three principal components (PCs) capture {approx}85% of total variance, with PC1 describing a general spatial trend, and PC2 and PC3 each describing a spatial contrast. Six clusters were identified for California's San Joaquin Valley (SJV) with two low, three moderate, and one high-ozone cluster. The moderate ozone clusters are distinguished by elevated ozone levels inmore » different parts of the valley: northern, western, and eastern, respectively. The SJV ozone clusters have stronger coupling with the San Francisco Bay area (SFB) than with the Sacramento Valley (SV). Variations in ozone spatial distributions induced by anthropogenic emission changes are small relative to the overall variations in ozone amomalies observed for the whole summer. Ozone regimes identified here are mostly determined by the direct and indirect meteorological effects. Existing measurement sites are sufficiently representative to capture ozone spatial patterns in the SFB and SV, but the western side of the SJV is under-sampled.« less

D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

NASA Astrophysics Data System (ADS)

Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.

2016-06-01

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

Spatial and space-time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005-2014.

PubMed

Hundessa, Samuel H; Williams, Gail; Li, Shanshan; Guo, Jinpeng; Chen, Linping; Zhang, Wenyi; Guo, Yuming

2016-12-19

Despite the declining burden of malaria in China, the disease remains a significant public health problem with periodic outbreaks and spatial variation across the country. A better understanding of the spatial and temporal characteristics of malaria is essential for consolidating the disease control and elimination programme. This study aims to understand the spatial and spatiotemporal distribution of Plasmodium vivax and Plasmodium falciparum malaria in China during 2005-2009. Global Moran's I statistics was used to detect a spatial distribution of local P. falciparum and P. vivax malaria at the county level. Spatial and space-time scan statistics were applied to detect spatial and spatiotemporal clusters, respectively. Both P. vivax and P. falciparum malaria showed spatial autocorrelation. The most likely spatial cluster of P. vivax was detected in northern Anhui province between 2005 and 2009, and western Yunnan province between 2010 and 2014. For P. falciparum, the clusters included several counties of western Yunnan province from 2005 to 2011, Guangxi from 2012 to 2013, and Anhui in 2014. The most likely space-time clusters of P. vivax malaria and P. falciparum malaria were detected in northern Anhui province and western Yunnan province, respectively, during 2005-2009. The spatial and space-time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Contrary to P. vivax, the high-risk areas for P. falciparum malaria shifted from the west to the east of China. Further studies are required to examine the spatial changes in risk of malaria transmission and identify the underlying causes of elevated risk in the high-risk areas.

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

PubMed Central

2009-01-01

Background Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns. Methods In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender. Results Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships. Conclusions Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services. PMID:20003460

[Spatial analysis of autumn-winter type scrub typhus in Shandong province, 2006-2014].

PubMed

Yang, H; Bi, Z W; Kou, Z Q; Zheng, L; Zhao, Z T

2016-05-01

To discuss the spatial-temporal distribution and epidemic trends of autumn-winter type scrub typhus in Shandong province, and provide scientific evidence for further study for the prevention and control of the disease. The scrub typhus surveillance data during 2006-2014 were collected from Shandong Disease Reporting Information System. The data was analyzed by using software ArcGIS 9.3(ESRI Inc., Redlands, CA, USA), GeoDa 0.9.5-i and SatScan 9.1.1. The Moran' s I, log-likelihood ratio(LLR), relative risk(RR)were calculated and the incidence choropleth maps, local indicators of spatial autocorrelation cluster maps and space scaning cluster maps were drawn. A total of 4 453 scrub typhus cases were reported during 2006-2014, and the annual incidence increased with year. Among the 17 prefectures(municipality)in Shandong, 13 were affected by scrub typhus. The global Moran's I index was 0.501 5(P<0.01). The differences in local Moran' s I index among 16 prefectures were significant(P<0.01). The " high-high" clustering areas were mainly Wulian county, Lanshan district and Juxian county of Rizhao, Xintai county of Tai' an, Gangcheng and Laicheng districts of Laiwu, Yiyuan county of Zibo and Mengyin county of Linyi. Spatial scan analysis showed that an eastward moving trend of high-risk clusters and two new high-risk clusters were found in Zaozhuang in 2014. The centers of the most likely clusters were in the south central mountainous areas during 2006-2010 and in 2012, eastern hilly areas in 2011, 2013 and 2014, and the size of the clusters expanded in 2008, 2011, 2013 and 2014. One spatial-temporal cluster was detected from October 1, 2014 to November 30, 2014, the center of the cluster was in Rizhao and the radius was 222.34 kilometers. A positive spatial correlation and spatial agglomerations were found in the distribution of autumn-winter type scrub typhus in Shandong. Since 2006, the epidemic area of the disease has expanded and the number of high-risk areas has increased. Moreover, the eastward moving and periodically expanding trends of high-risk clusters were detected.

Cluster pattern analysis of energy deposition sites for the brachytherapy sources 103Pd, 125I, 192Ir, 137Cs, and 60Co.

PubMed

Villegas, Fernanda; Tilly, Nina; Bäckström, Gloria; Ahnesjö, Anders

2014-09-21

Analysing the pattern of energy depositions may help elucidate differences in the severity of radiation-induced DNA strand breakage for different radiation qualities. It is often claimed that energy deposition (ED) sites from photon radiation form a uniform random pattern, but there is indication of differences in RBE values among different photon sources used in brachytherapy. The aim of this work is to analyse the spatial patterns of EDs from 103Pd, 125I, 192Ir, 137Cs sources commonly used in brachytherapy and a 60Co source as a reference radiation. The results suggest that there is both a non-uniform and a uniform random component to the frequency distribution of distances to the nearest neighbour ED. The closest neighbouring EDs show high spatial correlation for all investigated radiation qualities, whilst the uniform random component dominates for neighbours with longer distances for the three higher mean photon energy sources (192Ir, 137Cs, and 60Co). The two lower energy photon emitters (103Pd and 125I) present a very small uniform random component. The ratio of frequencies of clusters with respect to 60Co differs up to 15% for the lower energy sources and less than 2% for the higher energy sources when the maximum distance between each pair of EDs is 2 nm. At distances relevant to DNA damage, cluster patterns can be differentiated between the lower and higher energy sources. This may be part of the explanation to the reported difference in RBE values with initial DSB yields as an endpoint for these brachytherapy sources.

Cluster pattern analysis of energy deposition sites for the brachytherapy sources 103Pd, 125I, 192Ir, 137Cs, and 60Co

NASA Astrophysics Data System (ADS)

Villegas, Fernanda; Tilly, Nina; Bäckström, Gloria; Ahnesjö, Anders

2014-09-01

Analysing the pattern of energy depositions may help elucidate differences in the severity of radiation-induced DNA strand breakage for different radiation qualities. It is often claimed that energy deposition (ED) sites from photon radiation form a uniform random pattern, but there is indication of differences in RBE values among different photon sources used in brachytherapy. The aim of this work is to analyse the spatial patterns of EDs from 103Pd, 125I, 192Ir, 137Cs sources commonly used in brachytherapy and a 60Co source as a reference radiation. The results suggest that there is both a non-uniform and a uniform random component to the frequency distribution of distances to the nearest neighbour ED. The closest neighbouring EDs show high spatial correlation for all investigated radiation qualities, whilst the uniform random component dominates for neighbours with longer distances for the three higher mean photon energy sources (192Ir, 137Cs, and 60Co). The two lower energy photon emitters (103Pd and 125I) present a very small uniform random component. The ratio of frequencies of clusters with respect to 60Co differs up to 15% for the lower energy sources and less than 2% for the higher energy sources when the maximum distance between each pair of EDs is 2 nm. At distances relevant to DNA damage, cluster patterns can be differentiated between the lower and higher energy sources. This may be part of the explanation to the reported difference in RBE values with initial DSB yields as an endpoint for these brachytherapy sources.

Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014

PubMed Central

Hu, Wen-Biao; Haque, Ubydul; Weppelmann, Thomas A.; Wang, Yong; Liu, Yun-Xi; Li, Xin-Lou; Sun, Hai-Long; Sun, Yan-Song; Clements, Archie C. A.; Li, Shen-Long; Zhang, Wen-Yi

2016-01-01

Background Scrub typhus is endemic in the Asia-Pacific region including China, and the number of reported cases has increased dramatically in the past decade. However, the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized. Objective This study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014, to detect the location of high risk spatiotemporal clusters of scrub typhus cases, and identify the potential risk factors affecting the re-emergence of the disease. Method Monthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention. Time-series analyses, spatiotemporal cluster analyses, and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence. To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted. Results During the time period between 2006 and 2014 a total of 54,558 scrub typhus cases were reported in mainland China, which grew exponentially. The majority of cases were reported each year between July and November, with peak incidence during October every year. The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest, southern, and middle-eastern part of China. Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation. Conclusions The results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country. PMID:27479297

Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014.

PubMed

Wu, Yi-Cheng; Qian, Quan; Soares Magalhaes, Ricardo J; Han, Zhi-Hai; Hu, Wen-Biao; Haque, Ubydul; Weppelmann, Thomas A; Wang, Yong; Liu, Yun-Xi; Li, Xin-Lou; Sun, Hai-Long; Sun, Yan-Song; Clements, Archie C A; Li, Shen-Long; Zhang, Wen-Yi

2016-08-01

Scrub typhus is endemic in the Asia-Pacific region including China, and the number of reported cases has increased dramatically in the past decade. However, the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized. This study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014, to detect the location of high risk spatiotemporal clusters of scrub typhus cases, and identify the potential risk factors affecting the re-emergence of the disease. Monthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention. Time-series analyses, spatiotemporal cluster analyses, and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence. To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted. During the time period between 2006 and 2014 a total of 54,558 scrub typhus cases were reported in mainland China, which grew exponentially. The majority of cases were reported each year between July and November, with peak incidence during October every year. The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest, southern, and middle-eastern part of China. Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation. The results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country.

A spatial scan statistic for multiple clusters.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2011-10-01

Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters' shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster's shadowing effect. Copyright © 2011 Elsevier Inc. All rights reserved.

Information extraction from dynamic PS-InSAR time series using machine learning

NASA Astrophysics Data System (ADS)

van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

2017-12-01

Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account, but is time consuming. Therefore, we successively apply our machine learning approach with the hypothesis testing approach in order to benefit from both the reduced computation time of the machine learning approach as from the robust quality metrics of hypothesis testing. We acknowledge support from NASA AISTNNX15AG84G (PI V. Pankratius)

Detecting spatial regimes in ecosystems

USGS Publications Warehouse

Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

2017-01-01

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

Genetic patterns of habitat fragmentation and past climate-change effects in the Mediterranean high-mountain plant Armeria caespitosa (Plumbaginaceae).

PubMed

García-Fernández, Alfredo; Iriondo, Jose M; Escudero, Adrián; Aguilar, Javier Fuertes; Feliner, Gonzalo Nieto

2013-08-01

Mountain plants are among the species most vulnerable to global warming, because of their isolation, narrow geographic distribution, and limited geographic range shifts. Stochastic and selective processes can act on the genome, modulating genetic structure and diversity. Fragmentation and historical processes also have a great influence on current genetic patterns, but the spatial and temporal contexts of these processes are poorly known. We aimed to evaluate the microevolutionary processes that may have taken place in Mediterranean high-mountain plants in response to changing historical environmental conditions. Genetic structure, diversity, and loci under selection were analyzed using AFLP markers in 17 populations distributed over the whole geographic range of Armeria caespitosa, an endemic plant that inhabits isolated mountains (Sierra de Guadarrama, Spain). Differences in altitude, geographic location, and climate conditions were considered in the analyses, because they may play an important role in selective and stochastic processes. Bayesian clustering approaches identified nine genetic groups, although some discrepancies in assignment were found between alternative analyses. Spatially explicit analyses showed a weak relationship between genetic parameters and spatial or environmental distances. However, a large proportion of outlier loci were detected, and some outliers were related to environmental variables. A. caespitosa populations exhibit spatial patterns of genetic structure that cannot be explained by the isolation-by-distance model. Shifts along the altitude gradient in response to Pleistocene climatic oscillations and environmentally mediated selective forces might explain the resulting structure and genetic diversity values found.

Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.

PubMed

Hoover, Joseph H; Coker, Eric; Barney, Yolanda; Shuey, Chris; Lewis, Johnnye

2018-08-15

Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Dynamics of spatial clustering of schistosomiasis in the Yangtze River Valley at the end of and following the World Bank Loan Project.

PubMed

Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Ward, Michael; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

2014-06-01

The 10-year (1992-2001) World Bank Loan Project (WBLP) contributed greatly to schistosomiasis control in China. However, the re-emergence of schistosomiasis in recent years challenged the long-term progress of the WBLP strategy. In order to gain insight in the long-term progress of the WBLP, the spatial pattern of the epidemic was investigated in the Yangtze River Valley between 1999-2001 and 2007-2008. Two spatial cluster methods were jointly used to identify spatial clusters of cases. The magnitude and number of clusters varied during 1999-2001. It was found that prevalence of schistosomiasis had been greatly reduced and maintained at a low level during 2007-2008, with little change. Besides, spatial clusters most frequently occurred within 16 counties in the Dongting Lake region and within 5 counties in the Poyang Lake region. These findings precisely pointed out the prior places for future public health planning and resource allocation of schistosomiasis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Evaluation of Potential LSST Spatial Indexing Strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nikolaev, S; Abdulla, G; Matzke, R

2006-10-13

The LSST requirement for producing alerts in near real-time, and the fact that generating an alert depends on knowing the history of light variations for a given sky position, both imply that the clustering information for all detections is available at any time during the survey. Therefore, any data structure describing clustering of detections in LSST needs to be continuously updated, even as new detections are arriving from the pipeline. We call this use case ''incremental clustering'', to reflect this continuous updating of clustering information. This document describes the evaluation results for several potential LSST incremental clustering strategies, using: (1)more » Neighbors table and zone optimization to store spatial clusters (a.k.a. Jim Grey's, or SDSS algorithm); (2) MySQL built-in R-tree implementation; (3) an external spatial index library which supports a query interface.« less

Emergence of increased frequency and severity of multiple infections by viruses due to spatial clustering of hosts

NASA Astrophysics Data System (ADS)

Taylor, Bradford P.; Penington, Catherine J.; Weitz, Joshua S.

2016-12-01

Multiple virus particles can infect a target host cell. Such multiple infections (MIs) have significant and varied ecological and evolutionary consequences for both virus and host populations. Yet, the in situ rates and drivers of MIs in virus-microbe systems remain largely unknown. Here, we develop an individual-based model (IBM) of virus-microbe dynamics to probe how spatial interactions drive the frequency and nature of MIs. In our IBMs, we identify increasingly spatially correlated clusters of viruses given sufficient decreases in viral movement. We also identify increasingly spatially correlated clusters of viruses and clusters of hosts given sufficient increases in viral infectivity. The emergence of clusters is associated with an increase in multiply infected hosts as compared to expectations from an analogous mean field model. We also observe long-tails in the distribution of the multiplicity of infection in contrast to mean field expectations that such events are exponentially rare. We show that increases in both the frequency and severity of MIs occur when viruses invade a cluster of uninfected microbes. We contend that population-scale enhancement of MI arises from an aggregate of invasion dynamics over a distribution of microbe cluster sizes. Our work highlights the need to consider spatially explicit interactions as a potentially key driver underlying the ecology and evolution of virus-microbe communities.

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

PubMed Central

Goovaerts, Pierre; Jacquez, Geoffrey M

2004-01-01

Background Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses. PMID:15272930

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.

PubMed

Goungounga, Juste Aristide; Gaudart, Jean; Colonna, Marc; Giorgi, Roch

2016-10-12

The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. Moran's I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only. The study found a spatial heterogeneity (p < 0.01) and an autocorrelation for prostate (EBI = 0.02; p = 0.001), lung (EBI = 0.01; p = 0.019) and bladder (EBI = 0.007; p = 0.05) cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes (p < 0.05): four in the Western and one in the Northern parts of the study area (standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively). In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods (RR >1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6). In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.

Neurons in cat V1 show significant clustering by degree of tuning

PubMed Central

Ziskind, Avi J.; Emondi, Al A.; Kurgansky, Andrei V.; Rebrik, Sergei P.

2015-01-01

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons recorded at a single site were significantly more similar in each of these properties than pairs of neurons from different recording sites. The strength of the clustering was generally modest. The percent decrease in the median difference between pairs from the same site, relative to pairs from different sites, was as follows: for different measures of orientation tuning width, 29–35% (drifting gratings) or 15–25% (flashed gratings); for DSI, 24%; and for spatial frequency tuning width measured in octaves, 8% (drifting gratings). The clusterings of all of these measures were much weaker than for preferred orientation (68% decrease) but comparable to that seen for preferred spatial frequency in response to drifting gratings (26%). For the above properties, little difference in clustering was seen between simple and complex cells. In studies of spatial frequency tuning to flashed gratings, strong clustering was seen among simple-cell pairs for tuning width (70% decrease) and preferred frequency (71% decrease), whereas no clustering was seen for simple-complex or complex-complex cell pairs. PMID:25652921

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2016-10-02

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) tomore » compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.« less

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

PubMed

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

NASA Astrophysics Data System (ADS)

Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

2018-05-01

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

Buruli ulcer disease prevalence in Benin, West Africa: Associations with land use/cover and the identification of disease clusters

USGS Publications Warehouse

Wagner, T.; Benbow, M.E.; Brenden, T.O.; Qi, J.; Johnson, R.C.

2008-01-01

Background: Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs. Results: Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin. Conclusion: Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks. ?? 2008 Wagner et al; licensee BioMed Central Ltd.

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

PubMed Central

Wagner, Tyler; Benbow, M Eric; Brenden, Travis O; Qi, Jiaguo; Johnson, R Christian

2008-01-01

Background Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs. Results Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin. Conclusion Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks. PMID:18505567

Spatio-temporal surveillance of water based infectious disease (malaria) in Rawalpindi, Pakistan using geostatistical modeling techniques.

PubMed

Ahmad, Sheikh Saeed; Aziz, Neelam; Butt, Amna; Shabbir, Rabia; Erum, Summra

2015-09-01

One of the features of medical geography that has made it so useful in health research is statistical spatial analysis, which enables the quantification and qualification of health events. The main objective of this research was to study the spatial distribution patterns of malaria in Rawalpindi district using spatial statistical techniques to identify the hot spots and the possible risk factor. Spatial statistical analyses were done in ArcGIS, and satellite images for land use classification were processed in ERDAS Imagine. Four hundred and fifty water samples were also collected from the study area to identify the presence or absence of any microbial contamination. The results of this study indicated that malaria incidence varied according to geographical location, with eco-climatic condition and showing significant positive spatial autocorrelation. Hotspots or location of clusters were identified using Getis-Ord Gi* statistic. Significant clustering of malaria incidence occurred in rural central part of the study area including Gujar Khan, Kaller Syedan, and some part of Kahuta and Rawalpindi Tehsil. Ordinary least square (OLS) regression analysis was conducted to analyze the relationship of risk factors with the disease cases. Relationship of different land cover with the disease cases indicated that malaria was more related with agriculture, low vegetation, and water class. Temporal variation of malaria cases showed significant positive association with the meteorological variables including average monthly rainfall and temperature. The results of the study further suggested that water supply and sewage system and solid waste collection system needs a serious attention to prevent any outbreak in the study area.

A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332

NASA Astrophysics Data System (ADS)

Romero, C.; McWilliam, M.; Macías-Pérez, J.-F.; Adam, R.; Ade, P.; André, P.; Aussel, H.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; Comis, B.; de Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Lagache, G.; Leclercq, S.; Lestrade, J.-F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pascale, E.; Perotto, L.; Pisano, G.; Ponthieu, N.; Revéret, V.; Ritacco, A.; Roussel, H.; Ruppin, F.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.

2018-04-01

Context. In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ) studies of galaxy clusters have become common. Whereas many previous SZ studies have parameterized the pressure profiles of galaxy clusters, non-parametric reconstructions will provide insights into the thermodynamic state of the intracluster medium. Aim. We seek to recover the non-parametric pressure profiles of the high redshift (z = 0.89) galaxy cluster CLJ 1226.9+3332 as inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments, which all probe different angular scales. Methods: Our non-parametric algorithm makes use of logarithmic interpolation, which under the assumption of ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam we derive a non-parametric pressure profile independently and find good agreement among the instruments. In particular, we find that the non-parametric profiles are consistent with a fitted generalized Navaro-Frenk-White (gNFW) profile. Given the ability of Planck to constrain the total signal, we include a prior on the integrated Compton Y parameter as determined by Planck. Results: For a given instrument, constraints on the pressure profile diminish rapidly beyond the field of view. The overlap in spatial scales probed by these four datasets is therefore critical in checking for consistency between instruments. By using multiple instruments, our analysis of CLJ 1226.9+3332 covers a large radial range, from the central regions to the cluster outskirts: 0.05 R500 < r < 1.1 R500. This is a wider range of spatial scales than is typically recovered by SZ instruments. Similar analyses will be possible with the new generation of SZ instruments such as NIKA2 and MUSTANG2.

A SUZAKU SEARCH FOR NONTHERMAL EMISSION AT HARD X-RAY ENERGIES IN THE COMA CLUSTER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wik, Daniel R.; Sarazin, Craig L.; Finoguenov, Alexis

2009-05-10

The brightest cluster radio halo known resides in the Coma cluster of galaxies. The relativistic electrons producing this diffuse synchrotron emission should also produce inverse Compton emission that becomes competitive with thermal emission from the intracluster medium (ICM) at hard X-ray energies. Thus far, claimed detections of this emission in Coma are controversial. We present a Suzaku HXD-PIN observation of the Coma cluster in order to nail down its nonthermal hard X-ray content. The contribution of thermal emission to the HXD-PIN spectrum is constrained by simultaneously fitting thermal and nonthermal models to it and a spatially equivalent spectrum derived frommore » an XMM-Newton mosaic of the Coma field. We fail to find statistically significant evidence for nonthermal emission in the spectra which are better described by only a single- or multitemperature model for the ICM. Including systematic uncertainties, we derive a 90% upper limit on the flux of nonthermal emission of 6.0 x 10{sup -12} erg s{sup -1} cm{sup -2} (20-80 keV, for {gamma} = 2.0), which implies a lower limit on the cluster-averaged magnetic field of B>0.15 {mu}G. Our flux upper limit is 2.5 times lower than the detected nonthermal flux from RXTE and BeppoSAX. However, if the nonthermal hard X-ray emission in Coma is more spatially extended than the observed radio halo, the Suzaku HXD-PIN may miss some fraction of the emission. A detailed investigation indicates that {approx}50%-67% of the emission might go undetected, which could make our limit consistent with that of Rephaeli and Gruber and Fusco-Femiano et al. The thermal interpretation of the hard Coma spectrum is consistent with recent analyses of INTEGRAL and Swift data.« less

Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

NASA Astrophysics Data System (ADS)

Ryu, D.; Liu, S.; Western, A. W.; Webb, J. A.; Lintern, A.; Leahy, P.; Wilson, P.; Watson, M.; Waters, D.; Bende-Michl, U.

2016-12-01

The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites, spatial variations in water quality are likely from the agricultural inputs (fertilises) and for the Cluster 2 sites, the differences in hydrological transport is responsible for large spatial variations in water quality. These findings can be applied to water quality assessment along with establish effective water and land management in the future.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Stepwise and stagewise approaches for spatial cluster detection.

PubMed

Xu, Jiale; Gangnon, Ronald E

2016-05-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either a hypothesis testing framework or a Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with a tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic areas. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial distribution and cluster analysis of risky sexual behaviours and STDs reported by Chinese adults in Guangzhou, China: a representative population-based study

PubMed Central

Chen, Wen; Zhou, Fangjing; Hall, Brian J; Wang, Yu; Latkin, Carl; Ling, Li; Tucker, Joseph D

2016-01-01

Objectives To assess associations between residences location, risky sexual behaviours and sexually transmitted diseases (STDs) among adults living in Guangzhou, China. Methods Data were obtained from 751 Chinese adults aged 18–59 years in Guangzhou, China, using stratified random sampling by using spatial epidemiological methods. Face-to-face household interviews were conducted to collect self-report data on risky sexual behaviours and diagnosed STDs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of risky sexual behaviours and STDs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results The prevalence of self-reported risky sexual behaviours was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STDs was 7.06%. Anal intercourse clustered in an area located along the border within the rural–urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou <1 year (p=0.007) overlapped this cluster. Excess cases for unprotected sex (p=0.031) overlapped the cluster for college students (p<0.001). Five of nine (55.6%) students who had sexual experience during the last 12 months located in the cluster of unprotected sex. Conclusions Short-term migrants and college students reported greater risky sexual behaviours. Programmes to increase safer sex within these communities to reduce the risk of STDs are warranted in Guangzhou. Spatial analysis identified geographical clusters of risky sexual behaviours, which is critical for optimising surveillance and targeting control measures for these locations in the future. PMID:26843400

Analysis of ambient SO 2 concentrations and winds in the complex surroundings of a thermal power plant

NASA Astrophysics Data System (ADS)

Mlakar, P.

2004-11-01

SO2 pollution is still a significant problem in Slovenia, especially around large thermal power plants (TPPs), like the one at Šoštanj. The Šoštanj TPP is the exclusive source of SO2 in the area and is therefore a perfect example for air pollution studies. In order to understand air pollution around the Šoštanj TPP in detail, some analyses of emissions and ambient concentrations of SO2 at six automated monitoring stations in the surroundings of the TPP were made. The data base from 1991 to 1993 was used when there were no desulfurisation plants in operation. Statistical analyses of the influence of the emissions from the three TPP stacks at different measuring points were made. The analyses prove that the smallest stack (100 m) mainly pollutes villages and towns near the TPP within a radius of a few kilometres. The medium stack's (150 m) influence is noticed at shorter as well as at longer distances up to more than ten kilometres. The highest stack (230 m) pollutes mainly at longer distances, where the plume reaches the higher hills. Detailed analyses of ambient SO2 concentrations were made. They show the temporal and spatial distribution of different classes of SO2 concentrations from very low to alarming values. These analyses show that pollution patterns at a particular station remain the same if observed on a yearly basis, but can vary very much if observed on a monthly basis, mainly because of different weather patterns. Therefore the winds in the basin (as the most important feature influencing air pollution dispersion) were further analysed in detail to find clusters of similar patterns. For cluster analysis of ground-level winds patterns in the basin around the Šoštanj Thermal Power Plant, the Kohonen neural network and Leaders' method were used. Furthermore, the dependence of ambient SO2 concentrations on the clusters obtained was analysed. The results proved that effective cluster analysis can be a useful tool for compressing a huge wind data base in order to find the correlation between winds and pollutant concentrations. The analyses made provide a better insight into air pollution over complex terrain.

Joint spatial-spectral hyperspectral image clustering using block-diagonal amplified affinity matrix

NASA Astrophysics Data System (ADS)

Fan, Lei; Messinger, David W.

2018-03-01

The large number of spectral channels in a hyperspectral image (HSI) produces a fine spectral resolution to differentiate between materials in a scene. However, difficult classes that have similar spectral signatures are often confused while merely exploiting information in the spectral domain. Therefore, in addition to spectral characteristics, the spatial relationships inherent in HSIs should also be considered for incorporation into classifiers. The growing availability of high spectral and spatial resolution of remote sensors provides rich information for image clustering. Besides the discriminating power in the rich spectrum, contextual information can be extracted from the spatial domain, such as the size and the shape of the structure to which one pixel belongs. In recent years, spectral clustering has gained popularity compared to other clustering methods due to the difficulty of accurate statistical modeling of data in high dimensional space. The joint spatial-spectral information could be effectively incorporated into the proximity graph for spectral clustering approach, which provides a better data representation by discovering the inherent lower dimensionality from the input space. We embedded both spectral and spatial information into our proposed local density adaptive affinity matrix, which is able to handle multiscale data by automatically selecting the scale of analysis for every pixel according to its neighborhood of the correlated pixels. Furthermore, we explored the "conductivity method," which aims at amplifying the block diagonal structure of the affinity matrix to further improve the performance of spectral clustering on HSI datasets.

WIYN OPEN CLUSTER STUDY. XXXVI. SPECTROSCOPIC BINARY ORBITS IN NGC 188

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geller, Aaron M.; Mathieu, Robert D.; Harris, Hugh C.

2009-04-15

We present 98 spectroscopic binary orbits resulting from our ongoing radial velocity survey of the old (7 Gyr) open cluster NGC 188. All but 13 are high-probability cluster members based on both radial velocity and proper motion membership analyses. Fifteen of these member binaries are double lined. Our stellar sample spans a magnitude range of 10.8 {<=}V{<=} 16.5 (1.14-0.92 M {sub sun}) and extends spatially to 17 pc ({approx}13 core radii). All of our binary orbits have periods ranging from a few days to on the order of 10{sup 3} days, and thus are hard binaries that dynamically power themore » cluster. For each binary, we present the orbital solutions and place constraints on the component masses. Additionally, we discuss a few binaries of note from our sample, identifying a likely blue straggler-blue straggler binary system (7782), a double-lined binary with a secondary star which is underluminous for its mass (5080), two potential eclipsing binaries (4705 and 5762), and two binaries which are likely members of a quadruple system (5015a and 5015b)« less

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

NASA Astrophysics Data System (ADS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

NASA Technical Reports Server (NTRS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Performance map of a cluster detection test using extended power

PubMed Central

2013-01-01

Background Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. Methods To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. Results Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. Conclusions The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. PMID:24156765

Restriction of Retrotransposon Mobilization in Schizosaccharomyces pombe by Transcriptional Silencing and Higher-Order Chromatin Organization

PubMed Central

Murton, Heather E.; Grady, Patrick J. R.; Chan, Tsun Ho; Cam, Hugh P.; Whitehall, Simon K.

2016-01-01

Uncontrolled propagation of retrotransposons is potentially detrimental to host genome integrity. Therefore, cells have evolved surveillance mechanisms to restrict the mobility of these elements. In Schizosaccharomyces pombe the Tf2 LTR retrotransposons are transcriptionally silenced and are also clustered in the nucleus into structures termed Tf bodies. Here we describe the impact of silencing and clustering on the mobility of an endogenous Tf2 element. Deletion of genes such as set1+ (histone H3 lysine 4 methyltransferase) or abp1+ (CENP-B homolog) that both alleviate silencing and clustering, result in a corresponding increase in mobilization. Furthermore, expression of constitutively active Sre1, a transcriptional activator of Tf2 elements, also alleviates clustering and induces mobilization. In contrast, clustering is not disrupted by loss of the HIRA histone chaperone, despite high levels of expression, and in this background, mobilization frequency is only marginally increased. Thus, mutations that compromise transcriptional silencing but not Tf bodies are insufficient to drive mobilization. Furthermore, analyses of mutant alleles that separate the transcriptional repression and clustering functions of Set1 are consistent with control of Tf2 propagation via a combination of silencing and spatial organization. Our results indicate that host surveillance mechanisms operate at multiple levels to restrict Tf2 retrotransposon mobilization. PMID:27343236

Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

PubMed

Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

2017-12-01

Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the limitations, trade-offs, and considerations for the sensitivities of variables and the biological interpretations of results. The Cluster app is available as a free download for Apple computers at iTunes, with a link to a user guide website.

Relevant genetic differentiation among Brazilian populations of Anastrepha fraterculus (Diptera, Tephritidae)

PubMed Central

Manni, Mosè; Lima, Kátia Manuela; Guglielmino, Carmela Rosalba; Lanzavecchia, Silvia Beatriz; Juri, Marianela; Vera, Teresa; Cladera, Jorge; Scolari, Francesca; Gomulski, Ludvik; Bonizzoni, Mariangela; Gasperi, Giuliano; Silva, Janisete Gomes; Malacrida, Anna Rodolfa

2015-01-01

Abstract We used a population genetic approach to detect the presence of genetic diversity among six populations of Anastrepha fraterculus across Brazil. To this aim, we used Simple Sequence Repeat (SSR) markers, which may capture the presence of differentiative processes across the genome in distinct populations. Spatial analyses of molecular variance were used to identify groups of populations that are both genetically and geographically homogeneous while also being maximally differentiated from each other. The spatial analysis of genetic diversity indicates that the levels of diversity among the six populations vary significantly on an eco-geographical basis. Particularly, altitude seems to represent a differentiating adaptation, as the main genetic differentiation is detected between the two populations present at higher altitudes and the other four populations at sea level. The data, together with the outcomes from different cluster analyses, identify a genetic diversity pattern that overlaps with the distribution of the known morphotypes in the Brazilian area. PMID:26798258

Genetic diversity and population structure in contemporary house sparrow populations along an urbanization gradient

PubMed Central

Vangestel, C; Mergeay, J; Dawson, D A; Callens, T; Vandomme, V; Lens, L

2012-01-01

House sparrow (Passer domesticus) populations have suffered major declines in urban as well as rural areas, while remaining relatively stable in suburban ones. Yet, to date no exhaustive attempt has been made to examine how, and to what extent, spatial variation in population demography is reflected in genetic population structuring along contemporary urbanization gradients. Here we use putatively neutral microsatellite loci to study if and how genetic variation can be partitioned in a hierarchical way among different urbanization classes. Principal coordinate analyses did not support the hypothesis that urban/suburban and rural populations comprise two distinct genetic clusters. Comparison of FST values at different hierarchical scales revealed drift as an important force of population differentiation. Redundancy analyses revealed that genetic structure was strongly affected by both spatial variation and level of urbanization. The results shown here can be used as baseline information for future genetic monitoring programmes and provide additional insights into contemporary house sparrow dynamics along urbanization gradients. PMID:22588131

Genetic diversity and population structure in contemporary house sparrow populations along an urbanization gradient.

PubMed

Vangestel, C; Mergeay, J; Dawson, D A; Callens, T; Vandomme, V; Lens, L

2012-09-01

House sparrow (Passer domesticus) populations have suffered major declines in urban as well as rural areas, while remaining relatively stable in suburban ones. Yet, to date no exhaustive attempt has been made to examine how, and to what extent, spatial variation in population demography is reflected in genetic population structuring along contemporary urbanization gradients. Here we use putatively neutral microsatellite loci to study if and how genetic variation can be partitioned in a hierarchical way among different urbanization classes. Principal coordinate analyses did not support the hypothesis that urban/suburban and rural populations comprise two distinct genetic clusters. Comparison of FST values at different hierarchical scales revealed drift as an important force of population differentiation. Redundancy analyses revealed that genetic structure was strongly affected by both spatial variation and level of urbanization. The results shown here can be used as baseline information for future genetic monitoring programmes and provide additional insights into contemporary house sparrow dynamics along urbanization gradients.

Phylogeography, phylogeny and hybridization in trichechid sirenians: Implications for manatee conservation

USGS Publications Warehouse

Vianna, J.A.; Bonde, R.K.; Caballero, S.; Giraldo, J.P.; Lima, R.P.; Clark, A.; Marmontel, M.; Morales-Vela, B.; De Souza, M. J.; Parr, L.; Rodriguez-Lopez, M.A.; Mignucci-Giannoni, A. A.; Powell, J.A.; Santos, F.R.

2006-01-01

The three living species of manatees, West Indian (Trichechus manatus), Amazonian (Trichechus inunguis) and West African (Trichechus senegalensis), are distributed across the shallow tropical and subtropical waters of America and the western coast of Africa. We have sequenced the mitochondrial DNA control region in 330 Trichechus to compare their phylogeographic patterns. In T. manatus we observed a marked population structure with the identification of three haplotype clusters showing a distinct spatial distribution. A geographic barrier represented by the continuity of the Lesser Antilles to Trinidad Island, near the mouth of the Orinoco River in Venezuela, appears to have restricted the gene flow historically in T. manatus. However, for T. inunguis we observed a single expanding population cluster, with a high diversity of very closely related haplotypes. A marked geographic population structure is likely present in T. senegalensis with at least two distinct clusters. Phylogenetic analyses with the mtDNA cytochrome b gene suggest a clade of the marine Trichechus species, with T. inunguis as the most basal trichechid. This is in agreement with previous morphological analyses. Mitochondrial DNA, autosomal microsatellites and cytogenetic analyses revealed the presence of hybrids between the T. manatus and T. inunguis species at the mouth of the Amazon River in Brazil, extending to the Guyanas and probably as far as the mouth of the Orinoco River. Future conservation strategies should consider the distinct population structure of manatee species, as well as the historical barriers to gene flow and the likely occurrence of interspecific hybridization. ?? 2006 Blackwell Publishing Ltd.

[Space-time suicide clustering in the community of Antequera (Spain)].

PubMed

Pérez-Costillas, Lucía; Blasco-Fontecilla, Hilario; Benítez, Nicolás; Comino, Raquel; Antón, José Miguel; Ramos-Medina, Valentín; Lopez, Amalia; Palomo, José Luis; Madrigal, Lucía; Alcalde, Javier; Perea-Millá, Emilio; Artieda-Urrutia, Paula; de León-Martínez, Victoria; de Diego Otero, Yolanda

2015-01-01

Approximately 3,500 people commit suicide every year in Spain. The main aim of this study is to explore if a spatial and temporal clustering of suicide exists in the region of Antequera (Málaga, España). Sample and procedure: All suicides from January 1, 2004 to December 31, 2008 were identified using data from the Forensic Pathology Department of the Institute of Legal Medicine, Málaga (España). Geolocalisation. Google Earth was used to calculate the coordinates for each suicide decedent's address. Statistical analysis. A spatiotemporal permutation scan statistic and the Ripley's K function were used to explore spatiotemporal clustering. Pearson's chi-squared was used to determine whether there were differences between suicides inside and outside the spatiotemporal clusters. A total of 120 individuals committed suicide within the region of Antequera, of which 96 (80%) were included in our analyses. Statistically significant evidence for 7 spatiotemporal suicide clusters emerged within critical limits for the 0-2.5 km distance and for the first and second semanas (P<.05 in both cases) after suicide. There was not a single subject diagnosed with a current psychotic disorder, among suicides within clusters, whereas outside clusters, 20% had this diagnosis (X2=4.13; df=1; P<.05). There are spatiotemporal suicide clusters in the area surrounding Antequera. Patients diagnosed with current psychotic disorder are less likely to be influenced by the factors explaining suicide clustering. Copyright © 2013 SEP y SEPB. Published by Elsevier España. All rights reserved.

Spatial and temporal analysis of extreme sea level and storm surge events around the coastline of the UK

PubMed Central

Haigh, Ivan D.; Wadey, Matthew P.; Wahl, Thomas; Ozsoy, Ozgun; Nicholls, Robert J.; Brown, Jennifer M.; Horsburgh, Kevin; Gouldby, Ben

2016-01-01

In this paper we analyse the spatial footprint and temporal clustering of extreme sea level and skew surge events around the UK coast over the last 100 years (1915–2014). The vast majority of the extreme sea level events are generated by moderate, rather than extreme skew surges, combined with spring astronomical high tides. We distinguish four broad categories of spatial footprints of events and the distinct storm tracks that generated them. There have been rare events when extreme levels have occurred along two unconnected coastal regions during the same storm. The events that occur in closest succession (<4 days) typically impact different stretches of coastline. The spring/neap tidal cycle prevents successive extreme sea level events from happening within 4–8 days. Finally, the 2013/14 season was highly unusual in the context of the last 100 years from an extreme sea level perspective. PMID:27922630

Understanding neighbourhoods, communities and environments: new approaches for social work research.

PubMed

Holland, Sally; Burgess, Stephen; Grogan-Kaylor, Andy; Delva, Jorge

2010-06-01

This article discusses some new ways in which social work research can explore the interaction between neighbourhoods and child and adult wellbeing. The authors note that social work practices are often criticised for taking an individualistic approach and paying too little attention to the service user's environment. The article uses examples of research projects from Chile, the United States of America and Wales, to discuss the use of spatially oriented research methods for understanding neighbourhood factors. Quantitative, qualitative and mixed methods approaches that are particularly appropriate for investigating social work relevant topics are discussed in turn, including quantitative and qualitative uses for geographical information systems (GIS), hierarchical linear modelling (HLM) for analysing spatially clustered data and qualitative mobile interviews. The article continues with a discussion of the strengths and limitations of using spatially orientated research designs in social work research settings and concludes optimistically with suggestions for future directions in this area.

Identification of an intact ParaHox cluster with temporal colinearity but altered spatial colinearity in the hemichordate Ptychodera flava

PubMed Central

2013-01-01

Background ParaHox and Hox genes are thought to have evolved from a common ancestral ProtoHox cluster or from tandem duplication prior to the divergence of cnidarians and bilaterians. Similar to Hox clusters, chordate ParaHox genes including Gsx, Xlox, and Cdx, are clustered and their expression exhibits temporal and spatial colinearity. In non-chordate animals, however, studies on the genomic organization of ParaHox genes are limited to only a few animal taxa. Hemichordates, such as the Enteropneust acorn worms, have been used to gain insights into the origins of chordate characters. In this study, we investigated the genomic organization and expression of ParaHox genes in the indirect developing hemichordate acorn worm Ptychodera flava. Results We found that P. flava contains an intact ParaHox cluster with a similar arrangement to that of chordates. The temporal expression order of the P. flava ParaHox genes is the same as that of the chordate ParaHox genes. During embryogenesis, the spatial expression pattern of PfCdx in the posterior endoderm represents a conserved feature similar to the expression of its orthologs in other animals. On the other hand, PfXlox and PfGsx show a novel expression pattern in the blastopore. Nevertheless, during metamorphosis, PfXlox and PfCdx are expressed in the endoderm in a spatially staggered pattern similar to the situation in chordates. Conclusions Our study shows that P. flava ParaHox genes, despite forming an intact cluster, exhibit temporal colinearity but lose spatial colinearity during embryogenesis. During metamorphosis, partial spatial colinearity is retained in the transforming larva. These results strongly suggest that intact ParaHox gene clustering was retained in the deuterostome ancestor and is correlated with temporal colinearity. PMID:23802544

The Spatial Distribution of the Young Stellar Clusters in the Star-forming Galaxy NGC 628

NASA Astrophysics Data System (ADS)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Aloisi, A.; Bright, S. N.; Christian, C.; Cignoni, M.; Dale, D. A.; Dobbs, C.; Elmegreen, D. M.; Fumagalli, M.; Gallagher, J. S., III; Grebel, E. K.; Johnson, K. E.; Lee, J. C.; Messa, M.; Smith, L. J.; Ryon, J. E.; Thilker, D.; Ubeda, L.; Wofford, A.

2015-12-01

We present a study of the spatial distribution of the stellar cluster populations in the star-forming galaxy NGC 628. Using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey), we have identified 1392 potential young (≲ 100 Myr) stellar clusters within the galaxy using a combination of visual inspection and automatic selection. We investigate the clustering of these young stellar clusters and quantify the strength and change of clustering strength with scale using the two-point correlation function. We also investigate how image boundary conditions and dust lanes affect the observed clustering. The distribution of the clusters is well fit by a broken power law with negative exponent α. We recover a weighted mean index of α ∼ -0.8 for all spatial scales below the break at 3.″3 (158 pc at a distance of 9.9 Mpc) and an index of α ∼ -0.18 above 158 pc for the accumulation of all cluster types. The strength of the clustering increases with decreasing age and clusters older than 40 Myr lose their clustered structure very rapidly and tend to be randomly distributed in this galaxy, whereas the mass of the star cluster has little effect on the clustering strength. This is consistent with results from other studies that the morphological hierarchy in stellar clustering resembles the same hierarchy as the turbulent interstellar medium.

A comparative study of spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) offshore Peru

NASA Astrophysics Data System (ADS)

Feng, Yongjiu; Cui, Li; Chen, Xinjun; Liu, Yu

2017-06-01

We examined spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid. The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013. The data for all years as well as the eight years (excluding El Niño events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Niño, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Niño conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

A new multidimensional population health indicator for policy makers: absolute level, inequality and spatial clustering - an empirical application using global sub-national infant mortality data.

PubMed

Sartorius, Benn K D; Sartorius, Kurt

2014-11-01

The need for a multidimensional measure of population health that accounts for its distribution remains a central problem to guide the allocation of limited resources. Absolute proxy measures, like the infant mortality rate (IMR), are limited because they ignore inequality and spatial clustering. We propose a novel, three-part, multidimensional mortality indicator that can be used as the first step to differentiate interventions in a region or country. The three-part indicator (MortalityABC index) combines absolute mortality rate, the Theil Index to calculate mortality inequality and the Getis-Ord G statistic to determine the degree of spatial clustering. The analysis utilises global sub-national IMR data to empirically illustrate the proposed indicator. The three-part indicator is mapped globally to display regional/country variation and further highlight its potential application. Developing countries (e.g. in sub-Saharan Africa) display high levels of absolute mortality as well as variable mortality inequality with evidence of spatial clustering within certain sub-national units ("hotspots"). Although greater inequality is observed outside developed regions, high mortality inequality and spatial clustering are common in both developed and developing countries. Significant positive correlation was observed between the degree of spatial clustering and absolute mortality. The proposed multidimensional indicator should prove useful for spatial allocation of healthcare resources within a country, because it can prompt a wide range of policy options and prioritise high-risk areas. The new indicator demonstrates the inadequacy of IMR as a single measure of population health, and it can also be adapted to lower administrative levels within a country and other population health measures.

Age Differences in Recall and Information Processing in Verbal and Spatial Learning.

ERIC Educational Resources Information Center

Mungas, Dan; And Others

1991-01-01

Three age groups of 24 people each completed verbal word list tasks and spatial learning tasks 5 times each. Significant age differences were found for total recall and type of task. Younger subjects showed increased levels of clustering--organizing information according to semantic or spatial clusters. Age was not related to temporal order of…

Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing.

PubMed

Liu, Chao; Abu-Jamous, Basel; Brattico, Elvira; Nandi, Asoke K

2017-03-01

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

Continuous Variable Cluster State Generation over the Optical Spatial Mode Comb

DOE PAGES

Pooser, Raphael C.; Jing, Jietai

2014-10-20

One way quantum computing uses single qubit projective measurements performed on a cluster state (a highly entangled state of multiple qubits) in order to enact quantum gates. The model is promising due to its potential scalability; the cluster state may be produced at the beginning of the computation and operated on over time. Continuous variables (CV) offer another potential benefit in the form of deterministic entanglement generation. This determinism can lead to robust cluster states and scalable quantum computation. Recent demonstrations of CV cluster states have made great strides on the path to scalability utilizing either time or frequency multiplexingmore » in optical parametric oscillators (OPO) both above and below threshold. The techniques relied on a combination of entangling operators and beam splitter transformations. Here we show that an analogous transformation exists for amplifiers with Gaussian inputs states operating on multiple spatial modes. By judicious selection of local oscillators (LOs), the spatial mode distribution is analogous to the optical frequency comb consisting of axial modes in an OPO cavity. We outline an experimental system that generates cluster states across the spatial frequency comb which can also scale the amount of quantum noise reduction to potentially larger than in other systems.« less

Spatial clusters of daytime sleepiness and association with nighttime noise levels in a Swiss general population (GeoHypnoLaus).

PubMed

Joost, Stéphane; Haba-Rubio, José; Himsl, Rebecca; Vollenweider, Peter; Preisig, Martin; Waeber, Gérard; Marques-Vidal, Pedro; Heinzer, Raphaël; Guessous, Idris

2018-05-31

Daytime sleepiness is highly prevalent in the general adult population and has been linked to an increased risk of workplace and vehicle accidents, lower professional performance and poorer health. Despite the established relationship between noise and daytime sleepiness, little research has explored the individual-level spatial distribution of noise-related sleep disturbances. We assessed the spatial dependence of daytime sleepiness and tested whether clusters of individuals exhibiting higher daytime sleepiness were characterized by higher nocturnal noise levels than other clusters. Population-based cross-sectional study, in the city of Lausanne, Switzerland. Sleepiness was measured using the Epworth Sleepiness Scale (ESS) for 3697 georeferenced individuals from the CoLaus|PsyCoLaus cohort (period = 2009-2012). We used the sonBASE georeferenced database produced by the Swiss Federal Office for the Environment to characterize nighttime road traffic noise exposure throughout the city. We used the GeoDa software program to calculate the Getis-Ord G i * statistics for unadjusted and adjusted ESS in order to detect spatial clusters of high and low ESS values. Modeled nighttime noise exposure from road and rail traffic was compared across ESS clusters. Daytime sleepiness was not randomly distributed and showed a significant spatial dependence. The median nighttime traffic noise exposure was significantly different across the three ESS Getis cluster classes (p < 0.001). The mean nighttime noise exposure in the high ESS cluster class was 47.6, dB(A) 5.2 dB(A) higher than in low clusters (p < 0.001) and 2.1 dB(A) higher than in the neutral class (p < 0.001). These associations were independent of major potential confounders including body mass index and neighborhood income level. Clusters of higher daytime sleepiness in adults are associated with higher median nighttime noise levels. The identification of these clusters can guide tailored public health interventions. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

PubMed

El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

2017-10-01

Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock-water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

Applications for edge detection techniques using Chandra and XMM-Newton data: galaxy clusters and beyond

NASA Astrophysics Data System (ADS)

Walker, S. A.; Sanders, J. S.; Fabian, A. C.

2016-09-01

The unrivalled spatial resolution of the Chandra X-ray observatory has allowed many breakthroughs to be made in high-energy astrophysics. Here we explore applications of Gaussian gradient magnitude (GGM) filtering to X-ray data, which dramatically improves the clarity of surface brightness edges in X-ray observations, and maps gradients in X-ray surface brightness over a range of spatial scales. In galaxy clusters, we find that this method is able to reveal remarkable substructure behind the cold fronts in Abell 2142 and Abell 496, possibly the result of Kelvin-Helmholtz instabilities. In Abell 2319 and Abell 3667, we demonstrate that the GGM filter can provide a straightforward way of mapping variations in the widths and jump ratios along the lengths of cold fronts. We present results from our ongoing programme of analysing the Chandra and XMM-Newton archives with the GGM filter. In the Perseus cluster, we identify a previously unseen edge around 850 kpc from the core to the east, lying outside a known large-scale cold front, which is possibly a bow shock. In MKW 3s we find an unusual `V' shape surface brightness enhancement starting at the cluster core, which may be linked to the AGN jet. In the Crab nebula a new, moving feature in the outer part of the torus is identified which moves across the plane of the sky at a speed of ˜0.1c, and lies much further from the central pulsar than the previous motions seen by Chandra.

Spatiotemporal Analysis of the Malaria Epidemic in Mainland China, 2004-2014.

PubMed

Huang, Qiang; Hu, Lin; Liao, Qi-Bin; Xia, Jing; Wang, Qian-Ru; Peng, Hong-Juan

2017-08-01

The purpose of this study is to characterize spatiotemporal heterogeneities in malaria distribution at a provincial level and investigate the association between malaria incidence and climate factors from 2004 to 2014 in China to inform current malaria control efforts. National malaria incidence peaked (4.6/100,000) in 2006 and decreased to a very low level (0.21/100,000) in 2014, and the proportion of imported cases increased from 16.2% in 2004 to 98.2% in 2014. Statistical analyses of global and local spatial autocorrelations and purely spatial scan statistics revealed that malaria was localized in Hainan, Anhui, and Yunnan during 2004-2009 and then gradually shifted and clustered in Yunnan after 2010. Purely temporal clusters shortened to less than 5 months during 2012-2014. The two most likely clusters detected using spatiotemporal analysis occurred in Anhui between July 2005 and November 2007 and Yunnan between January 2010 and June 2012. Correlation coefficients for the association between malaria incidence and climate factors sharply decreased after 2010, and there were zero-month lag effects for climate factors during 2010-2014. Overall, the spatiotemporal distribution of malaria in China changed from relatively scattered (2004-2009) to relatively clustered (2010-2014). As the proportion of imported cases increased, the effect of climate factors on malaria incidence has gradually become weaker since 2011. Therefore, new warning systems should be applied to monitor resurgence and outbreaks of malaria in mainland China, and quarantine at borders should be reinforced to control the increasingly trend of imported malaria cases.

Functionally relevant diversity of closely related Nitrospira in activated sludge.

PubMed

Gruber-Dorninger, Christiane; Pester, Michael; Kitzinger, Katharina; Savio, Domenico F; Loy, Alexander; Rattei, Thomas; Wagner, Michael; Daims, Holger

2015-03-01

Nitrospira are chemolithoautotrophic nitrite-oxidizing bacteria that catalyze the second step of nitrification in most oxic habitats and are important for excess nitrogen removal from sewage in wastewater treatment plants (WWTPs). To date, little is known about their diversity and ecological niche partitioning within complex communities. In this study, the fine-scale community structure and function of Nitrospira was analyzed in two full-scale WWTPs as model ecosystems. In Nitrospira-specific 16S rRNA clone libraries retrieved from each plant, closely related phylogenetic clusters (16S rRNA identities between clusters ranged from 95.8% to 99.6%) within Nitrospira lineages I and II were found. Newly designed probes for fluorescence in situ hybridization (FISH) allowed the specific detection of several of these clusters, whose coexistence in the WWTPs was shown for prolonged periods of several years. In situ ecophysiological analyses based on FISH, relative abundance and spatial arrangement quantification, as well as microautoradiography revealed functional differences of these Nitrospira clusters regarding the preferred nitrite concentration, the utilization of formate as substrate and the spatial coaggregation with ammonia-oxidizing bacteria as symbiotic partners. Amplicon pyrosequencing of the nxrB gene, which encodes subunit beta of nitrite oxidoreductase of Nitrospira, revealed in one of the WWTPs as many as 121 species-level nxrB operational taxonomic units with highly uneven relative abundances in the amplicon library. These results show a previously unrecognized high diversity of Nitrospira in engineered systems, which is at least partially linked to niche differentiation and may have important implications for process stability.

Spatial correlations, clustering and percolation-like transitions in homicide crimes

NASA Astrophysics Data System (ADS)

Alves, L. G. A.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2015-07-01

The spatial dynamics of criminal activities has been recently studied through statistical physics methods; however, models and results have been focusing on local scales (city level) and much less is known about these patterns at larger scales, e.g. at a country level. Here we report on a characterization of the spatial dynamics of the homicide crimes along the Brazilian territory using data from all cities (˜5000) in a period of more than thirty years. Our results show that the spatial correlation function in the per capita homicides decays exponentially with the distance between cities and that the characteristic correlation length displays an acute increasing trend in the latest years. We also investigate the formation of spatial clusters of cities via a percolation-like analysis, where clustering of cities and a phase-transition-like behavior describing the size of the largest cluster as a function of a homicide threshold are observed. This transition-like behavior presents evolutive features characterized by an increasing in the homicide threshold (where the transitions occur) and by a decreasing in the transition magnitudes (length of the jumps in the cluster size). We believe that our work sheds new light on the spatial patterns of criminal activities at large scales, which may contribute for better political decisions and resources allocation as well as opens new possibilities for modeling criminal activities by setting up fundamental empirical patterns at large scales.

The seven sisters DANCe. III. Projected spatial distribution

NASA Astrophysics Data System (ADS)

Olivares, J.; Moraux, E.; Sarro, L. M.; Bouy, H.; Berihuete, A.; Barrado, D.; Huelamo, N.; Bertin, E.; Bouvier, J.

2018-04-01

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims: We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods: We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results: We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King's model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions: We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

A Fine-Scale Functional Logic to Convergence from Retina to Thalamus.

PubMed

Liang, Liang; Fratzl, Alex; Goldey, Glenn; Ramesh, Rohan N; Sugden, Arthur U; Morgan, Josh L; Chen, Chinfei; Andermann, Mark L

2018-05-31

Numerous well-defined classes of retinal ganglion cells innervate the thalamus to guide image-forming vision, yet the rules governing their convergence and divergence remain unknown. Using two-photon calcium imaging in awake mouse thalamus, we observed a functional arrangement of retinal ganglion cell axonal boutons in which coarse-scale retinotopic ordering gives way to fine-scale organization based on shared preferences for other visual features. Specifically, at the ∼6 μm scale, clusters of boutons from different axons often showed similar preferences for either one or multiple features, including axis and direction of motion, spatial frequency, and changes in luminance. Conversely, individual axons could "de-multiplex" information channels by participating in multiple, functionally distinct bouton clusters. Finally, ultrastructural analyses demonstrated that retinal axonal boutons in a local cluster often target the same dendritic domain. These data suggest that functionally specific convergence and divergence of retinal axons may impart diverse, robust, and often novel feature selectivity to visual thalamus. Copyright © 2018 Elsevier Inc. All rights reserved.

Cancer mortality rates and spillover effects among different areas: A case study in Campania (southern Italy).

PubMed

Agovino, Massimiliano; Aprile, Maria Carmela; Garofalo, Antonio; Mariani, Angela

2018-05-01

The present study analyses the spatial distribution of cancer mortality rates in Campania (an Italian region with the highest population density), in which residents in several areas are exposed to major environmental health hazards. The paper has the methodological aims of verifying the existence, or otherwise, of a spatial correlation between mortality from different types of cancer and the occurrence of some specific area characteristics, using both Bayesian statistics and spatial econometrics. We show that the use of the Spatial Empirical Bayes Smoothed Rate, instead of the more commonly used Raw Rate, allows a more comprehensive analysis of the mortality rate, highlighting the existence of different cluster sizes throughout the region, according to the type of cancer mortality rate analysed. By using a Spatial Durbin model we verify that cancer mortality rates are related to the environmental characteristics of specific areas with spatial spillover effects. Our results validate the hypothesis that living along the coast by Mt Vesuvius and, to a lesser extent, along the Domitio-Flegreo coast NW of Naples and in more urbanised municipalities, increases the risk of dying of cancer. By contrast, living in less urbanised municipalities, with the presence of natural and historical attractions, has a positive effect on the residents' health, reducing their risk of disease. In both cases significant spillover effects (negative and positive) are found in municipalities close to the areas in question. Despite a number of reasonable limitations, our findings may provide useful information support for policy makers to foster knowledge, awareness and informed participation of citizens. Copyright © 2018 Elsevier Ltd. All rights reserved.

Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control

PubMed Central

Echodu, Richard; Opiyo, Elizabeth A.; Dion, Kirstin; Halyard, Alexis; Dunn, Augustine W.; Aksoy, Serap; Caccone, Adalgisa

2017-01-01

Uganda is the only country where the chronic and acute forms of human African Trypanosomiasis (HAT) or sleeping sickness both occur and are separated by < 100 km in areas north of Lake Kyoga. In Uganda, Glossina fuscipes fuscipes is the main vector of the Trypanosoma parasites responsible for these diseases as well for the animal African Trypanosomiasis (AAT), or Nagana. We used highly polymorphic microsatellite loci and a mitochondrial DNA (mtDNA) marker to provide fine scale spatial resolution of genetic structure of G. f. fuscipes from 42 sampling sites from the northern region of Uganda where a merger of the two disease belts is feared. Based on microsatellite analyses, we found that G. f. fuscipes in northern Uganda are structured into three distinct genetic clusters with varying degrees of interconnectivity among them. Based on genetic assignment and spatial location, we grouped the sampling sites into four genetic units corresponding to northwestern Uganda in the Albert Nile drainage, northeastern Uganda in the Lake Kyoga drainage, western Uganda in the Victoria Nile drainage, and a transition zone between the two northern genetic clusters characterized by high level of genetic admixture. An analysis using HYBRIDLAB supported a hybrid swarm model as most consistent with tsetse genotypes in these admixed samples. Results of mtDNA analyses revealed the presence of 30 haplotypes representing three main haplogroups, whose location broadly overlaps with the microsatellite defined clusters. Migration analyses based on microsatellites point to moderate migration among the northern units located in the Albert Nile, Achwa River, Okole River, and Lake Kyoga drainages, but not between the northern units and the Victoria Nile drainage in the west. Effective population size estimates were variable with low to moderate sizes in most populations and with evidence of recent population bottlenecks, especially in the northeast unit of the Lake Kyoga drainage. Our microsatellite and mtDNA based analyses indicate that G. f. fuscipes movement along the Achwa and Okole rivers may facilitate northwest expansion of the Rhodesiense disease belt in Uganda. We identified tsetse migration corridors and recommend a rolling carpet approach from south of Lake Kyoga northward to minimize disease dispersal and prevent vector re-colonization. Additionally, our findings highlight the need for continuing tsetse monitoring efforts during and after control. PMID:28453513

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults

PubMed Central

Gartner, Danielle R.; Taber, Daniel R.; Hirsch, Jana A.; Robinson, Whitney R.

2016-01-01

Purpose While obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race/ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence. Methods Data from the Centers for Disease Control and Prevention’s 2013 Behavioral Risk Factor Surveillance System (BRFSS) were used to calculate state-specific obesity prevalence and gender inequality measures. Global and Local Moran’s Indices were calculated to determine spatial autocorrelation. Results Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score=4.89, p-value <0.001). Local Moran’s Indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the U.S. Conclusion Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality. PMID:27039046

Fractal Analyses of High-Resolution Cloud Droplet Measurements.

NASA Astrophysics Data System (ADS)

Malinowski, Szymon P.; Leclerc, Monique Y.; Baumgardner, Darrel G.

1994-02-01

Fractal analyses of individual cloud droplet distributions using aircraft measurements along one-dimensional horizontal cross sections through clouds are performed. Box counting and cluster analyses are used to determine spatial scales of inhomogeneity of cloud droplet spacing. These analyses reveal that droplet spatial distributions do not exhibit a fractal behavior. A high variability in local droplet concentration in cloud volumes undergoing mixing was found. In these regions, thin filaments of cloudy air with droplet concentration close to those observed in cloud cores were found. Results suggest that these filaments may be anisotropic. Additional box counting analyses performed for various classes of cloud droplet diameters indicate that large and small droplets are similarly distributed, except for the larger characteristic spacing of large droplets.A cloud-clear air interface defined by a certain threshold of total droplet count (TDC) was investigated. There are indications that this interface is a convoluted surface of a fractal nature, at least in actively developing cumuliform clouds. In contrast, TDC in the cloud interior does not have fractal or multifractal properties. Finally a random Cantor set (RCS) was introduced as a model of a fractal process with an ill-defined internal scale. A uniform measure associated with the RCS after several generations was introduced to simulate the TDC records. Comparison of the model with real TDC records indicates similar properties of both types of data series.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution.

PubMed

Gangnon, Ronald E

2012-03-01

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. © 2011, The International Biometric Society.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

PubMed Central

Gangnon, Ronald E.

2011-01-01

Summary The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, while rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. PMID:21762118

Exploring the Mechanisms of Ecological Land Change Based on the Spatial Autoregressive Model: A Case Study of the Poyang Lake Eco-Economic Zone, China

PubMed Central

Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai

2013-01-01

Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778

Geographic Clusters of Basal Cell Carcinoma in a Northern California Health Plan Population.

PubMed

Ray, G Thomas; Kulldorff, Martin; Asgari, Maryam M

2016-11-01

Rates of skin cancer, including basal cell carcinoma (BCC), the most common cancer, have been increasing over the past 3 decades. A better understanding of geographic clustering of BCCs can help target screening and prevention efforts. Present a methodology to identify spatial clusters of BCC and identify such clusters in a northern California population. This retrospective study used a BCC registry to determine rates of BCC by census block group, and used spatial scan statistics to identify statistically significant geographic clusters of BCCs, adjusting for age, sex, and socioeconomic status. The study population consisted of white, non-Hispanic members of Kaiser Permanente Northern California during years 2011 and 2012. Statistically significant geographic clusters of BCC as determined by spatial scan statistics. Spatial analysis of 28 408 individuals who received a diagnosis of at least 1 BCC in 2011 or 2012 revealed distinct geographic areas with elevated BCC rates. Among the 14 counties studied, BCC incidence ranged from 661 to 1598 per 100 000 person-years. After adjustment for age, sex, and neighborhood socioeconomic status, a pattern of 5 discrete geographic clusters emerged, with a relative risk ranging from 1.12 (95% CI, 1.03-1.21; P = .006) for a cluster in eastern Sonoma and northern Napa Counties to 1.40 (95% CI, 1.15-1.71; P < .001) for a cluster in east Contra Costa and west San Joaquin Counties, compared with persons residing outside that cluster. In this study of a northern California population, we identified several geographic clusters with modestly elevated incidence of BCC. Knowledge of geographic clusters can help inform future research on the underlying etiology of the clustering including factors related to the environment, health care access, or other characteristics of the resident population, and can help target screening efforts to areas of highest yield.

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

PubMed

Shen, Lili; Guo, Jiming; Wang, Lei

2018-06-06

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

A Secondary Spatial Analysis of Gun Violence near Boston Schools: a Public Health Approach.

PubMed

Barboza, Gia

2018-06-01

School neighborhood violence continues to be a major public health problem among urban students. A large body of research addresses violence at school; however, fewer studies have explored concentrations of violence in areas proximal to schools. This study aimed to quantify the concentration of shootings near schools to elucidate the place-based dynamics that may be focal points for violence prevention. Geocoded databases of shooting and school locations were used to examine locational patterns of firearm shootings and elementary, middle, and high schools in Boston, Massachusetts. Analyses utilized spatial statistics for point pattern data including distance matrix and K function methodology to quantify the degree of spatial dependence of shootings around schools. Results suggested that between 2012 and 2015, there were 678 shooting incidents in Boston; the average density was 5.1 per square kilometer. The nearest neighbor index (NNI = 0.335 km, p < .001, O = 0.95 km, E = 0.28 km) and G function analysis revealed a clustered pattern of gun shooting incidents indicative of a spatially non-random process. The mean and median distance from any school to the nearest shooting location was 0.35 and 0.33 km, respectively. A majority (56%, 74/133) of schools in Boston had at least one shooting incident within 400 m, a distance that would take about 5 min to walk if traveling by foot. The bivariate K function indicated that a significantly greater number of shootings were clustered within short distances from schools than would be expected under a null hypothesis of no spatial dependence. Implications for students attending schools in racially homogenous neighborhoods across all income levels are discussed.

Spatial clustering of high load ocular Chlamydia trachomatis infection in trachoma: a cross-sectional population-based study.

PubMed

Last, Anna; Burr, Sarah; Alexander, Neal; Harding-Esch, Emma; Roberts, Chrissy H; Nabicassa, Meno; Cassama, Eunice Teixeira da Silva; Mabey, David; Holland, Martin; Bailey, Robin

2017-07-31

Chlamydia trachomatis (Ct) is the most common cause of bacterial sexually transmitted infection and infectious cause of blindness (trachoma) worldwide. Understanding the spatial distribution of Ct infection may enable us to identify populations at risk and improve our understanding of Ct transmission. In this study, we sought to investigate the spatial distribution of Ct infection and the clinical features associated with high Ct load in trachoma-endemic communities on the Bijagós Archipelago (Guinea Bissau). We collected 1507 conjunctival samples and corresponding detailed clinical data during a cross-sectional population-based geospatially representative trachoma survey. We used droplet digital PCR to estimate Ct load on conjunctival swabs. Geostatistical tools were used to investigate clustering of ocular Ct infections. Spatial clusters (independent of age and gender) of individuals with high Ct loads were identified using local indicators of spatial association. We did not detect clustering of individuals with low load infections. These data suggest that infections with high bacterial load may be important in Ct transmission. These geospatial tools may be useful in the study of ocular Ct transmission dynamics and as part of trachoma surveillance post-treatment, to identify clusters of infection and thresholds of Ct load that may be important foci of re-emergent infection in communities. © FEMS 2017.

Spatial pattern evolution of Aedes aegypti breeding sites in an Argentinean city without a dengue vector control programme.

PubMed

Espinosa, Manuel O; Polop, Francisco; Rotela, Camilo H; Abril, Marcelo; Scavuzzo, Carlos M

2016-11-21

The main objective of this study was to obtain and analyse the space-time dynamics of Aedes aegypti breeding sites in Clorinda City, Formosa Province, Argentina coupled with landscape analysis using the maximum entropy approach in order to generate a dengue vector niche model. In urban areas, without vector control activities, 12 entomologic (larval) samplings were performed during three years (October 2011 to October 2014). The entomologic surveillance area represented 16,511 houses. Predictive models for Aedes distribution were developed using vector breeding abundance data, density analysis, clustering and geoprocessing techniques coupled with Earth observation satellite data. The spatial analysis showed a vector spatial distribution pattern with clusters of high density in the central region of Clorinda with a well-defined high-risk area in the western part of the city. It also showed a differential temporal behaviour among different areas, which could have implications for risk models and control strategies at the urban scale. The niche model obtained for Ae. aegypti, based on only one year of field data, showed that 85.8% of the distribution of breeding sites is explained by the percentage of water supply (48.2%), urban distribution (33.2%), and the percentage of urban coverage (4.4%). The consequences for the development of control strategies are discussed with reference to the results obtained using distribution maps based on environmental variables.

Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search.

PubMed

Mena, Carlos; Sepúlveda, Cesar; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2018-05-07

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Lung cancer mortality clusters in Shandong Province, China: how do they change over 40 years?

PubMed Central

Fu, Zhentao; Li, Yingmei; Lu, Zilong; Chu, Jie; Sun, Jiandong; Zhang, Jiyu; Zhang, Gaohui; Xue, Fuzhong; Guo, Xiaolei; Xu, Aiqiang

2017-01-01

Lung cancer has long been a major health problem in China. This study aimed to examine the temporal trend and spatial pattern of lung cancer mortality in Shandong Province from 1970 to 2013. Lung cancer mortality data were obtained from Shandong Death Registration System and three nationwide retrospective cause-of-death surveys. A Purely Spatial Scan Statistics method with Discrete Poisson models was used to detect possible high-risk spatial clusters. The results show that lung cancer mortality rate in Shandong Province increased markedly from 1970-1974 (7.22 per 100,000 person-years) to 2011-2013 (56.37/100, 000). This increase was associated with both demographic and non-demographic factors. Several significant spatial clusters with high lung cancer mortality were identified. The most likely cluster was located in the northern region of Shandong Province during both 1970-1974 and 2011-2013. It appears the spatial pattern remained largely consistent over the last 40 years despite the absolute increase in the mortality rates. These findings will help develop intervention strategies to reduce lung cancer mortality in this large Chinese population. PMID:29179474

A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

NASA Astrophysics Data System (ADS)

Lee, M. J.; Oh, K. Y.; Joung-ho, L.

2016-12-01

Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The authors appreciate the support that this study has received from `Building application frame of environmental issues, to respond to the latest ICT trends'.

Effect of a new motorway on social-spatial patterning of road traffic accidents: A retrospective longitudinal natural experimental study

PubMed Central

Mitchell, Richard; Ogilvie, David

2017-01-01

Background The World Health Organisation reports that road traffic accidents (accidents) could become the seventh leading cause of death globally by 2030. Accidents often occur in spatial clusters and, generally, there are more accidents in less advantaged areas. Infrastructure changes, such as new roads, can affect the locations and magnitude of accident clusters but evidence of impact is lacking. A new 5-mile motorway extension was opened in 2011 in Glasgow, Scotland. Previous research found no impact on the number of accidents but did not consider their spatial location or socio-economic setting. We evaluated impacts on these, both locally and city-wide. Methods We used STATS19 data covering the period 2008 to 2014 and describing the location and details of all reported accidents involving a personal injury. Poisson-based continuous scan statistics were used to detect spatial clusters of accidents and any change in these over time. Change in the socio-economic distribution of accident cluster locations during the study period was also assessed. Results In each year accidents were strongly clustered, with statistically significant clusters more likely to occur in socio-economically deprived areas. There was no significant shift in the magnitude or location of accident clusters during motorway construction or following opening, either locally or city-wide. There was also no impact on the socio-economic patterning of accident cluster locations. Conclusions Although urban infrastructure changes occur constantly, all around the world, this is the first study to evaluate the impact of such changes on road accident clusters. Despite expectations to the contrary from both proponents and opponents of the M74 extension, we found no beneficial or adverse change in the socio-spatial distribution of accidents associated with its construction, opening or operation. Our approach and findings can help inform urban planning internationally. PMID:28880956

Effect of a new motorway on social-spatial patterning of road traffic accidents: A retrospective longitudinal natural experimental study.

PubMed

Olsen, Jonathan R; Mitchell, Richard; Ogilvie, David

2017-01-01

The World Health Organisation reports that road traffic accidents (accidents) could become the seventh leading cause of death globally by 2030. Accidents often occur in spatial clusters and, generally, there are more accidents in less advantaged areas. Infrastructure changes, such as new roads, can affect the locations and magnitude of accident clusters but evidence of impact is lacking. A new 5-mile motorway extension was opened in 2011 in Glasgow, Scotland. Previous research found no impact on the number of accidents but did not consider their spatial location or socio-economic setting. We evaluated impacts on these, both locally and city-wide. We used STATS19 data covering the period 2008 to 2014 and describing the location and details of all reported accidents involving a personal injury. Poisson-based continuous scan statistics were used to detect spatial clusters of accidents and any change in these over time. Change in the socio-economic distribution of accident cluster locations during the study period was also assessed. In each year accidents were strongly clustered, with statistically significant clusters more likely to occur in socio-economically deprived areas. There was no significant shift in the magnitude or location of accident clusters during motorway construction or following opening, either locally or city-wide. There was also no impact on the socio-economic patterning of accident cluster locations. Although urban infrastructure changes occur constantly, all around the world, this is the first study to evaluate the impact of such changes on road accident clusters. Despite expectations to the contrary from both proponents and opponents of the M74 extension, we found no beneficial or adverse change in the socio-spatial distribution of accidents associated with its construction, opening or operation. Our approach and findings can help inform urban planning internationally.

A Socio-spatial Dimension of Local Creative Industry Development in Semarang and Kudus Batik Clusters

NASA Astrophysics Data System (ADS)

Nugroho, P.

2018-02-01

Creative industries existence is inseparable from the underlying social construct which provides sources for creativity and innovation. The working of social capital in a society facilitates information exchange, knowledge transfer and technology acquisition within the industry through social networks. As a result, a socio-spatial divide exists in directing the growth of the creative industries. This paper aims to examine how such a socio-spatial divide contributes to the local creative industry development in Semarang and Kudus batik clusters. Explanatory sequential mixed methods approach covering a quantitative approach followed by a qualitative approach is chosen to understand better the interplay between tangible and intangible variables in the local batik clusters. Surveys on secondary data taken from the government statistics and reports, previous studies, and media exposures are completed in the former approach to identify clustering pattern of the local batik industry and the local embeddedness factors which have shaped the existing business environment. In-depth interviews, content analysis, and field observations are engaged in the latter approach to explore reciprocal relationships between the elements of social capital and the local batik cluster development. The result demonstrates that particular social ties have determined the forms of spatial proximity manifested in forward and backward business linkages. Trust, shared norms, and inherited traditions are the key social capital attributes that lead to such a socio-spatial divide. Therefore, the intermediating roles of the bridging actors are necessary to encouraging cooperation among the participating stakeholders for a better cluster development.

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models

USGS Publications Warehouse

Sun, Catherine C.; Fuller, Angela K.; Royle, J. Andrew

2014-01-01

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug-prescribing pattern in Taiwan

PubMed Central

2011-01-01

Background Geographic Information Systems (GIS) combined with spatial analytical methods could be helpful in examining patterns of drug use. Little attention has been paid to geographic variation of cardiovascular prescription use in Taiwan. The main objective was to use local spatial association statistics to test whether or not the cardiovascular medication-prescribing pattern is homogenous across 352 townships in Taiwan. Methods The statistical methods used were the global measures of Moran's I and Local Indicators of Spatial Association (LISA). While Moran's I provides information on the overall spatial distribution of the data, LISA provides information on types of spatial association at the local level. LISA statistics can also be used to identify influential locations in spatial association analysis. The major classes of prescription cardiovascular drugs were taken from Taiwan's National Health Insurance Research Database (NHIRD), which has a coverage rate of over 97%. The dosage of each prescription was converted into defined daily doses to measure the consumption of each class of drugs. Data were analyzed with ArcGIS and GeoDa at the township level. Results The LISA statistics showed an unusual use of cardiovascular medications in the southern townships with high local variation. Patterns of drug use also showed more low-low spatial clusters (cold spots) than high-high spatial clusters (hot spots), and those low-low associations were clustered in the rural areas. Conclusions The cardiovascular drug prescribing patterns were heterogeneous across Taiwan. In particular, a clear pattern of north-south disparity exists. Such spatial clustering helps prioritize the target areas that require better education concerning drug use. PMID:21609462

[Spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in 3 provinces in southwestern China, 2001-2012].

PubMed

Wang, L X; Yang, B; Yan, M Y; Tang, Y Q; Liu, Z C; Wang, R Q; Li, S; Ma, L; Kan, B

2017-11-10

Objective: To analyze the spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in Yunnan, Guizhou and Guangxi provinces in southwestern China in recent years. Methods: The incidence data of typhoid and paratyphoid fever cases at county level in 3 provinces during 2001-2012 were collected from China Information System for Diseases Control and Prevention and analyzed by the methods of descriptive epidemiology and geographic informatics. And the map showing the spatial and temporal clustering characters of typhoid and paratyphoid fever cases in three provinces was drawn. SaTScan statistics was used to identify the typhoid and paratyphoid fever clustering areas of three provinces in each year from 2001 to 2012. Results: During the study period, the reported cases of typhoid and paratyphoid fever declined with year. The reported incidence decreased from 30.15 per 100 000 in 2001 to 10.83 per 100 000 in 2006(annual incidence 21.12 per 100 000); while during 2007-2012, the incidence became stable, ranging from 4.75 per 100 000 to 6.83 per 100 000 (annual incidence 5.73 per 100 000). The seasonal variation of the incidence was consistent in three provinces, with majority of cases occurred in summer and autumn. The spatial and temporal clustering of typhoid and paratyphoid fever was demonstrated by the incidence map. Most high-incidence counties were located in a zonal area extending from Yuxi of Yunnan to Guiyang of Guizhou, but were concentrated in Guilin in Guangxi. Temporal and spatial scan statistics identified the positional shifting of class Ⅰ clustering area from Guizhou to Yunnan. Class Ⅰ clustering area was located around the central and western areas (Zunyi and Anshun) of Guizhou during 2001-2003, and moved to the central area of Yunnan during 2004-2012. Conclusion: Spatial and temporal clustering of typhoid and paratyphoid fever existed in the endemic areas of southwestern China, and the clustering area covered a zone connecting the central areas of Guizhou and Yunnan. From 2004 to 2012, the most important clustering area shifted from Guizhou to Yunnan. Findings from this study provided evidence for the identifying key areas for typhoid and paratyphoid fever control and prevention and allocate health resources.

Dynamic fractals in spatial evolutionary games

NASA Astrophysics Data System (ADS)

Kolotev, Sergei; Malyutin, Aleksandr; Burovski, Evgeni; Krashakov, Sergei; Shchur, Lev

2018-06-01

We investigate critical properties of a spatial evolutionary game based on the Prisoner's Dilemma. Simulations demonstrate a jump in the component densities accompanied by drastic changes in average sizes of the component clusters. We argue that the cluster boundary is a random fractal. Our simulations are consistent with the fractal dimension of the boundary being equal to 2, and the cluster boundaries are hence asymptotically space filling as the system size increases.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

PubMed Central

2010-01-01

Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters. PMID:21034451

The balance between keystone clustering and bed roughness in experimental step-pool stabilization

NASA Astrophysics Data System (ADS)

Johnson, J. P.

2016-12-01

Predicting how mountain channels will respond to environmental perturbations such as floods requires an improved quantitative understanding of morphodynamic feedbacks among bed topography, surface grain size and sediment sorting. In boulder-rich gravel streams, transport and sorting often lead to the development of step pool morphologies, which are expressed both in bed topography and coarse grain clustering. Bed stability is difficult to measure, and is sometimes inferred from the presence of step pools. I use scaled flume experiments to explore feedbacks among surface grain sizes, coarse grain clustering, bed roughness and hydraulic roughness during progressive bed stabilization and over a range of sediment transport rates. While grain clusters are sometimes identified by subjective interpretation, I quantify the degree of coarse surface grain clustering using spatial statistics, including a novel normalization of Ripley's K function. This approach is objective and provides information on the strength of clustering over a range of length scales. Flume experiments start with an initial bed surface with a broad grain size distribution and spatially random positions. Flow causes the bed surface to progressively stabilize in response to erosion, surface coarsening, roughening and grain reorganization. At 95% confidence, many but not all beds stabilized with coarse grains becoming more clustered than complete spatial randomness (CSR). I observe a tradeoff between topographic roughness and clustering. Beds that stabilized with higher degrees of coarse-grain clustering were topographically smoother, and vice-versa. Initial conditions influenced the degree of clustering at stability: Beds that happened to have fewer initial coarse grains had more coarse grain reorganization during stabilization, leading to more clustering. Finally, regressions demonstrate that clustering statistics actually predict hydraulic roughness significantly better than does D84 (the size at which 84% of grains are smaller). In the experimental data, the spatial organization of surface grains is a stronger control on flow characteristics than the size of surface grains.

Exploring the relation between spatial configuration of buildings and remotely sensed temperatures

NASA Astrophysics Data System (ADS)

Myint, S. W.; Zheng, B.; Kaplan, S.; Huang, H.

2013-12-01

While the relationship between fractional cover of buildings and the UHI has been well studied, relationships of how spatial arrangements (e.g., clustered, dispersed) of buildings influence urban warming are not well understood. Since a diversity of spatial patterns can be observed under the same percentage of buildings cover, it is of great interest and importance to investigate the amount of variation in certain urban thermal feature such as surface temperature that is accounted for by the inclusion of spatial arrangement component. The various spatial arrangements of buildings cover can give rise to different urban thermal behaviors that may not be uncovered with the information of buildings fraction only, but can be captured to some extent using spatial analysis. The goal of this study is to examine how spatial arrangements of buildings influence and shape surface temperature in different urban settings. The study area selected is the Las-Vegas metropolitan area in Nevada, located in the Mojave Desert. An object-oriented approach was used to identify buildings using a Geoeye-1 image acquired on October 12, 2011. A spatial autocorrelation technique (i.e., Moran's I) that can measure spatial pattern (clustered, dispersed) was used to determine spatial configuration of buildings. A daytime temperature layer in degree Celsius, generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, was integrated with Moran's I values of building cover and building fractions to achieve the goals set in the study. To avoid uncertainty and properly evaluate if spatial pattern of buildings has an impact on urban warming, the relation between Moran's I values and surface temperatures was observed at different levels according to their fractions (e.g., 0-0.1, 0.5-0.6, 0.9-1). There is a negative correlation exists between spatial pattern of buildings and surface temperatures implying that dispersed building arrangements elevate surface temperatures more severely than clustered buildings. This suggests that more clustered buildings have less impact on the urban heat island (UHI) effect. We conclude that having buildings as clustered as possible can be expected to protect the settlements from increased heat island effects, reduce pollution, and preserve the hydrological systems.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

Probabilistic cluster labeling of imagery data

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1980-01-01

The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.

Detecting space-time cancer clusters using residential histories

NASA Astrophysics Data System (ADS)

Jacquez, Geoffrey M.; Meliker, Jaymie R.

2007-04-01

Methods for analyzing geographic clusters of disease typically ignore the space-time variability inherent in epidemiologic datasets, do not adequately account for known risk factors (e.g., smoking and education) or covariates (e.g., age, gender, and race), and do not permit investigation of the latency window between exposure and disease. Our research group recently developed Q-statistics for evaluating space-time clustering in cancer case-control studies with residential histories. This technique relies on time-dependent nearest neighbor relationships to examine clustering at any moment in the life-course of the residential histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness, each individual's probability of being a case is instead based on his/her risk factors and covariates. Case-control clusters will be presented using residential histories of 220 bladder cancer cases and 440 controls in Michigan. In preliminary analyses of this dataset, smoking, age, gender, race and education were sufficient to explain the majority of the clustering of residential histories of the cases. Clusters of unexplained risk, however, were identified surrounding the business address histories of 10 industries that emit known or suspected bladder cancer carcinogens. The clustering of 5 of these industries began in the 1970's and persisted through the 1990's. This systematic approach for evaluating space-time clustering has the potential to generate novel hypotheses about environmental risk factors. These methods may be extended to detect differences in space-time patterns of any two groups of people, making them valuable for security intelligence and surveillance operations.

Multiple imputation of missing fMRI data in whole brain analysis

PubMed Central

Vaden, Kenneth I.; Gebregziabher, Mulugeta; Kuchinsky, Stefanie E.; Eckert, Mark A.

2012-01-01

Whole brain fMRI analyses rarely include the entire brain because of missing data that result from data acquisition limits and susceptibility artifact, in particular. This missing data problem is typically addressed by omitting voxels from analysis, which may exclude brain regions that are of theoretical interest and increase the potential for Type II error at cortical boundaries or Type I error when spatial thresholds are used to establish significance. Imputation could significantly expand statistical map coverage, increase power, and enhance interpretations of fMRI results. We examined multiple imputation for group level analyses of missing fMRI data using methods that leverage the spatial information in fMRI datasets for both real and simulated data. Available case analysis, neighbor replacement, and regression based imputation approaches were compared in a general linear model framework to determine the extent to which these methods quantitatively (effect size) and qualitatively (spatial coverage) increased the sensitivity of group analyses. In both real and simulated data analysis, multiple imputation provided 1) variance that was most similar to estimates for voxels with no missing data, 2) fewer false positive errors in comparison to mean replacement, and 3) fewer false negative errors in comparison to available case analysis. Compared to the standard analysis approach of omitting voxels with missing data, imputation methods increased brain coverage in this study by 35% (from 33,323 to 45,071 voxels). In addition, multiple imputation increased the size of significant clusters by 58% and number of significant clusters across statistical thresholds, compared to the standard voxel omission approach. While neighbor replacement produced similar results, we recommend multiple imputation because it uses an informed sampling distribution to deal with missing data across subjects that can include neighbor values and other predictors. Multiple imputation is anticipated to be particularly useful for 1) large fMRI data sets with inconsistent missing voxels across subjects and 2) addressing the problem of increased artifact at ultra-high field, which significantly limit the extent of whole brain coverage and interpretations of results. PMID:22500925

Global survey of star clusters in the Milky Way. VI. Age distribution and cluster formation history

NASA Astrophysics Data System (ADS)

Piskunov, A. E.; Just, A.; Kharchenko, N. V.; Berczik, P.; Scholz, R.-D.; Reffert, S.; Yen, S. X.

2018-06-01

Context. The all-sky Milky Way Star Clusters (MWSC) survey provides uniform and precise ages, along with other relevant parameters, for a wide variety of clusters in the extended solar neighbourhood. Aims: In this study we aim to construct the cluster age distribution, investigate its spatial variations, and discuss constraints on cluster formation scenarios of the Galactic disk during the last 5 Gyrs. Methods: Due to the spatial extent of the MWSC, we have considered spatial variations of the age distribution along galactocentric radius RG, and along Z-axis. For the analysis of the age distribution we used 2242 clusters, which all lie within roughly 2.5 kpc of the Sun. To connect the observed age distribution to the cluster formation history we built an analytical model based on simple assumptions on the cluster initial mass function and on the cluster mass-lifetime relation, fit it to the observations, and determined the parameters of the cluster formation law. Results: Comparison with the literature shows that earlier results strongly underestimated the number of evolved clusters with ages t ≳ 100 Myr. Recent studies based on all-sky catalogues agree better with our data, but still lack the oldest clusters with ages t ≳ 1 Gyr. We do not observe a strong variation in the age distribution along RG, though we find an enhanced fraction of older clusters (t > 1 Gyr) in the inner disk. In contrast, the distribution strongly varies along Z. The high altitude distribution practically does not contain clusters with t < 1 Gyr. With simple assumptions on the cluster formation history, the cluster initial mass function and the cluster lifetime we can reproduce the observations. The cluster formation rate and the cluster lifetime are strongly degenerate, which does not allow us to disentangle different formation scenarios. In all cases the cluster formation rate is strongly declining with time, and the cluster initial mass function is very shallow at the high mass end.

Neural network-based multiple robot simultaneous localization and mapping.

PubMed

Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

2011-12-01

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

Could the outcome of the 2016 US elections have been predicted from past voting patterns?

NASA Astrophysics Data System (ADS)

Schmitz, Peter M. U.; Holloway, Jennifer P.; Dudeni-Tlhone, Nontembeko; Ntlangu, Mbulelo B.; Koen, Renee

2018-05-01

In South Africa, a team of analysts has for some years been using statistical techniques to predict election outcomes during election nights in South Africa. The prediction method involves using statistical clusters based on past voting patterns to predict final election outcomes, using a small number of released vote counts. With the US presidential elections in November 2016 hitting the global media headlines during the time period directly after successful predictions were done for the South African elections, the team decided to investigate adapting their meth-od to forecast the final outcome in the US elections. In particular, it was felt that the time zone differences between states would affect the time at which results are released and thereby provide a window of opportunity for doing election night prediction using only the early results from the eastern side of the US. Testing the method on the US presidential elections would have two advantages: it would determine whether the core methodology could be generalised, and whether it would work to include a stronger spatial element in the modelling, since the early results released would be spatially biased due to time zone differences. This paper presents a high-level view of the overall methodology and how it was adapted to predict the results of the US presidential elections. A discussion on the clustering of spatial units within the US is also provided and the spatial distribution of results together with the Electoral College prediction results from both a `test-run' and the final 2016 presidential elections are given and analysed.

The biogeodynamics of microbial landscapes

NASA Astrophysics Data System (ADS)

Battin, T. J.; Hödl, I.; Bertuzzo, E.; Mari, L.; Suweis, S. S.; Rinaldo, A.

2011-12-01

Spatial configuration is fundamental in defining the structural and functional properties of biological systems. Biofilms, surface-attached and matrix-enclosed microorganisms, are a striking example of spatial organisation. Coupled biotic and abiotic processes shape the spatial organisation across scales of the landscapes formed by these benthic biofilms in streams and rivers. Experimenting with such biofilms in streams, we found that, depending on the streambed topography and the related hydrodynamic microenvironment, biofilm landscapes form increasingly diverging spatial patterns as they grow. Strikingly, however, cluster size distributions tend to converge even in contrasting hydrodynamic microenvironments. To reproduce the observed cluster size distributions we used a continuous, size-structured population model. The model accounts for the formation, growth, erosion and merging of biofilm clusters. Our results suggest not only that hydrodynamic forcing induce the diverging patterning of the microbial landscape, but also that microorganisms have developed strategies to equally exploit spatial resources independently of the physical structure of the microenvironment where they live.

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

Patterns of population structure for inshore bottlenose dolphins along the eastern United States.

PubMed

Richards, Vincent P; Greig, Thomas W; Fair, Patricia A; McCulloch, Stephen D; Politz, Christine; Natoli, Ada; Driscoll, Carlos A; Hoelzel, A Rus; David, Victor; Bossart, Gregory D; Lopez, Jose V

2013-01-01

Globally distributed, the bottlenose dolphin (Tursiops truncatus) is found in a range of offshore and coastal habitats. Using 15 microsatellite loci and mtDNA control region sequences, we investigated patterns of genetic differentiation among putative populations along the eastern US shoreline (the Indian River Lagoon, Florida, and Charleston Harbor, South Carolina) (microsatellite analyses: n = 125, mtDNA analyses: n = 132). We further utilized the mtDNA to compare these populations with those from the Northwest Atlantic, Gulf of Mexico, and Caribbean. Results showed strong differentiation among inshore, alongshore, and offshore habitats (ФST = 0.744). In addition, Bayesian clustering analyses revealed the presence of 2 genetic clusters (populations) within the 250 km Indian River Lagoon. Habitat heterogeneity is likely an important force diversifying bottlenose dolphin populations through its influence on social behavior and foraging strategy. We propose that the spatial pattern of genetic variation within the lagoon reflects both its steep longitudinal transition of climate and also its historical discontinuity and recent connection as part of Intracoastal Waterway development. These findings have important management implications as they emphasize the role of habitat and the consequence of its modification in shaping bottlenose dolphin population structure and highlight the possibility of multiple management units existing in discrete inshore habitats along the entire eastern US shoreline.

Patterns of Population Structure for Inshore Bottlenose Dolphins along the Eastern United States

PubMed Central

2013-01-01

Globally distributed, the bottlenose dolphin (Tursiops truncatus) is found in a range of offshore and coastal habitats. Using 15 microsatellite loci and mtDNA control region sequences, we investigated patterns of genetic differentiation among putative populations along the eastern US shoreline (the Indian River Lagoon, Florida, and Charleston Harbor, South Carolina) (microsatellite analyses: n = 125, mtDNA analyses: n = 132). We further utilized the mtDNA to compare these populations with those from the Northwest Atlantic, Gulf of Mexico, and Caribbean. Results showed strong differentiation among inshore, alongshore, and offshore habitats (ФST = 0.744). In addition, Bayesian clustering analyses revealed the presence of 2 genetic clusters (populations) within the 250 km Indian River Lagoon. Habitat heterogeneity is likely an important force diversifying bottlenose dolphin populations through its influence on social behavior and foraging strategy. We propose that the spatial pattern of genetic variation within the lagoon reflects both its steep longitudinal transition of climate and also its historical discontinuity and recent connection as part of Intracoastal Waterway development. These findings have important management implications as they emphasize the role of habitat and the consequence of its modification in shaping bottlenose dolphin population structure and highlight the possibility of multiple management units existing in discrete inshore habitats along the entire eastern US shoreline. PMID:24129993

The impact of landscape disturbance on spatial genetic structure in the Guanacaste tree, Enterolobium cyclocarpum (Fabaceae).

PubMed

Gonzales, Eva; Hamrick, James L; Smouse, Peter E; Trapnell, Dorset W; Peakall, Rod

2010-01-01

We examined spatial genetic structure (SGS) in Enterolobium cyclocarpum (the Guanacaste tree), a dominant tree of Central American dry forests in 4 sites in Guanacaste Province, Costa Rica. In disturbed dry forest sites (e.g., pastures), E. cyclocarpum is primarily dispersed by cattle and horses, whose movements are restricted by pasture boundaries. The study sites varied in tree densities and disturbance. Allozyme analyses of adult trees demonstrated significant levels of SGS in 3 of 4 sites. SGS was primarily due to clusters of young adults located along seasonal streams, rocky areas, and in abandoned pastures. SGS was highest in the first distance class in the least disturbed population, which also had the lowest density of large adults. Low, but significant SGS characterized the site with the highest number of large adults located in individual pastures. The semiurban site, had no clusters of young adults and, probably as a result, failed to exhibit SGS. Our results demonstrate that disturbance can strongly influence SGS patterns and are consistent with a landscape model in which the location of potential recruitment sites, restricted seed disperser movements, and the number and location of maternal individuals dictate the level and pattern of SGS.

Pattern formation in a model for mountain pine beetle dispersal: linking model predictions to data.

PubMed

Strohm, S; Tyson, R C; Powell, J A

2013-10-01

Pattern formation occurs in a wide range of biological systems. This pattern formation can occur in mathematical models because of diffusion-driven instability or due to the interaction between reaction, diffusion, and chemotaxis. In this paper, we investigate the spatial pattern formation of attack clusters in a system for Mountain Pine Beetle. The pattern formation (aggregation) of the Mountain Pine Beetle in order to attack susceptible trees is crucial for their survival and reproduction. We use a reaction-diffusion equation with chemotaxis to model the interaction between Mountain Pine Beetle, Mountain Pine Beetle pheromones, and susceptible trees. Mathematical analysis is utilized to discover the spacing in-between beetle attacks on the susceptible landscape. The model predictions are verified by analysing aerial detection survey data of Mountain Pine Beetle Attack from the Sawtooth National Recreation Area. We find that the distance between Mountain Pine Beetle attack clusters predicted by our model closely corresponds to the observed attack data in the Sawtooth National Recreation Area. These results clarify the spatial mechanisms controlling the transition from incipient to epidemic populations and may lead to control measures which protect forests from Mountain Pine Beetle outbreak.

The sparkling Universe: clustering of voids and void clumps

NASA Astrophysics Data System (ADS)

Lares, Marcelo; Ruiz, Andrés N.; Luparello, Heliana E.; Ceccarelli, Laura; Garcia Lambas, Diego; Paz, Dante J.

2017-07-01

We analyse the clustering of cosmic voids using a numerical simulation and the main galaxy sample from the Sloan Digital Sky Survey. We take into account the classification of voids into two types that resemble different evolutionary modes: those with a rising integrated density profile (void-in-void mode or R-type) and voids with shells (void-in-cloud mode or S-type). The results show that voids of the same type have stronger clustering than the full sample. We use the correlation analysis to define void clumps, associations with at least two voids separated by a distance of at most the mean void separation. In order to study the spatial configuration of void clumps, we compute the minimal spanning tree and analyse their multiplicity, maximum length and elongation parameter. We further study the dynamics of the smaller sphere that enclose all the voids in each clump. Although the global densities of void clumps are different according to their member-void types, the bulk motions of these spheres are remarkably lower than those of randomly placed spheres with the same radius distribution. In addition, the coherence of pairwise void motions does not strongly depend on whether voids belong to the same clump. Void clumps are useful to analyse the large-scale flows around voids, since voids embedded in large underdense regions are mostly in the void-in-void regime, where the expansion of the larger region produces the separation of voids. Similarly, voids around overdense regions form clumps that are in collapse, as reflected in the relative velocities of voids that are mostly approaching.

Applications of spatial statistical network models to stream data

USGS Publications Warehouse

Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal

2014-01-01

Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults.

PubMed

Gartner, Danielle R; Taber, Daniel R; Hirsch, Jana A; Robinson, Whitney R

2016-04-01

Although obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race and/or ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered, and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence. Data from the Centers for Disease Control and Prevention's 2013 Behavioral Risk Factor Surveillance System were used to calculate state-specific obesity prevalence and gender inequality measures. Global and local Moran's indices were calculated to determine spatial autocorrelation. Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score = 4.89, P-value < .001). Local Moran's indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the United States. Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatio-Temporal Clustering of Monitoring Network

NASA Astrophysics Data System (ADS)

Hussain, I.; Pilz, J.

2009-04-01

Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters existed. Soltani and Modarres (2006) classified the sites by using only average rainfall of sites, they did not consider time replications and spatial coordinates. Kerby et.al (2007) purposed spatial clustering method based on likelihood. They took account of the geographic locations through the variance covariance matrix. Their purposed method works like hierarchical clustering methods. Moreovere, it is inappropiriate for time replication data and could not perform well for large number of sites. Tuia.et.al (2008) used scan statistics for identifying spatio-temporal clusters for fire sequences in the Tuscany region in Italy. The scan statistics clustering method was developed by Kulldorff et al. (1997) to detect spatio-temporal clusters in epidemiology and assessing their significance. The purposed scan statistics method is used only for univariate discrete stochastic random variables. In this paper we make use of a very simple approach for spatio-temporal clustering which can create separable and homogeneous clusters. Most of the clustering methods are based on Euclidean distances. It is well known that geographic coordinates are spherical coordinates and estimating Euclidean distances from spherical coordinates is inappropriate. As a transformation from geographic coordinates to rectangular (D-plane) coordinates we use the Lambert projection method. The partition around medoids clustering method is incorporated on the data including D-plane coordinates. Ordinary kriging is taken as validity measure for the precipitation data. The kriging results for clusters are more accurate and have less variation compared to complete monitoring network precipitation data. References Casto.V.E and Murray.A.T (1997). Spatial Clustering with Data Mining with Genetic Algorithms. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.8573 Kaufman.L and Rousseeuw.P.J (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley series of Probability and Mathematical Statistics, New York. Kulldorf.M (1997). A spatial scan statistic. Commun. Stat.-Theor. Math. 26(6), 1481-1496 Kerby. A , Marx. D, Samal. A and Adamchuck. V. (2007). Spatial Clustering Using the Likelihood Function. Seventh IEEE International Conference on Data Mining - Workshops Steinhaus.H (1956). Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci., C1. III vol IV:801- 804 Snyder, J. P. (1987). Map Projection: A Working Manual. U. S. Geological Survey Professional Paper 1395. Washington, DC: U. S. Government Printing Office, pp. 104-110 Sap.M.N and Awan. A.M (2005). Finding Spatio-Temporal Patterns in Climate Data Using Clustering. Proceedings of the International Conference on Cyberworlds (CW'05) Soltani.S and Modarres.R (2006). Classification of Spatio -Temporal Pattern of Rainfall in Iran: Using Hierarchical and Divisive Cluster Analysis. Journal of Spatial Hydrology Vol.6, No.2 Tuia.D, Ratle.F, Lasaponara.R, Telesca.L and Kanevski.M (2008). Scan Statistics Analysis for Forest Fire Clusters. Commun. in Nonlinear science and numerical simulation 13,1689-1694.

Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003-2015): Implications for Prevention and Control Policies.

PubMed

Zhu, Bin; Liu, Jinlin; Fu, Yang; Zhang, Bo; Mao, Ying

2018-04-02

Viral hepatitis, as one of the most serious notifiable infectious diseases in China, takes heavy tolls from the infected and causes a severe economic burden to society, yet few studies have systematically explored the spatio-temporal epidemiology of viral hepatitis in China. This study aims to explore, visualize and compare the epidemiologic trends and spatial changing patterns of different types of viral hepatitis (A, B, C, E and unspecified, based on the classification of CDC) at the provincial level in China. The growth rates of incidence are used and converted to box plots to visualize the epidemiologic trends, with the linear trend being tested by chi-square linear by linear association test. Two complementary spatial cluster methods are used to explore the overall agglomeration level and identify spatial clusters: spatial autocorrelation analysis (measured by global and local Moran's I) and space-time scan analysis. Based on the spatial autocorrelation analysis, the hotspots of hepatitis A remain relatively stable and gradually shrunk, with Yunnan and Sichuan successively moving out the high-high (HH) cluster area. The HH clustering feature of hepatitis B in China gradually disappeared with time. However, the HH cluster area of hepatitis C has gradually moved towards the west, while for hepatitis E, the provincial units around the Yangtze River Delta region have been revealing HH cluster features since 2005. The space-time scan analysis also indicates the distinct spatial changing patterns of different types of viral hepatitis in China. It is easy to conclude that there is no one-size-fits-all plan for the prevention and control of viral hepatitis in all the provincial units. An effective response requires a package of coordinated actions, which should vary across localities regarding the spatial-temporal epidemic dynamics of each type of virus and the specific conditions of each provincial unit.

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

PubMed Central

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; MacEachren, Alan M

2008-01-01

Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. Conclusion The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. Method We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit. PMID:18992163

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

PubMed

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

2008-11-07

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

NASA Astrophysics Data System (ADS)

Xiao, F.

2018-04-01

In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

Assessing the significance of pedobarographic signals using random field theory.

PubMed

Pataky, Todd C

2008-08-07

Traditional pedobarographic statistical analyses are conducted over discrete regions. Recent studies have demonstrated that regionalization can corrupt pedobarographic field data through conflation when arbitrary dividing lines inappropriately delineate smooth field processes. An alternative is to register images such that homologous structures optimally overlap and then conduct statistical tests at each pixel to generate statistical parametric maps (SPMs). The significance of SPM processes may be assessed within the framework of random field theory (RFT). RFT is ideally suited to pedobarographic image analysis because its fundamental data unit is a lattice sampling of a smooth and continuous spatial field. To correct for the vast number of multiple comparisons inherent in such data, recent pedobarographic studies have employed a Bonferroni correction to retain a constant family-wise error rate. This approach unfortunately neglects the spatial correlation of neighbouring pixels, so provides an overly conservative (albeit valid) statistical threshold. RFT generally relaxes the threshold depending on field smoothness and on the geometry of the search area, but it also provides a framework for assigning p values to suprathreshold clusters based on their spatial extent. The current paper provides an overview of basic RFT concepts and uses simulated and experimental data to validate both RFT-relevant field smoothness estimations and RFT predictions regarding the topological characteristics of random pedobarographic fields. Finally, previously published experimental data are re-analysed using RFT inference procedures to demonstrate how RFT yields easily understandable statistical results that may be incorporated into routine clinical and laboratory analyses.

Sampling procedures for inventory of commercial volume tree species in Amazon Forest.

PubMed

Netto, Sylvio P; Pelissari, Allan L; Cysneiros, Vinicius C; Bonazza, Marcelo; Sanquetta, Carlos R

2017-01-01

The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with diameters equal to or higher than 40 cm were measured in 1,355 plots. Species with different spatial patterns were selected and sampled with simple random sampling, systematic sampling, linear cluster sampling and adaptive cluster sampling, whereby the accuracy of the volumetric estimation and presence of zero-plots were evaluated. The sampling procedures applied to species were affected by the low density of trees and the large number of zero-plots, wherein the adaptive clusters allowed concentrating the sampling effort in plots with trees and, thus, agglutinating more representative samples to estimate the commercial volume.

Decreasing child mortality, spatial clustering and decreasing disparity in North-Western Burkina Faso.

PubMed

Becher, Heiko; Müller, Olaf; Dambach, Peter; Gabrysch, Sabine; Niamba, Louis; Sankoh, Osman; Simboro, Seraphin; Schoeps, Anja; Stieglbauer, Gabriele; Yé, Yazoume; Sié, Ali

2016-04-01

Within relatively small areas, there exist high spatial variations of mortality between villages. In rural Burkina Faso, with data from 1993 to 1998, clusters of particularly high child mortality were identified in the population of the Nouna Health and Demographic Surveillance System (HDSS), a member of the INDEPTH Network. In this paper, we report child mortality with respect to temporal trends, spatial clustering and disparity in this HDSS from 1993 to 2012. Poisson regression was used to describe village-specific child mortality rates and time trends in mortality. The spatial scan statistic was used to identify villages or village clusters with higher child mortality. Clustering of mortality in the area is still present, but not as strong as before. The disparity of child mortality between villages has decreased. The decrease occurred in the context of an overall halving of child mortality in the rural area of Nouna HDSS between 1993 and 2012. Extrapolated to the Millennium Development Goals target period 1990-2015, this yields an estimated reduction of 54%, which is not too far off the aim of a two-thirds reduction. © 2016 John Wiley & Sons Ltd.

Large-area imaging reveals biologically driven non-random spatial patterns of corals at a remote reef

NASA Astrophysics Data System (ADS)

Edwards, Clinton B.; Eynaud, Yoan; Williams, Gareth J.; Pedersen, Nicole E.; Zgliczynski, Brian J.; Gleason, Arthur C. R.; Smith, Jennifer E.; Sandin, Stuart A.

2017-12-01

For sessile organisms such as reef-building corals, differences in the degree of dispersion of individuals across a landscape may result from important differences in life-history strategies or may reflect patterns of habitat availability. Descriptions of spatial patterns can thus be useful not only for the identification of key biological and physical mechanisms structuring an ecosystem, but also by providing the data necessary to generate and test ecological theory. Here, we used an in situ imaging technique to create large-area photomosaics of 16 plots at Palmyra Atoll, central Pacific, each covering 100 m2 of benthic habitat. We mapped the location of 44,008 coral colonies and identified each to the lowest taxonomic level possible. Using metrics of spatial dispersion, we tested for departures from spatial randomness. We also used targeted model fitting to explore candidate processes leading to differences in spatial patterns among taxa. Most taxa were clustered and the degree of clustering varied by taxon. A small number of taxa did not significantly depart from randomness and none revealed evidence of spatial uniformity. Importantly, taxa that readily fragment or tolerate stress through partial mortality were more clustered. With little exception, clustering patterns were consistent with models of fragmentation and dispersal limitation. In some taxa, dispersion was linearly related to abundance, suggesting density dependence of spatial patterning. The spatial patterns of stony corals are non-random and reflect fundamental life-history characteristics of the taxa, suggesting that the reef landscape may, in many cases, have important elements of spatial predictability.

Spatial distribution of end-stage renal disease (ESRD) and social inequalities in mixed urban and rural areas: a study in the Bretagne administrative region of France.

PubMed

Kihal-Talantikite, Wahida; Deguen, Séverine; Padilla, Cindy; Siebert, Muriel; Couchoud, Cécile; Vigneau, Cécile; Bayat, Sahar

2015-02-01

Several studies have investigated the implication of biological and environmental factors on geographic variations of end-stage renal disease (ESRD) incidence at large area scales, but none of them assessed the implication of neighbourhood characteristics (healthcare supply, socio-economic level and urbanization degree) on spatial repartition of ESRD. We evaluated the spatial implications of adjustment for neighbourhood characteristics on the spatial distribution of ESRD incidence at the smallest geographic unit in France. All adult patients living in Bretagne and beginning renal replacement therapy during the 2004-09 period were included. Their residential address was geocoded at the census block level. Each census block was characterized by socio-economic deprivation index, healthcare supply and rural/urban typology. Using a spatial scan statistic, we examined whether there were significant clusters of high risk of ESRD incidence. The ESRD incidence was non-randomly spatially distributed, with a cluster of high risk in the western Bretagne region (relative risk, RR = 1.28, P-value = 0.0003). Adjustment for sex, age and neighbourhood characteristics induced cluster shifts. After these adjustments, a significant cluster (P = 0.013) persisted. Our spatial analysis of ESRD incidence at a fine scale, across a mixed rural/urban area, indicated that, beyond age and sex, neighbourhood characteristics explained a great part of spatial distribution of ESRD incidence. However, to better understand spatial variation of ESRD incidence, it would be necessary to research and adjust for other determinants of ESRD.

Invasive advance of an advantageous mutation: nucleation theory.

PubMed

O'Malley, Lauren; Basham, James; Yasi, Joseph A; Korniss, G; Allstadt, Andrew; Caraco, Thomas

2006-12-01

For sedentary organisms with localized reproduction, spatially clustered growth drives the invasive advance of a favorable mutation. We model competition between two alleles where recurrent mutation introduces a genotype with a rate of local propagation exceeding the resident's rate. We capture ecologically important properties of the rare invader's stochastic dynamics by assuming discrete individuals and local neighborhood interactions. To understand how individual-level processes may govern population patterns, we invoke the physical theory for nucleation of spatial systems. Nucleation theory discriminates between single-cluster and multi-cluster dynamics. A sufficiently low mutation rate, or a sufficiently small environment, generates single-cluster dynamics, an inherently stochastic process; a favorable mutation advances only if the invader cluster reaches a critical radius. For this mode of invasion, we identify the probability distribution of waiting times until the favored allele advances to competitive dominance, and we ask how the critical cluster size varies as propagation or mortality rates vary. Increasing the mutation rate or system size generates multi-cluster invasion, where spatial averaging produces nearly deterministic global dynamics. For this process, an analytical approximation from nucleation theory, called Avrami's Law, describes the time-dependent behavior of the genotype densities with remarkable accuracy.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

PubMed

Lin, Pei-Sheng; Kung, Yi-Hung; Clayton, Murray

2016-12-01

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan. © 2016, The International Biometric Society.

Wildfire cluster detection using space-time scan statistics

NASA Astrophysics Data System (ADS)

Tonini, M.; Tuia, D.; Ratle, F.; Kanevski, M.

2009-04-01

The aim of the present study is to identify spatio-temporal clusters of fires sequences using space-time scan statistics. These statistical methods are specifically designed to detect clusters and assess their significance. Basically, scan statistics work by comparing a set of events occurring inside a scanning window (or a space-time cylinder for spatio-temporal data) with those that lie outside. Windows of increasing size scan the zone across space and time: the likelihood ratio is calculated for each window (comparing the ratio "observed cases over expected" inside and outside): the window with the maximum value is assumed to be the most probable cluster, and so on. Under the null hypothesis of spatial and temporal randomness, these events are distributed according to a known discrete-state random process (Poisson or Bernoulli), which parameters can be estimated. Given this assumption, it is possible to test whether or not the null hypothesis holds in a specific area. In order to deal with fires data, the space-time permutation scan statistic has been applied since it does not require the explicit specification of the population-at risk in each cylinder. The case study is represented by Florida daily fire detection using the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product during the period 2003-2006. As result, statistically significant clusters have been identified. Performing the analyses over the entire frame period, three out of the five most likely clusters have been identified in the forest areas, on the North of the country; the other two clusters cover a large zone in the South, corresponding to agricultural land and the prairies in the Everglades. Furthermore, the analyses have been performed separately for the four years to analyze if the wildfires recur each year during the same period. It emerges that clusters of forest fires are more frequent in hot seasons (spring and summer), while in the South areas they are widely present along the whole year. The analysis of fires distribution to evaluate if they are statistically more frequent in some area or/and in some period of the year, can be useful to support fire management and to focus on prevention measures.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

PubMed

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Disaster declarations associated with bushfires, floods and storms in New South Wales, Australia between 2004 and 2014

PubMed Central

Sewell, T.; Stephens, R. E.; Dominey-Howes, D.; Bruce, E.; Perkins-Kirkpatrick, S.

2016-01-01

Australia regularly experiences disasters triggered by natural hazards and New South Wales (NSW) the most populous State is no exception. To date, no publically available spatial and temporal analyses of disaster declarations triggered by hazards (specifically, bushfires, floods and storms) in NSW have been undertaken and no studies have explored the relationship between disaster occurrence and socio-economic disadvantage. We source, collate and analyse data about bushfire, flood and storm disaster declarations between 2004 and 2014. Floods resulted in the most frequent type of disaster declaration. The greatest number of disaster declarations occurred in 2012–2013. Whilst no significant Spearman’s correlation exists between bushfire, flood and storm disaster declarations and the strength of the El Niño/Southern Oscillation (ENSO) phase, we observe that bushfire disaster declarations were much more common during El Niño, and flood disaster declarations were five times more common during La Niña phases. We identify a spatial cluster or ‘hot spot’ of disaster declarations in the northeast of the State that is also spatially coincident with 43% of the most socio-economically disadvantaged Local Government Areas in NSW. The results have implications for disaster risk management in the State. PMID:27819298

Admixture and gene flow from Russia in the recovering Northern European brown bear (Ursus arctos).

PubMed

Kopatz, Alexander; Eiken, Hans Geir; Aspi, Jouni; Kojola, Ilpo; Tobiassen, Camilla; Tirronen, Konstantin F; Danilov, Pjotr I; Hagen, Snorre B

2014-01-01

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise FST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia.

Admixture and Gene Flow from Russia in the Recovering Northern European Brown Bear (Ursus arctos)

PubMed Central

Kopatz, Alexander; Eiken, Hans Geir; Aspi, Jouni; Kojola, Ilpo; Tobiassen, Camilla; Tirronen, Konstantin F.; Danilov, Pjotr I.; Hagen, Snorre B.

2014-01-01

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise FST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia. PMID:24839968

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

PubMed Central

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence-based methods. Conclusions Appropriate homologous sequences are selected automatically and objectively by the index. Such sequence selection improved the performance of functional region prediction. As far as we know, this is the first approach in which spatial statistics have been applied to protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems. PMID:22643026

Geospatial clustering in sugar-sweetened beverage consumption among Boston youth.

PubMed

Tamura, Kosuke; Duncan, Dustin T; Athens, Jessica K; Bragg, Marie A; Rienti, Michael; Aldstadt, Jared; Scott, Marc A; Elbel, Brian

2017-09-01

The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13-19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.

Modulating STDP Balance Impacts the Dendritic Mosaic

PubMed Central

Iannella, Nicolangelo; Launey, Thomas

2017-01-01

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams. PMID:28649195

Timing of sea ice retreat can alter phytoplankton community structure in the western Arctic Ocean

NASA Astrophysics Data System (ADS)

Fujiwara, A.; Hirawake, T.; Suzuki, K.; Imai, I.; Saitoh, S.-I.

2014-04-01

This study assesses the response of phytoplankton assemblages to recent climate change, especially with regard to the shrinking of sea ice in the northern Chukchi Sea of the western Arctic Ocean. Distribution patterns of phytoplankton groups in the late summers of 2008-2010 were analysed based on HPLC pigment signatures and, the following four major algal groups were inferred via multiple regression and cluster analyses: prasinophytes, diatoms, haptophytes and dinoflagellates. A remarkable interannual difference in the distribution pattern of the groups was found in the northern basin area. Haptophytes dominated and dispersed widely in warm surface waters in 2008, whereas prasinophytes dominated in cold water in 2009 and 2010. A difference in the onset date of sea ice retreat was evident among years-the sea ice retreat in 2008 was 1-2 months earlier than in 2009 and 2010. The spatial distribution of early sea ice retreat matched the areas in which a shift in algal community composition was observed. Steel-Dwass's multiple comparison tests were used to assess the physical, chemical and biological parameters of the four clusters. We found a statistically significant difference in temperature between the haptophyte-dominated cluster and the other clusters, suggesting that the change in the phytoplankton communities was related to the earlier sea ice retreat in 2008 and the corollary increase in sea surface temperatures. Longer periods of open water during the summer, which are expected in the future, may affect food webs and biogeochemical cycles in the western Arctic due to shifts in phytoplankton community structure.

Exploring the Structure of Spatial Representations

PubMed Central

Madl, Tamas; Franklin, Stan; Chen, Ke; Trappl, Robert; Montaldi, Daniela

2016-01-01

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory. PMID:27347681

Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

PubMed

Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

2014-03-20

Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

Spatial analyses identify the geographic source of patients at a National Cancer Institute Comprehensive Cancer Center.

PubMed

Su, Shu-Chih; Kanarek, Norma; Fox, Michael G; Guseynova, Alla; Crow, Shirley; Piantadosi, Steven

2010-02-01

We examined the geographic distribution of patients to better understand the service area of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, a designated National Cancer Institute (NCI) comprehensive cancer center located in an urban center. Like most NCI cancer centers, the Sidney Kimmel Comprehensive Cancer Center serves a population beyond city limits. Urban cancer centers are expected to serve their immediate neighborhoods and to address disparities in access to specialty care. Our purpose was to learn the extent and nature of the cancer center service area. Statistical clustering of patient residence in the continental United States was assessed for all patients and by gender, cancer site, and race using SaTScan. Primary clusters detected for all cases and demographically and tumor-defined subpopulations were centered at Baltimore City and consisted of adjacent counties in Delaware, Pennsylvania, Virginia, West Virginia, New Jersey and New York, and the District of Columbia. Primary clusters varied in size by race, gender, and cancer site. Spatial analysis can provide insights into the populations served by urban cancer centers, assess centers' performance relative to their communities, and aid in developing a cancer center business plan that recognizes strengths, regional utility, and referral patterns. Today, 62 NCI cancer centers serve a quarter of the U.S. population in their immediate communities. From the Baltimore experience, we might project that the population served by these centers is actually more extensive and varies by patient characteristics, cancer site, and probably cancer center services offered.

Use of a spatial scan statistic to identify clusters of births occurring outside Ghanaian health facilities for targeted intervention.

PubMed

Bosomprah, Samuel; Dotse-Gborgbortsi, Winfred; Aboagye, Patrick; Matthews, Zoe

2016-11-01

To identify and evaluate clusters of births that occurred outside health facilities in Ghana for targeted intervention. A retrospective study was conducted using a convenience sample of live births registered in Ghanaian health facilities from January 1 to December 31, 2014. Data were extracted from the district health information system. A spatial scan statistic was used to investigate clusters of home births through a discrete Poisson probability model. Scanning with a circular spatial window was conducted only for clusters with high rates of such deliveries. The district was used as the geographic unit of analysis. The likelihood P value was estimated using Monte Carlo simulations. Ten statistically significant clusters with a high rate of home birth were identified. The relative risks ranged from 1.43 ("least likely" cluster; P=0.001) to 1.95 ("most likely" cluster; P=0.001). The relative risks of the top five "most likely" clusters ranged from 1.68 to 1.95; these clusters were located in Ashanti, Brong Ahafo, and the Western, Eastern, and Greater regions of Accra. Health facility records, geospatial techniques, and geographic information systems provided locally relevant information to assist policy makers in delivering targeted interventions to small geographic areas. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

A tale of two cities: The role of neighborhood socioeconomic status in spatial clustering of bystander CPR in Austin and Houston☆

PubMed Central

Root, Elisabeth Dowling; Gonzales, Louis; Persse, David E.; Hinchey, Paul R.; McNally, Bryan; Sasson, Comilla

2013-01-01

Background Despite evidence to suggest significant spatial variation in out-of-hospital cardiac arrest (OHCA) and bystander cardiopulmonary resuscitation (BCPR) rates, geographic information systems (GIS) and spatial analysis have not been widely used to understand the reasons behind this variation. This study employs spatial statistics to identify the location and extent of clusters of bystander CPR in Houston and Travis County, TX. Methods Data were extracted from the Cardiac Arrest Registry to Enhance Survival for two U.S. sites –Austin-Travis County EMS and the Houston Fire Department – between October 1, 2006 and December 31, 2009. Hierarchical logistic regression models were used to assess the relationship between income and racial/ethnic composition of a neighborhood and BCPR for OHCA and to adjust expected counts of BCPR for spatial cluster analysis. The spatial scan statistic was used to find the geographic extent of clusters of high and low BCPR. Results Results indicate spatial clusters of lower than expected BCPR rates in Houston. Compared to BCPR rates in the rest of the community, there was a circular area of 4.2 km radius where BCPR rates were lower than expected (RR = 0.62; p < 0.0001 and RR = 0.55; p = 0.037) which persist when adjusted for individual-level patient characteristics (RR = 0.34; p = 0.027) and neighborhood-level race (RR = 0.34; p = 0.034) and household income (RR = 0.34; p = 0.046). We also find a spatial cluster of higher than expected BCPR in Austin. Compared to the rest of the community, there was a 23.8 km radius area where BCPR rates were higher than expected (RR = 1.75; p = 0.07) which disappears after controlling for individual-level characteristics. Conclusions A geographically targeted CPR training strategy which is tailored to individual and neighborhood population characteristics may be effective in reducing existing disparities in the provision of bystander CPR for out-of-hospital cardiac arrest. PMID:23318916

Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia

PubMed Central

Morgan, Geoffrey G.; Jalaludin, Bin B.; Bauman, Adrian E.

2018-01-01

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed “disease maps” for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial distress. PMID:29415461

Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia.

PubMed

Mayne, Darren J; Morgan, Geoffrey G; Jalaludin, Bin B; Bauman, Adrian E

2018-02-06

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed "disease maps" for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial distress.

Geospatial Analysis on the Distributions of Tobacco Smoking and Alcohol Drinking in India

PubMed Central

Fu, Sze Hang; Jha, Prabhat; Gupta, Prakash C.; Kumar, Rajesh; Dikshit, Rajesh; Sinha, Dhirendra

2014-01-01

Background Tobacco smoking and binge alcohol drinking are two of the leading risk factors for premature mortality worldwide. In India, studies have examined the geographic distributions of tobacco smoking and alcohol drinking only at the state-level; sub-state variations and the spatial association between the two consumptions are poorly understood. Methodology We used data from the Special Fertility and Mortality Survey conducted in 1998 to examine the geographic distributions of tobacco smoking and alcohol drinking at the district and postal code levels. We used kriging interpolation to generate smoking and drinking distributions at the postal code level. We also examined spatial autocorrelations and identified spatial clusters of high and low prevalence of smoking and drinking. Finally, we used bivariate analyses to examine the spatial correlations between smoking and drinking, and between cigarette and bidi smoking. Results There was a high prevalence of any smoking in the central and northeastern states, and a high prevalence of any drinking in Himachal Pradesh, Arunachal Pradesh, and eastern Madhya Pradesh. Spatial clusters of early smoking (started smoking before age 20) were identified in the central states. Cigarette and bidi smoking showed distinctly different geographic patterns, with high levels of cigarette smoking in the northeastern states and high levels of bidi smoking in the central states. The geographic pattern of bidi smoking was similar to early smoking. Cigarette smoking was spatially associated with any drinking. Smoking prevalences in 1998 were correlated with prevalences in 2004 at the district level and 2010 at the state level. Conclusion These results along with earlier evidence on the complementarities between tobacco smoking and alcohol drinking suggest that local public health action on smoking might also help to reduce alcohol consumption, and vice versa. Surveys that properly represent tobacco and alcohol consumptions at the district level are recommended. PMID:25025379

Spatial Clustering of Occupational Injuries in Communities

PubMed Central

Friedman, Lee; Chin, Brian; Madigan, Dana

2015-01-01

Objectives. Using the social-ecological model, we hypothesized that the home residences of injured workers would be clustered predictably and geographically. Methods. We linked health care and publicly available datasets by home zip code for traumatically injured workers in Illinois from 2000 to 2009. We calculated numbers and rates of injuries, determined the spatial relationships, and developed 3 models. Results. Among the 23 200 occupational injuries, 80% of cases were located in 20% of zip codes and clustered in 10 locations. After component analysis, numbers and clusters of injuries correlated directly with immigrants; injury rates inversely correlated with urban poverty. Conclusions. Traumatic occupational injuries were clustered spatially by home location of the affected workers and in a predictable way. This put an inequitable burden on communities and provided evidence for the possible value of community-based interventions for prevention of occupational injuries. Work should be included in health disparities research. Stakeholders should determine whether and how to intervene at the community level to prevent occupational injuries. PMID:25905838

Investigation of Spatial and Temporal Trends in Water Quality in Daya Bay, South China Sea

PubMed Central

Wu, Mei-Lin; Wang, You-Shao; Dong, Jun-De; Sun, Cui-Ci; Wang, Yu-Tu; Sun, Fu-Lin; Cheng, Hao

2011-01-01

The objective is to identify the spatial and temporal variability of the hydrochemical quality of the water column in a subtropical coastal system, Daya Bay, China. Water samples were collected in four seasons at 12 monitoring sites. The Southeast Asian monsoons, northeasterly from October to the next April and southwesterly from May to September have also an important influence on water quality in Daya Bay. In the spatial pattern, two groups have been identified, with the help of multidimensional scaling analysis and cluster analysis. Cluster I consisted of the sites S3, S8, S10 and S11 in the west and north coastal parts of Daya Bay. Cluster I is mainly related to anthropogenic activities such as fish-farming. Cluster II consisted of the rest of the stations in the center, east and south parts of Daya Bay. Cluster II is mainly related to seawater exchange from South China Sea. PMID:21776234

NGC 2548: clumpy spatial and kinematic structure in an intermediate-age Galactic cluster

NASA Astrophysics Data System (ADS)

Vicente, Belén; Sánchez, Néstor; Alfaro, Emilio J.

2016-09-01

NGC 2548 is a ˜400-500 Myr old open cluster with evidence of spatial substructures likely caused by its interaction with the Galactic disc. In this work we use precise astrometric data from the Carte du Ciel - San Fernando (CdC-SF) catalogue to study the clumpy structure in this cluster. We confirm the fragmented structure of NGC 2548 but, additionally, the relatively high precision of our kinematic data lead us to the first detection of substructures in the proper motion space of a stellar cluster. There are three spatially separated cores each of which has its own counterpart in the proper motion distribution. The two main cores lie nearly parallel to the Galactic plane whereas the third one is significantly fainter than the others and it moves towards the Galactic plane separating from the rest of the cluster. We derive core positions and proper motions, as well as the stars belonging to each core.

Spatial clustering of average risks and risk trends in Bayesian disease mapping.

PubMed

Anderson, Craig; Lee, Duncan; Dean, Nema

2017-01-01

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hotspots and Coldspots: Household and village-level variation in orphanhood prevalence in rural Malawi

PubMed Central

Weinreb, Alexander; Gerland, Patrick; Fleming, Peter

2009-01-01

We explore the characteristics of households and villages in which orphans are resident in two areas of Malawi. We first review pertinent themes in qualitative data collected in our research sites. Then, using spatial analysis, we show how positive and negative clusters of orphans – which we term orphanhood "hotspots" and "coldspots" – can be found at the village and sub-village levels. In the third and longest section of the paper, and using multilevel analyses with both simple and complex variance structures, we evaluate the relationship between the presence of orphans and a range of individual, household and village-level characteristics, including households' spatial relationship to each other and to other local sites of significance. This series of analyses shows that the most important covariates of orphan presence are household size, wealth, and religious characteristics, with all measured simultaneously at both household and village-level. In addition, most of these have heterogenous effects across villages. We conclude by reviewing some difficulties in explaining causal mechanisms underlying these observed relationships, and discuss conceptual, theoretical and programmatic implications. PMID:20148129

Statistical analyses of the results of 25 years of beach litter surveys on the south-eastern North Sea coast.

PubMed

Schulz, Marcus; Clemens, Thomas; Förster, Harald; Harder, Thorsten; Fleet, David; Gaus, Silvia; Grave, Christel; Flegel, Imme; Schrey, Eckart; Hartwig, Eike

2015-08-01

In the North Sea, the amount of litter present in the marine environment represents a severe environmental problem. In order to assess the magnitude of the problem and measure changes in abundance, the results of two beach litter monitoring programmes were compared and analysed for long-term trends applying multivariate techniques. Total beach litter pollution was persistently high. Spatial differences in litter abundance made it difficult to identify long-term trends: Partly more than 8000 litter items year(-1) were recorded on a 100 m long survey site on the island of Scharhörn, while the survey site on the beach on the island of Amrum revealed abundances lower by two orders of magnitude. Beach litter was dominated by plastic with mean proportions of 52%-91% of total beach litter. Non-parametric time series analyses detected many significant trends, which, however, did not show any systematic spatial patterns. Cluster analyses partly led to groupings of beaches according to their expositions to sources of litter, wind and currents. Surveys in short intervals of one to two weeks were found to give higher annual sums of beach litter than the quarterly surveys of the OSPAR method. Surveys at regular intervals of four weeks to five months would make monitoring results more reliable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pfhrp2-Deleted Plasmodium falciparum Parasites in the Democratic Republic of the Congo: A National Cross-sectional Survey.

PubMed

Parr, Jonathan B; Verity, Robert; Doctor, Stephanie M; Janko, Mark; Carey-Ewend, Kelly; Turman, Breanna J; Keeler, Corinna; Slater, Hannah C; Whitesell, Amy N; Mwandagalirwa, Kashamuka; Ghani, Azra C; Likwela, Joris L; Tshefu, Antoinette K; Emch, Michael; Juliano, Jonathan J; Meshnick, Steven R

2017-07-01

Rapid diagnostic tests (RDTs) account for more than two-thirds of malaria diagnoses in Africa. Deletions of the Plasmodium falciparum hrp2 (pfhrp2) gene cause false-negative RDT results and have never been investigated on a national level. Spread of pfhrp2-deleted P. falciparum mutants, resistant to detection by HRP2-based RDTs, would represent a serious threat to malaria elimination efforts. Using a nationally representative cross-sectional study of 7,137 children under five years of age from the Democratic Republic of Congo (DRC), we tested 783 subjects with RDT-/PCR+ results using PCR assays to detect and confirm deletions of the pfhrp2 gene. Spatial and population genetic analyses were employed to examine the distribution and evolution of these parasites. We identified 149 pfhrp2-deleted parasites, representing 6.4% of all P. falciparum infections country-wide (95% confidence interval 5.1-8.0%). Bayesian spatial analyses identified statistically significant clustering of pfhrp2 deletions near Kinshasa and Kivu. Population genetic analysis revealed significant genetic differentiation between wild-type and pfhrp2-deleted parasite populations (GST = .046, p ≤ .00001). Pfhrp2-deleted P. falciparum is a common cause of RDT-/PCR+ malaria among asymptomatic children in the DRC and appears to be clustered within select communities. Surveillance for these deletions is needed, and alternatives to HRP2-specific RDTs may be necessary. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand.

PubMed

Nimbalkar, Prakash Madhav; Tripathi, Nitin Kumar

2016-11-21

Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P>0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

A WISE Survey of New Star Clusters in the Central Plane Region of the Milky Way

NASA Astrophysics Data System (ADS)

Ryu, Jinhyuk; Lee, Myung Gyoon

2018-04-01

We present the discovery of new star clusters in the central plane region (| l| < 30^\\circ and | b| < 6^\\circ ) of the Milky Way. In order to overcome the extinction problem and the spatial limit of previous surveys, we use the Wide-field Infrared Survey Explorer (WISE) data to find clusters. We also use other infrared survey data in the archive for additional analysis. We find 923 new clusters, of which 202 clusters are embedded clusters. These clusters are concentrated toward the Galactic plane and show a symmetric distribution with respect to the Galactic latitude. The embedded clusters show a stronger concentration to the Galactic plane than the nonembedded clusters. The new clusters are found more in the first Galactic quadrant, while previously known clusters are found more in the fourth Galactic quadrant. The spatial distribution of the combined sample of known clusters and new clusters is approximately symmetric with respect to the Galactic longitude. We estimate reddenings, distances, and relative ages of the 15 class A clusters using theoretical isochrones. Ten of them are relatively old (age >800 Myr) and five are young (age ≈4 Myr).

Relative risk estimates from spatial and space-time scan statistics: Are they biased?

PubMed Central

Prates, Marcos O.; Kulldorff, Martin; Assunção, Renato M.

2014-01-01

The purely spatial and space-time scan statistics have been successfully used by many scientists to detect and evaluate geographical disease clusters. Although the scan statistic has high power in correctly identifying a cluster, no study has considered the estimates of the cluster relative risk in the detected cluster. In this paper we evaluate whether there is any bias on these estimated relative risks. Intuitively, one may expect that the estimated relative risks has upward bias, since the scan statistic cherry picks high rate areas to include in the cluster. We show that this intuition is correct for clusters with low statistical power, but with medium to high power the bias becomes negligible. The same behaviour is not observed for the prospective space-time scan statistic, where there is an increasing conservative downward bias of the relative risk as the power to detect the cluster increases. PMID:24639031

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

Burstiness in Viral Bursts: How Stochasticity Affects Spatial Patterns in Virus-Microbe Dynamics

NASA Astrophysics Data System (ADS)

Lin, Yu-Hui; Taylor, Bradford P.; Weitz, Joshua S.

Spatial patterns emerge in living systems at the scale of microbes to metazoans. These patterns can be driven, in part, by the stochasticity inherent to the birth and death of individuals. For microbe-virus systems, infection and lysis of hosts by viruses results in both mortality of hosts and production of viral progeny. Here, we study how variation in the number of viral progeny per lysis event affects the spatial clustering of both viruses and microbes. Each viral ''burst'' is initially localized at a near-cellular scale. The number of progeny in a single lysis event can vary in magnitude between tens and thousands. These perturbations are not accounted for in mean-field models. Here we developed individual-based models to investigate how stochasticity affects spatial patterns in virus-microbe systems. We measured the spatial clustering of individuals using pair correlation functions. We found that increasing the burst size of viruses while maintaining the same production rate led to enhanced clustering. In this poster we also report on preliminary analysis on the evolution of the burstiness of viral bursts given a spatially distributed host community.

Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia.

PubMed

Fuentes-Vallejo, Mauricio

2017-07-24

Dengue is a widely spread vector-borne disease. Dengue cases in the Americas have increased over the last few decades, affecting various urban spaces throughout these continents, including the tourism-oriented city of Girardot, Colombia. Interactions among mosquitoes, pathogens and humans have recently been examined using different temporal and spatial scales in attempts to determine the roles that social and ecological systems play in dengue transmission. The current work characterizes the spatial and temporal behaviours of dengue in Girardot and discusses the potential territorial dynamics related to the distribution of this disease. Based on officially reported dengue cases (2012-2015) corresponding to epidemic (2013) and inter-epidemic years (2012, 2014, 2015), space (Getis-Ord index) and space-time (Kulldorff's scan statistics) analyses were performed. Geocoded dengue cases (n = 2027) were slightly overrepresented by men (52.1%). As expected, the cases were concentrated in the 0- to 15-year-old age group according to the actual trends of Colombia. The incidence rates of dengue during the rainy and dry seasons as well as those for individual years (2012, 2013 and 2014) were significant using the global Getis-Ord index. Local clusters shifted across seasons and years; nevertheless, the incidence rates clustered towards the southwest region of the city under different residential conditions. Space-time clusters shifted from the northeast to the southwest of the city (2012-2014). These clusters represented only 4.25% of the total cases over the same period (n = 1623). A general trend was observed, in which dengue cases increased during the dry seasons, especially between December and February. Despite study limitations related to official dengue records and available fine-scale demographic information, the spatial analysis results were promising from a geography of health perspective. Dengue did not show linear association with poverty or with vulnerable peripheral spaces in intra-urban settings, supporting the idea that the pathogenic complex of dengue is driven by different factors. A coordinated collaboration of epidemiological, public health and social science expertise is needed to assess the effect of "place" from a relational perspective in which geography has an important role to play.

Effect of random inhomogeneities in the spatial distribution of radiation-induced defect clusters on carrier transport through the thin base of a heterojunction bipolar transistor upon neutron irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Puzanov, A. S.; Obolenskiy, S. V., E-mail: obolensk@rf.unn.ru; Kozlov, V. A.

We analyze the electron transport through the thin base of a GaAs heterojunction bipolar transistor with regard to fluctuations in the spatial distribution of defect clusters induced by irradiation with a fissionspectrum fast neutron flux. We theoretically demonstrate that the homogeneous filling of the working region with radiation-induced defect clusters causes minimum degradation of the dc gain of the heterojunction bipolar transistor.

The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales

PubMed Central

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (FST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent. PMID:25628865

The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales.

PubMed

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (F ST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent.

Environmental heterogeneity blurs the signature of dispersal syndromes on spatial patterns of woody species in a moist tropical forest

PubMed Central

Velázquez, Eduardo; Escudero, Adrián; de la Cruz, Marcelino

2018-01-01

We assessed the relative importance of dispersal limitation, environmental heterogeneity and their joint effects as determinants of the spatial patterns of 229 species in the moist tropical forest of Barro Colorado Island (Panama). We differentiated five types of species according to their dispersal syndrome; autochorous, anemochorous, and zoochorous species with small, medium-size and large fruits. We characterized the spatial patterns of each species and we checked whether they were best fitted by Inhomogeneous Poisson (IPP), Homogeneous Poisson cluster (HPCP) and Inhomogeneous Poisson cluster processes (IPCP) by means of the Akaike Information Criterion. We also assessed the influence of species’ dispersal mode in the average cluster size. We found that 63% of the species were best fitted by IPCP regardless of their dispersal syndrome, although anemochorous species were best described by HPCP. Our results indicate that spatial patterns of tree species in this forest cannot be explained only by dispersal limitation, but by the joint effects of dispersal limitation and environmental heterogeneity. The absence of relationships between dispersal mode and degree of clustering suggests that several processes modify the original spatial pattern generated by seed dispersal. These findings emphasize the importance of fitting point process models with a different biological meaning when studying the main determinants of spatial structure in plant communities. PMID:29451871

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California.

PubMed

Liévanos, Raoul S

2018-04-16

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California.

Retooling CalEnviroScreen: Cumulative Pollution Burden and Race-Based Environmental Health Vulnerabilities in California

PubMed Central

2018-01-01

The California Community Environmental Health Screening Tool (CalEnviroScreen) advances research and policy pertaining to environmental health vulnerability. However, CalEnviroScreen departs from its historical foundations and comparable screening tools by no longer considering racial status as an indicator of environmental health vulnerability and predictor of cumulative pollution burden. This study used conceptual frameworks and analytical techniques from environmental health and inequality literature to address the limitations of CalEnviroScreen, especially its inattention to race-based environmental health vulnerabilities. It developed an adjusted measure of cumulative pollution burden from the CalEnviroScreen 2.0 data that facilitates multivariate analyses of the effect of neighborhood racial composition on cumulative pollution burden, net of other indicators of population vulnerability, traffic density, industrial zoning, and local and regional clustering of pollution burden. Principal component analyses produced three new measures of population vulnerability, including Latina/o cumulative disadvantage that represents the spatial concentration of Latinas/os, economic disadvantage, limited English-speaking ability, and health vulnerability. Spatial error regression analyses demonstrated that concentrations of Latinas/os, followed by Latina/o cumulative disadvantage, are the strongest demographic determinants of adjusted cumulative pollution burden. Findings have implications for research and policy pertaining to cumulative impacts and race-based environmental health vulnerabilities within and beyond California. PMID:29659481

Parental Action and Referral Patterns in Spatial Clusters of Childhood Autism Spectrum Disorder

ERIC Educational Resources Information Center

Schelly, David; Jiménez González, Patricia; Solís, Pedro J.

2018-01-01

Sociodemographic factors have long been associated with disparities in autism spectrum disorder (ASD) diagnosis. Studies that identified spatial clustering of cases have suggested the importance of information about ASD moving through social networks of parents. Yet there is no direct evidence of this mechanism. This study explores the…

Spatial and temporal variability of microgeographic genetic structure in white-tailed deer

USGS Publications Warehouse

Scribner, Kim T.; Smith, Michael H.; Chesser, Ronald K.

1997-01-01

Techniques are described that define contiguous genetic subpopulations of white-tailed deer (Odocoileus virginianus) based on the spatial dispersion of 4,749 individuals that possessed discrete character values (alleles or genotypes) during each of 6 years (1974-1979). White-tailed deer were not uniformly distributed in space, but exhibited considerable spatial genetic structuring. Significant non-random clusters of individuals were documented during each year based on specific alleles and genotypes at the Sdh locus. Considerable temporal variation was observed in the position and genetic composition of specific clusters, which reflected changes in allele frequency in small geographic areas. The position of clusters did not consistently correspond with traditional management boundaries based on major discontinuities in habitat (swamp versus upland) and hunt compartments that were defined by roads and streams. Spatio-temporal stability of observed genetic contiguous clusters was interpreted relative to method and intensity of harvest, movements, and breeding ecology.

Deforestation, agriculture and farm jobs: a good recipe for Plasmodium vivax in French Guiana.

PubMed

Basurko, Célia; Demattei, Christophe; Han-Sze, René; Grenier, Claire; Joubert, Michel; Nacher, Mathieu; Carme, Bernard

2013-03-11

In a malaria-endemic area the distribution of patients is neither constant in time nor homogeneous in space. The WHO recommends the stratification of malaria risk on a fine geographical scale. In the village of Cacao in French Guiana, the study of the spatial and temporal distribution of malaria cases, during an epidemic, allowed a better understanding of the environmental factors promoting malaria transmission. A dynamic cohort of 839 persons living in 176 households (only people residing permanently in the village) was constituted between January 1st, 2002 and December 31st, 2007.The information about the number of inhabitants per household, the number of confirmed cases of Plasmodium vivax and house GPS coordinates were collected to search for spatial or temporal clustering using Kurlldorff's statistical method. Of the 839 persons living permanently in the village of Cacao, 359 persons presented at least one vivax malaria episode between 2002 and 2007. Five temporal clusters and four spatial clusters were identified during the study period. In all temporal clusters, April was included. Two spatial clusters were localized at the north of the village near the Comté River and two others localized close to orchards. The spatial heterogeneity of malaria in the village may have been influenced by environmental disturbances due to local agricultural policies: deforestation, cultures of fresh produce, or drainage of water for agriculture. This study allowed generating behavioural, entomological, or environmental hypotheses that could be useful to improve prevention campaigns.

Spatial analysis of paediatric swimming pool submersions by housing type.

PubMed

Shenoi, Rohit P; Levine, Ned; Jones, Jennifer L; Frost, Mary H; Koerner, Christine E; Fraser, John J

2015-08-01

Drowning is a major cause of unintentional childhood death. The relationship between childhood swimming pool submersions, neighbourhood sociodemographics, housing type and swimming pool location was examined in Harris County, Texas. Childhood pool submersion incidents were examined for spatial clustering using the Nearest Neighbor Hierarchical Cluster (Nnh) algorithm. To relate submersions to predictive factors, an Markov Chain Monte Carlo (MCMC) Poisson-Lognormal-Conditional Autoregressive (CAR) spatial regression model was tested at the census tract level. There were 260 submersions; 49 were fatal. Forty-two per cent occurred at single-family residences and 36% at multifamily residential buildings. The risk of a submersion was 2.7 times higher for a child at a multifamily than a single-family residence and 28 times more likely in a multifamily swimming pool than a single family pool. However, multifamily submersions were clustered because of the concentration of such buildings with pools. Spatial clustering did not occur in single-family residences. At the tract level, submersions in single-family and multifamily residences were best predicted by the number of pools by housing type and the number of children aged 0-17 by housing type. Paediatric swimming pool submersions in multifamily buildings are spatially clustered. The likelihood of submersions is higher for children who live in multifamily buildings with pools than those who live in single-family homes with pools. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Spatial and kinematic distributions of transition populations in intermediate redshift galaxy clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, Steven M.; Wirth, Gregory D.; Bershady, Matthew A., E-mail: crawford@saao.ac.za, E-mail: wirth@keck.hawaii.edu, E-mail: mab@astro.wisc.edu

2014-05-01

We analyze the spatial and velocity distributions of confirmed members in five massive clusters of galaxies at intermediate redshift (0.5 < z < 0.9) to investigate the physical processes driving galaxy evolution. Based on spectral classifications derived from broad- and narrow-band photometry, we define four distinct galaxy populations representing different evolutionary stages: red sequence (RS) galaxies, blue cloud (BC) galaxies, green valley (GV) galaxies, and luminous compact blue galaxies (LCBGs). For each galaxy class, we derive the projected spatial and velocity distribution and characterize the degree of subclustering. We find that RS, BC, and GV galaxies in these clusters havemore » similar velocity distributions, but that BC and GV galaxies tend to avoid the core of the two z ≈ 0.55 clusters. GV galaxies exhibit subclustering properties similar to RS galaxies, but their radial velocity distribution is significantly platykurtic compared to the RS galaxies. The absence of GV galaxies in the cluster cores may explain their somewhat prolonged star-formation history. The LCBGs appear to have recently fallen into the cluster based on their larger velocity dispersion, absence from the cores of the clusters, and different radial velocity distribution than the RS galaxies. Both LCBG and BC galaxies show a high degree of subclustering on the smallest scales, leading us to conclude that star formation is likely triggered by galaxy-galaxy interactions during infall into the cluster.« less

Quantify spatial relations to discover handwritten graphical symbols

NASA Astrophysics Data System (ADS)

Li, Jinpeng; Mouchère, Harold; Viard-Gaudin, Christian

2012-01-01

To model a handwritten graphical language, spatial relations describe how the strokes are positioned in the 2-dimensional space. Most of existing handwriting recognition systems make use of some predefined spatial relations. However, considering a complex graphical language, it is hard to express manually all the spatial relations. Another possibility would be to use a clustering technique to discover the spatial relations. In this paper, we discuss how to create a relational graph between strokes (nodes) labeled with graphemes in a graphical language. Then we vectorize spatial relations (edges) for clustering and quantization. As the targeted application, we extract the repetitive sub-graphs (graphical symbols) composed of graphemes and learned spatial relations. On two handwriting databases, a simple mathematical expression database and a complex flowchart database, the unsupervised spatial relations outperform the predefined spatial relations. In addition, we visualize the frequent patterns on two text-lines containing Chinese characters.

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Estimating life expectancies for US small areas: a regression framework

NASA Astrophysics Data System (ADS)

Congdon, Peter

2014-01-01

Analysis of area mortality variations and estimation of area life tables raise methodological questions relevant to assessing spatial clustering, and socioeconomic inequalities in mortality. Existing small area analyses of US life expectancy variation generally adopt ad hoc amalgamations of counties to alleviate potential instability of mortality rates involved in deriving life tables, and use conventional life table analysis which takes no account of correlated mortality for adjacent areas or ages. The alternative strategy here uses structured random effects methods that recognize correlations between adjacent ages and areas, and allows retention of the original county boundaries. This strategy generalizes to include effects of area category (e.g. poverty status, ethnic mix), allowing estimation of life tables according to area category, and providing additional stabilization of estimated life table functions. This approach is used here to estimate stabilized mortality rates, derive life expectancies in US counties, and assess trends in clustering and in inequality according to county poverty category.

T-ReX Spies the Stars of 30 Doradus

NASA Astrophysics Data System (ADS)

Broos, Patrick; Townsley, Leisa K.; Pollock, Andrew; Crowther, Paul

2017-08-01

30 Doradus (the Tarantula Nebula) is the Local Group's most massive young star-forming complex. At its heart is R136, the most massive resolved stellar cluster; R136 contains, in turn, the most massive stars known. The Chandra X-ray Observatory has recently observed 30 Dor for the 2-megasecond X-ray Visionary Project ``The Tarantula -- Revealed by X-rays'' (T-ReX). This deep observation exploits Chandra's fine spatial resolution to study the full complement of massive stars and the brightest pre-main sequence stars that trace 25 Myrs of star formation in this incomparable nearby starburst. Here we give preliminary results from the ongoing analyses of the data, focusing on the massive stars. While many remain undetected even in this deep ACIS-I observation, a few show dramatic X-ray lightcurves and/or high luminosities befitting this amazing starburst cluster.

Analysis of Radiation Damage in Light Water Reactors: Comparison of Cluster Analysis Methods for the Analysis of Atom Probe Data.

PubMed

Hyde, Jonathan M; DaCosta, Gérald; Hatzoglou, Constantinos; Weekes, Hannah; Radiguet, Bertrand; Styman, Paul D; Vurpillot, Francois; Pareige, Cristelle; Etienne, Auriane; Bonny, Giovanni; Castin, Nicolas; Malerba, Lorenzo; Pareige, Philippe

2017-04-01

Irradiation of reactor pressure vessel (RPV) steels causes the formation of nanoscale microstructural features (termed radiation damage), which affect the mechanical properties of the vessel. A key tool for characterizing these nanoscale features is atom probe tomography (APT), due to its high spatial resolution and the ability to identify different chemical species in three dimensions. Microstructural observations using APT can underpin development of a mechanistic understanding of defect formation. However, with atom probe analyses there are currently multiple methods for analyzing the data. This can result in inconsistencies between results obtained from different researchers and unnecessary scatter when combining data from multiple sources. This makes interpretation of results more complex and calibration of radiation damage models challenging. In this work simulations of a range of different microstructures are used to directly compare different cluster analysis algorithms and identify their strengths and weaknesses.

Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system

PubMed Central

Xing, Jian; Burkom, Howard; Moniz, Linda; Edgerton, James; Leuze, Michael; Tokars, Jerome

2009-01-01

Background The Centers for Disease Control and Prevention's (CDC's) BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history. Methods The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate) and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. Results Simple estimation methods that account for day-of-week (DOW) data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. Conclusion The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving different spatial resolution or other syndromes can yield further improvement. PMID:19615075

[Temporal-spatial analysis of bacillary dysentery in the Three Gorges Area of China, 2005-2016].

PubMed

Zhang, P; Zhang, J; Chang, Z R; Li, Z J

2018-01-10

Objective: To analyze the spatial and temporal distributions of bacillary dysentery in Chongqing, Yichang and Enshi (the Three Gorges Area) from 2005 to 2016, and provide evidence for the disease prevention and control. Methods: The incidence data of bacillary dysentery in the Three Gorges Area during this period were collected from National Notifiable Infectious Disease Reporting System. The spatial-temporal scan statistic was conducted with software SaTScan 9.4 and bacillary dysentery clusters were visualized with software ArcGIS 10.3. Results: A total of 126 196 cases were reported in the Three Gorges Area during 2005-2016, with an average incidence rate of 29.67/100 000. The overall incidence was in a downward trend, with an average annual decline rate of 4.74%. Cases occurred all the year round but with an obvious seasonal increase between May and October. Among the reported cases, 44.71% (56 421/126 196) were children under 5-year-old, the cases in children outside child care settings accounted for 41.93% (52 918/126 196) of the total. The incidence rates in districts of Yuzhong, Dadukou, Jiangbei, Shapingba, Jiulongpo, Nanan, Yubei, Chengkou of Chongqing and districts of Xiling and Wujiagang of Yichang city of Hubei province were high, ranging from 60.20/100 000 to 114.81/100 000. Spatial-temporal scan statistic for the spatial and temporal distributions of bacillary dysentery during this period revealed that the temporal distribution was during May-October, and there were 12 class Ⅰ clusters, 35 class Ⅱ clusters, and 9 clusters without statistical significance in counties with high incidence. All the class Ⅰ clusters were in urban area of Chongqing (Yuzhong, Dadukou, Jiangbei, Shapingba, Jiulongpo, Nanan, Beibei, Yubei, Banan) and surrounding counties, and the class Ⅱ clusters transformed from concentrated distribution to scattered distribution. Conclusions: Temporal and spatial cluster of bacillary dysentery incidence existed in the three gorges area during 2005-2016. It is necessary to strengthen the bacillary dysentery prevention and control in urban areas of Chongqing and Yichang.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

PubMed Central

Austin, Elena; Coull, Brent A.; Zanobetti, Antonella; Koutrakis, Petros

2013-01-01

Background Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. Objectives Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. Methods 109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. Results Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. Conclusions The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research. PMID:23850585

Spatial event cluster detection using an approximate normal distribution.

PubMed

Torabi, Mahmoud; Rosychuk, Rhonda J

2008-12-12

In geographic surveillance of disease, areas with large numbers of disease cases are to be identified so that investigations of the causes of high disease rates can be pursued. Areas with high rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. Typically cluster detection tests are applied to incident or prevalent cases of disease, but surveillance of disease-related events, where an individual may have multiple events, may also be of interest. Previously, a compound Poisson approach that detects clusters of events by testing individual areas that may be combined with their neighbours has been proposed. However, the relevant probabilities from the compound Poisson distribution are obtained from a recursion relation that can be cumbersome if the number of events are large or analyses by strata are performed. We propose a simpler approach that uses an approximate normal distribution. This method is very easy to implement and is applicable to situations where the population sizes are large and the population distribution by important strata may differ by area. We demonstrate the approach on pediatric self-inflicted injury presentations to emergency departments and compare the results for probabilities based on the recursion and the normal approach. We also implement a Monte Carlo simulation to study the performance of the proposed approach. In a self-inflicted injury data example, the normal approach identifies twelve out of thirteen of the same clusters as the compound Poisson approach, noting that the compound Poisson method detects twelve significant clusters in total. Through simulation studies, the normal approach well approximates the compound Poisson approach for a variety of different population sizes and case and event thresholds. A drawback of the compound Poisson approach is that the relevant probabilities must be determined through a recursion relation and such calculations can be computationally intensive if the cluster size is relatively large or if analyses are conducted with strata variables. On the other hand, the normal approach is very flexible, easily implemented, and hence, more appealing for users. Moreover, the concepts may be more easily conveyed to non-statisticians interested in understanding the methodology associated with cluster detection test results.

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

DOE PAGES

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.; ...

2017-07-07

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

Textures in spinel peridotite mantle xenoliths using micro-CT scanning: Examples from Canary Islands and France

NASA Astrophysics Data System (ADS)

Bhanot, K. K.; Downes, H.; Petrone, C. M.; Humphreys-Williams, E.

2017-04-01

Spinel pyroxene-clusters, which are intergrowths of spinel, orthopyroxene and clinopyroxene in mantle xenoliths, have been investigated through the use of micro-CT (μ-CT) in this study. Samples have been studied from two different tectonic settings: (1) the northern Massif Central, France, an uplifted and rifted plateau on continental lithosphere and (2) Lanzarote in the Canary Islands, an intraplate volcanic island on old oceanic lithosphere. μ-CT analysis of samples from both locations has revealed a range of spinel textures from small < 2 mm microcrystals which can be either spatially concentrated or distributed more evenly throughout the rock with a lineation, to large 4-12 mm individual clusters with ellipsoidal complex vermicular textures in random orientation. Microprobe analyses of pyroxenes inside and outside the clusters show broadly similar compositions. Spinel-pyroxene clusters are the result of a transition of shallow lithospheric mantle from the garnet stability field to the spinel stability field. Both the northern Massif Central and Lanzarote are regions that have experienced significant lithospheric thinning. This process provides a mechanism where the sub-solidus reaction of olivine + garnet = orthopyroxene + clinopyroxene + spinel is satisfied by providing a pathway from garnet peridotite to spinel peridotite. We predict that such textures would only occur in the mantle beneath regions that show evidence of thinning of the lithospheric mantle. Metasomatic reactions are seen around spinel-pyroxene clusters in some Lanzarote xenoliths, so metasomatism post-dated cluster formation.

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaleita, Amy L.; Schott, Linda R.; Hargreaves, Sarah K.

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using sixmore » landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.« less

Coupled stochastic spatial and non-spatial simulations of ErbB1 signaling pathways demonstrate the importance of spatial organization in signal transduction.

PubMed

Costa, Michelle N; Radhakrishnan, Krishnan; Wilson, Bridget S; Vlachos, Dionisios G; Edwards, Jeremy S

2009-07-23

The ErbB family of receptors activates intracellular signaling pathways that control cellular proliferation, growth, differentiation and apoptosis. Given these central roles, it is not surprising that overexpression of the ErbB receptors is often associated with carcinogenesis. Therefore, extensive laboratory studies have been devoted to understanding the signaling events associated with ErbB activation. Systems biology has contributed significantly to our current understanding of ErbB signaling networks. However, although computational models have grown in complexity over the years, little work has been done to consider the spatial-temporal dynamics of receptor interactions and to evaluate how spatial organization of membrane receptors influences signaling transduction. Herein, we explore the impact of spatial organization of the epidermal growth factor receptor (ErbB1/EGFR) on the initiation of downstream signaling. We describe the development of an algorithm that couples a spatial stochastic model of membrane receptors with a nonspatial stochastic model of the reactions and interactions in the cytosol. This novel algorithm provides a computationally efficient method to evaluate the effects of spatial heterogeneity on the coupling of receptors to cytosolic signaling partners. Mathematical models of signal transduction rarely consider the contributions of spatial organization due to high computational costs. A hybrid stochastic approach simplifies analyses of the spatio-temporal aspects of cell signaling and, as an example, demonstrates that receptor clustering contributes significantly to the efficiency of signal propagation from ligand-engaged growth factor receptors.

Spatial Analysis of Hemorrhagic Fever with Renal Syndrome in Zibo City, China, 2009–2012

PubMed Central

Wang, Ling; Yang, Shuxia; Zhang, Ling; Cao, Haixia; Zhang, Yan; Hu, Haodong; Zhai, Shenyong

2013-01-01

Background Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90% of the total global cases. Zibo City is one of the most serious affected areas in Shandong Province China with the HFRS incidence increasing sharply from 2009 to 2012. However, the hotspots of HFRS in Zibo remained unclear. Thus, a spatial analysis was conducted with the aim to explore the spatial, spatial-temporal and seasonal patterns of HFRS in Zibo from 2009 to 2012, and to provide guidance for formulating regional prevention and control strategies. Methods The study was based on the reported cases of HFRS from the National Notifiable Disease Surveillance System. Annualized incidence maps and seasonal incidence maps were produced to analyze the spatial and seasonal distribution of HFRS in Zibo City. Then spatial scan statistics and space-time scan statistics were conducted to identify clusters of HFRS. Results There were 200 cases reported in Zibo City during the 4-year study period. One most likely cluster and one secondary cluster for high incidence of HFRS were identified by the space-time analysis. And the most likely cluster was found to exist at Yiyuan County in October to December 2012. The human infections in the fall and winter reflected a seasonal characteristic pattern of Hantaan virus (HTNV) transmission. The secondary cluster was detected at the center of Zibo in May to June 2009, presenting a seasonal characteristic of Seoul virus (SEOV) transmission. Conclusion To control and prevent HFRS in Zibo city, the comprehensive preventive strategy should be implemented in the southern areas of Zibo in autumn and in the northern areas of Zibo in spring. PMID:23840719

Crop Species Diversity Changes in the United States: 1978–2012

PubMed Central

Aguilar, Jonathan; Gramig, Greta G.; Hendrickson, John R.; Archer, David W.; Forcella, Frank; Liebig, Mark A.

2015-01-01

Anecdotal accounts regarding reduced US cropping system diversity have raised concerns about negative impacts of increasingly homogeneous cropping systems. However, formal analyses to document such changes are lacking. Using US Agriculture Census data, which are collected every five years, we quantified crop species diversity from 1978 to 2012, for the contiguous US on a county level basis. We used Shannon diversity indices expressed as effective number of crop species (ENCS) to quantify crop diversity. We then evaluated changes in county-level crop diversity both nationally and for each of the eight Farm Resource Regions developed by the National Agriculture Statistics Service. During the 34 years we considered in our analyses, both national and regional ENCS changed. Nationally, crop diversity was lower in 2012 than in 1978. However, our analyses also revealed interesting trends between and within different Resource Regions. Overall, the Heartland Resource Region had the lowest crop diversity whereas the Fruitful Rim and Northern Crescent had the highest. In contrast to the other Resource Regions, the Mississippi Portal had significantly higher crop diversity in 2012 than in 1978. Also, within regions there were differences between counties in crop diversity. Spatial autocorrelation revealed clustering of low and high ENCS and this trend became stronger over time. These results show that, nationally counties have been clustering into areas of either low diversity or high diversity. Moreover, a significant trend of more counties shifting to lower rather than to higher crop diversity was detected. The clustering and shifting demonstrates a trend toward crop diversity loss and attendant homogenization of agricultural production systems, which could have far-reaching consequences for provision of ecosystem system services associated with agricultural systems as well as food system sustainability. PMID:26308552

CFHTLenS and RCSLenS: testing photometric redshift distributions using angular cross-correlations with spectroscopic galaxy surveys

NASA Astrophysics Data System (ADS)

Choi, A.; Heymans, C.; Blake, C.; Hildebrandt, H.; Duncan, C. A. J.; Erben, T.; Nakajima, R.; Van Waerbeke, L.; Viola, M.

2016-12-01

We determine the accuracy of galaxy redshift distributions as estimated from photometric redshift probability distributions p(z). Our method utilizes measurements of the angular cross-correlation between photometric galaxies and an overlapping sample of galaxies with spectroscopic redshifts. We describe the redshift leakage from a galaxy photometric redshift bin j into a spectroscopic redshift bin I using the sum of the p(z) for the galaxies residing in bin j. We can then predict the angular cross-correlation between photometric and spectroscopic galaxies due to intrinsic galaxy clustering when I ≠ j as a function of the measured angular cross-correlation when I = j. We also account for enhanced clustering arising from lensing magnification using a halo model. The comparison of this prediction with the measured signal provides a consistency check on the validity of using the summed p(z) to determine galaxy redshift distributions in cosmological analyses, as advocated by the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). We present an analysis of the photometric redshifts measured by CFHTLenS, which overlaps the Baryon Oscillation Spectroscopic Survey (BOSS). We also analyse the Red-sequence Cluster Lensing Survey, which overlaps both BOSS and the WiggleZ Dark Energy Survey. We find that the summed p(z) from both surveys are generally biased with respect to the true underlying distributions. If unaccounted for, this bias would lead to errors in cosmological parameter estimation from CFHTLenS by less than ˜4 per cent. For photometric redshift bins which spatially overlap in 3D with our spectroscopic sample, we determine redshift bias corrections which can be used in future cosmological analyses that rely on accurate galaxy redshift distributions.

Spatial distribution of Parkinson's disease mortality in Spain, 1989-1998, as a guide for focused aetiological research or health-care intervention

PubMed Central

2009-01-01

Background Aetiologically, genetic and environmental factors having an uneven spatial distribution may underlie Parkinson's disease (PD). Undiagnosis of PD in selected regions might have limited access to treatment with levodopa and simultaneously, if present at death, determined PD underreporting at the death record. The purpose of this study was to describe and analyse municipal mortality due to PD in Spain in aetiological and interventional perspective. Methods PD mortality at a municipal level was modelled using the Besag-York- Molliè autoregressive spatial model, combining demographic information with cause-of-death diagnostic data (International Classification of Diseases 9th Revision (ICD-9) code 332.0). Municipal relative risks (RRs) were independently estimated for women, men and both sexes, and plotted on maps depicting smoothed RR estimates and the distribution of the posterior probability of RR>1. Results A south-north gradient, with large geographical areas suggesting clustered towns with high mortality, was seen in Asturias, the Basque Country, Balearic Islands and, particularly, in the Lower Ebro valley around Tarragona. Similarly, there was a suggestion that lowest mortality was clustered in the south-east and south-west. We identified some isolated or clustered municipalities with high mortality that were situated near industrial plants reported to be associated with environmental xenobiotic emissions. However, the same pattern was also observed for some cities with low mortality. Conclusion Municipal PD mortality in Spain was unevenly distributed. Patterns were roughly similar to reported provincial PD mortality and use of levodopa. While the overall pattern appears to result from spatially selective PD undiagnosis, and can not be ascribed to industrial emissions, it can not be excluded that selected "hot spots" reflect genetic factors and/or environmental exposures inducing parkinsonism. A few municipal populations, located in low-mortality-risk areas in the vicinity of polluting plants or registering high excess PD mortality, might constitute a priority for conducting direct etiological studies. Additionally, interventions aimed to reduce potential PD undiagnosis might be most appropriate in the South. PMID:19954536

Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis.

PubMed

Yang, Chongguang; Lu, Liping; Warren, Joshua L; Wu, Jie; Jiang, Qi; Zuo, Tianyu; Gan, Mingyu; Liu, Mei; Liu, Qingyun; DeRiemer, Kathryn; Hong, Jianjun; Shen, Xin; Colijn, Caroline; Guo, Xiaoqin; Gao, Qian; Cohen, Ted

2018-04-19

Massive internal migration from rural to urban areas poses new challenges for tuberculosis control in China. We aimed to combine genomic, spatial, and epidemiological data to describe the dynamics of tuberculosis in an urban setting with large numbers of migrants. We did a population-based study of culture-positive Mycobacterium tuberculosis isolates in Songjiang, Shanghai. We used whole-genome sequencing to discriminate apparent genetic clusters of M tuberculosis sharing identical variable-number-tandem-repeat (VNTR) patterns, and analysed the relations between proximity of residence and the risk of genomically clustered M tuberculosis. Finally, we used genomic, spatial, and epidemiological data to estimate time of infection and transmission links among migrants and residents. Between Jan 1, 2009, and Dec 31, 2015, 1620 cases of culture-positive tuberculosis were recorded, 1211 (75%) of which occurred among internal migrants. 150 (69%) of 218 people sharing identical VNTR patterns had isolates within ten single-nucleotide polymorphisms (SNPs) of at least one other strain, consistent with recent transmission of M tuberculosis. Pairs of strains collected from individuals living in close proximity were more likely to be genetically similar than those from individuals who lived far away-for every additional km of distance between patients' homes, the odds that genotypically matched strains were within ten SNPs of each other decreased by about 10% (OR 0·89 [95% CI 0·87-0·91]; p<0·0001). We inferred that transmission from residents to migrants occurs as commonly as transmission from migrants to residents, and we estimated that more than two-thirds of migrants in genomic clusters were infected locally after migration. The primary mechanism driving local incidence of tuberculosis in urban centres is local transmission between both migrants and residents. Combined analysis of epidemiological, genomic, and spatial data contributes to a richer understanding of local transmission dynamics and should inform the design of more effective interventions. National Natural Science Foundation of China, National Science and Technology Major Project of China, and US National Institutes of Health. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study.

PubMed

Zhu, Bin; Fu, Yang; Liu, Jinlin; Mao, Ying

2018-01-01

China is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China. Global and local Moran's I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps. Of the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high-low (unit with high incidence surrounded by units with high incidence, the same below) and high-high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low-low and low-high spatial cluster areas abound in provincial units in north and east China. Despite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high-high/high-low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders.

Imaging different components of a tectonic tremor sequence in southwestern Japan using an automatic statistical detection and location method

NASA Astrophysics Data System (ADS)

Poiata, Natalia; Vilotte, Jean-Pierre; Bernard, Pascal; Satriano, Claudio; Obara, Kazushige

2018-06-01

In this study, we demonstrate the capability of an automatic network-based detection and location method to extract and analyse different components of tectonic tremor activity by analysing a 9-day energetic tectonic tremor sequence occurring at the downdip extension of the subducting slab in southwestern Japan. The applied method exploits the coherency of multiscale, frequency-selective characteristics of non-stationary signals recorded across the seismic network. Use of different characteristic functions, in the signal processing step of the method, allows to extract and locate the sources of short-duration impulsive signal transients associated with low-frequency earthquakes and of longer-duration energy transients during the tectonic tremor sequence. Frequency-dependent characteristic functions, based on higher-order statistics' properties of the seismic signals, are used for the detection and location of low-frequency earthquakes. This allows extracting a more complete (˜6.5 times more events) and time-resolved catalogue of low-frequency earthquakes than the routine catalogue provided by the Japan Meteorological Agency. As such, this catalogue allows resolving the space-time evolution of the low-frequency earthquakes activity in great detail, unravelling spatial and temporal clustering, modulation in response to tide, and different scales of space-time migration patterns. In the second part of the study, the detection and source location of longer-duration signal energy transients within the tectonic tremor sequence is performed using characteristic functions built from smoothed frequency-dependent energy envelopes. This leads to a catalogue of longer-duration energy sources during the tectonic tremor sequence, characterized by their durations and 3-D spatial likelihood maps of the energy-release source regions. The summary 3-D likelihood map for the 9-day tectonic tremor sequence, built from this catalogue, exhibits an along-strike spatial segmentation of the long-duration energy-release regions, matching the large-scale clustering features evidenced from the low-frequency earthquake's activity analysis. Further examination of the two catalogues showed that the extracted short-duration low-frequency earthquakes activity coincides in space, within about 10-15 km distance, with the longer-duration energy sources during the tectonic tremor sequence. This observation provides a potential constraint on the size of the longer-duration energy-radiating source region in relation with the clustering of low-frequency earthquakes activity during the analysed tectonic tremor sequence. We show that advanced statistical network-based methods offer new capabilities for automatic high-resolution detection, location and monitoring of different scale-components of tectonic tremor activity, enriching existing slow earthquakes catalogues. Systematic application of such methods to large continuous data sets will allow imaging the slow transient seismic energy-release activity at higher resolution, and therefore, provide new insights into the underlying multiscale mechanisms of slow earthquakes generation.

Imaging different components of a tectonic tremor sequence in southwestern Japan using an automatic statistical detection and location method

NASA Astrophysics Data System (ADS)

Poiata, Natalia; Vilotte, Jean-Pierre; Bernard, Pascal; Satriano, Claudio; Obara, Kazushige

2018-02-01

In this study, we demonstrate the capability of an automatic network-based detection and location method to extract and analyse different components of tectonic tremor activity by analysing a 9-day energetic tectonic tremor sequence occurring at the down-dip extension of the subducting slab in southwestern Japan. The applied method exploits the coherency of multi-scale, frequency-selective characteristics of non-stationary signals recorded across the seismic network. Use of different characteristic functions, in the signal processing step of the method, allows to extract and locate the sources of short-duration impulsive signal transients associated with low-frequency earthquakes and of longer-duration energy transients during the tectonic tremor sequence. Frequency-dependent characteristic functions, based on higher-order statistics' properties of the seismic signals, are used for the detection and location of low-frequency earthquakes. This allows extracting a more complete (˜6.5 times more events) and time-resolved catalogue of low-frequency earthquakes than the routine catalogue provided by the Japan Meteorological Agency. As such, this catalogue allows resolving the space-time evolution of the low-frequency earthquakes activity in great detail, unravelling spatial and temporal clustering, modulation in response to tide, and different scales of space-time migration patterns. In the second part of the study, the detection and source location of longer-duration signal energy transients within the tectonic tremor sequence is performed using characteristic functions built from smoothed frequency-dependent energy envelopes. This leads to a catalogue of longer-duration energy sources during the tectonic tremor sequence, characterized by their durations and 3-D spatial likelihood maps of the energy-release source regions. The summary 3-D likelihood map for the 9-day tectonic tremor sequence, built from this catalogue, exhibits an along-strike spatial segmentation of the long-duration energy-release regions, matching the large-scale clustering features evidenced from the low-frequency earthquake's activity analysis. Further examination of the two catalogues showed that the extracted short-duration low-frequency earthquakes activity coincides in space, within about 10-15 km distance, with the longer-duration energy sources during the tectonic tremor sequence. This observation provides a potential constraint on the size of the longer-duration energy-radiating source region in relation with the clustering of low-frequency earthquakes activity during the analysed tectonic tremor sequence. We show that advanced statistical network-based methods offer new capabilities for automatic high-resolution detection, location and monitoring of different scale-components of tectonic tremor activity, enriching existing slow earthquakes catalogues. Systematic application of such methods to large continuous data sets will allow imaging the slow transient seismic energy-release activity at higher resolution, and therefore, provide new insights into the underlying multi-scale mechanisms of slow earthquakes generation.

Analysis of earthquake clustering and source spectra in the Salton Sea Geothermal Field

NASA Astrophysics Data System (ADS)

Cheng, Y.; Chen, X.

2015-12-01

The Salton Sea Geothermal field is located within the tectonic step-over between San Andreas Fault and Imperial Fault. Since the 1980s, geothermal energy exploration has resulted with step-like increase of microearthquake activities, which mirror the expansion of geothermal field. Distinguishing naturally occurred and induced seismicity, and their corresponding characteristics (e.g., energy release) is important for hazard assessment. Between 2008 and 2014, seismic data recorded by a local borehole array were provided public access from CalEnergy through SCEC data center; and the high quality local recording of over 7000 microearthquakes provides unique opportunity to sort out characteristics of induced versus natural activities. We obtain high-resolution earthquake location using improved S-wave picks, waveform cross-correlation and a new 3D velocity model. We then develop method to identify spatial-temporally isolated earthquake clusters. These clusters are classified into aftershock-type, swarm-type, and mixed-type (aftershock-like, with low skew, low magnitude and shorter duration), based on the relative timing of largest earthquakes and moment-release. The mixed-type clusters are mostly located at 3 - 4 km depth near injection well; while aftershock-type clusters and swarm-type clusters also occur further from injection well. By counting number of aftershocks within 1day following mainshock in each cluster, we find that the mixed-type clusters have much higher aftershock productivity compared with other types and historic M4 earthquakes. We analyze detailed spatial variation of 'b-value'. We find that the mixed-type clusters are mostly located within high b-value patches, while large (M>3) earthquakes and other types of clusters are located within low b-value patches. We are currently processing P and S-wave spectra to analyze the spatial-temporal correlation of earthquake stress parameter and seismicity characteristics. Preliminary results suggest that the mixed-type clusters and high b-value patches are spatially correlated with low stress drop earthquakes, indicating high-productivity microearthquakes within low differential stress region, potentially due to deeper injection activities.

Using an index of habitat patch proximity for landscape design

Treesearch

Eric J. Gustafson; George R. Parker

1994-01-01

A proximity index (PX) inspired by island biogeography theory is described which quantifies the spatial context of a habitat patch in relation to its neighbors. The index distinguishes sparse distributions of small habitat patches from clusters of large patches. An evaluation of the relationship between PX and variation in the spatial characteristics of clusters of...

Use of LANDSAT imagery for wildlife habitat mapping in northeast and eastcentral Alaska

NASA Technical Reports Server (NTRS)

Lent, P. C. (Principal Investigator)

1976-01-01

The author has identified the following significant results. There is strong indication that spatially rare feature classes may be missed in clustering classifications based on 2% random sampling. Therefore, it seems advisable to augment random sampling for cluster analysis with directed sampling of any spatially rare features which are relevant to the analysis.

Origin of Pareto-like spatial distributions in ecosystems.

PubMed

Manor, Alon; Shnerb, Nadav M

2008-12-31

Recent studies of cluster distribution in various ecosystems revealed Pareto statistics for the size of spatial colonies. These results were supported by cellular automata simulations that yield robust criticality for endogenous pattern formation based on positive feedback. We show that this patch statistics is a manifestation of the law of proportionate effect. Mapping the stochastic model to a Markov birth-death process, the transition rates are shown to scale linearly with cluster size. This mapping provides a connection between patch statistics and the dynamics of the ecosystem; the "first passage time" for different colonies emerges as a powerful tool that discriminates between endogenous and exogenous clustering mechanisms. Imminent catastrophic shifts (such as desertification) manifest themselves in a drastic change of the stability properties of spatial colonies.

Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead-zinc smelter.

PubMed

Garcia-Vargas, Gonzalo G; Rothenberg, Stephen J; Silbergeld, Ellen K; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J; Steuerwald, Amy J; Navas-Acién, Ana; Guallar, Eliseo

2014-11-01

High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world's fourth largest lead-zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12-15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6-14.7 μg/dl) and urine cadmium (0.18-1.14 μg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28-0.93 μg/g creatinine) and uranium (0.07-0.13 μg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods.

Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead–zinc smelter

PubMed Central

Garcia-Vargas, Gonzalo G.; Rothenberg, Stephen J.; Silbergeld, Ellen K.; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J.; Steuerwald, Amy J.; Navas-Acién, Ana; Guallar, Eliseo

2016-01-01

High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world’s fourth largest lead–zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12–15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6–14.7 µg/dl) and urine cadmium (0.18–1.14 µg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28–0.93 µg/g creatinine) and uranium (0.07–0.13 µg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods. PMID:24549228

Theria-Specific Homeodomain and cis-Regulatory Element Evolution of the Dlx3–4 Bigene Cluster in 12 Different Mammalian Species

PubMed Central

SUMIYAMA, KENTA; MIYAKE, TSUTOMU; GRIMWOOD, JANE; STUART, ANDREW; DICKSON, MARK; SCHMUTZ, JEREMY; RUDDLE, FRANK H.; MYERS, RICHARD M.; AMEMIYA, CHRIS T.

2013-01-01

The mammalian Dlx3 and Dlx4 genes are configured as a bigene cluster, and their respective expression patterns are controlled temporally and spatially by cis-elements that largely reside within the intergenic region of the cluster. Previous work revealed that there are conspicuously conserved elements within the intergenic region of the Dlx3–4 bigene clusters of mouse and human. In this paper we have extended these analyses to include 12 additional mammalian taxa (including a marsupial and a monotreme) in order to better define the nature and molecular evolutionary trends of the coding and non-coding functional elements among morphologically divergent mammals. Dlx3–4 regions were fully sequenced from 12 divergent taxa of interest. We identified three theria-specific amino acid replacements in homeodomain of Dlx4 gene that functions in placenta. Sequence analyses of constrained nucleotide sites in the intergenic non-coding region showed that many of the intergenic conserved elements are highly conserved and have evolved slowly within the mammals. In contrast, a branchial arch/craniofacial enhancer I37-2 exhibited accelerated evolution at the branch between the monotreme and therian common ancestor despite being highly conserved among therian species. Functional analysis of I37-2 in transgenic mice has shown that the equivalent region of the platypus fails to drive transcriptional activity in branchial arches. These observations, taken together with our molecular evolutionary data, suggest that theria-specific episodic changes in the I37-2 element may have contributed to craniofacial innovation at the base of the mammalian lineage. PMID:22951979

Pattern-formation mechanisms in motility mutants of Myxococcus xanthus

PubMed Central

Starruß, Jörn; Peruani, Fernando; Jakovljevic, Vladimir; Søgaard-Andersen, Lotte; Deutsch, Andreas; Bär, Markus

2012-01-01

Formation of spatial patterns of cells is a recurring theme in biology and often depends on regulated cell motility. Motility of the rod-shaped cells of the bacterium Myxococcus xanthus depends on two motility machineries, type IV pili (giving rise to S-motility) and the gliding motility apparatus (giving rise to A-motility). Cell motility is regulated by occasional reversals. Moving M. xanthus cells can organize into spreading colonies or spore-filled fruiting bodies, depending on their nutritional status. To ultimately understand these two pattern-formation processes and the contributions by the two motility machineries, as well as the cell reversal machinery, we analyse spatial self-organization in three M. xanthus strains: (i) a mutant that moves unidirectionally without reversing by the A-motility system only, (ii) a unidirectional mutant that is also equipped with the S-motility system, and (iii) the wild-type that, in addition to the two motility systems, occasionally reverses its direction of movement. The mutant moving by means of the A-engine illustrates that collective motion in the form of large moving clusters can arise in gliding bacteria owing to steric interactions of the rod-shaped cells, without the need of invoking any biochemical signal regulation. The two-engine strain mutant reveals that the same phenomenon emerges when both motility systems are present, and as long as cells exhibit unidirectional motion only. From the study of these two strains, we conclude that unidirectional cell motion induces the formation of large moving clusters at low and intermediate densities, while it results in vortex formation at very high densities. These findings are consistent with what is known from self-propelled rod models, which strongly suggests that the combined effect of self-propulsion and volume exclusion interactions is the pattern-formation mechanism leading to the observed phenomena. On the other hand, we learn that when cells occasionally reverse their moving direction, as observed in the wild-type, cells form small but strongly elongated clusters and self-organize into a mesh-like structure at high enough densities. These results have been obtained from a careful analysis of the cluster statistics of ensembles of cells, and analysed in the light of a coagulation Smoluchowski equation with fragmentation. PMID:24312730

Degree-based statistic and center persistency for brain connectivity analysis.

PubMed

Yoo, Kwangsun; Lee, Peter; Chung, Moo K; Sohn, William S; Chung, Sun Ju; Na, Duk L; Ju, Daheen; Jeong, Yong

2017-01-01

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Does the spatial arrangement of vegetation and anthropogenic land cover features matter? Case studies of urban warming and cooling in Phoenix and Las Vegas

NASA Astrophysics Data System (ADS)

Myint, S. W.; Zheng, B.; Fan, C.; Kaplan, S.; Brazel, A.; Middel, A.; Smith, M.

2014-12-01

While the relationship between fractional cover of anthropogenic and vegetation features and the urban heat island has been well studied, the effect of spatial arrangements (e.g., clustered, dispersed) of these features on urban warming or cooling are not well understood. The goal of this study is to examine if and how spatial configuration of land cover features influence land surface temperatures (LST) in urban areas. This study focuses on Phoenix, AZ and Las Vegas, NV that have undergone dramatic urban expansion. The data used to classify detailed urban land cover types include Geoeye-1 (Las Vegas) and QuickBird (Phoenix). The Geoeye-1 image (3 m resolution) was acquired on October 12, 2011 and the QuickBird image (2.4 m resolution) was taken on May 29, 2007. Classification was performed using object based image analysis (OBIA). We employed a spatial autocorrelation approach (i.e., Moran's I) that measures the spatial dependence of a point to its neighboring points and describes how clustered or dispersed points are arranged in space. We used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired over Phoenix (daytime on June 10, 2011 and nighttime on October 17, 2011) and Las Vegas (daytime on July 6, 2005 and nighttime on August 27, 2005) to examine daytime and nighttime LST with regards to the spatial arrangement of anthropogenic and vegetation features. We spatially correlate Moran's I values of each land cover per surface temperature, and develop regression models. The spatial configuration of grass and trees shows strong negative correlations with LST, implying that clustered vegetation lowers surface temperatures more effectively. In contrast, a clustered spatial arrangement of anthropogenic land-cover features, especially impervious surfaces, significantly elevates surface temperatures. Results from this study suggest that the spatial configuration of anthropogenic and vegetation features influence urban warming and cooling.

Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

PubMed

Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

2017-08-07

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.

Spatial Relationships of Sector-Specific Fossil-fuel CO2 Emissions in the United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Yuyu; Gurney, Kevin R.

2011-07-01

Quantification of the spatial distribution of sector-specific fossil fuel CO2 emissions provides strategic information to public and private decision-makers on climate change mitigation options and can provide critical constraints to carbon budget studies being performed at the national to urban scales. This study analyzes the spatial distribution and spatial drivers of total and sectoral fossil fuel CO2 emissions at the state and county levels in the United States. The spatial patterns of absolute versus per capita fossil fuel CO2 emissions differ substantially and these differences are sector-specific. Area-based sources such as those in the residential and commercial sectors are drivenmore » by a combination of population and surface temperature with per capita emissions largest in the northern latitudes and continental interior. Emission sources associated with large individual manufacturing or electricity producing facilities are heterogeneously distributed in both absolute and per capita metrics. The relationship between surface temperature and sectoral emissions suggests that the increased electricity consumption due to space cooling requirements under a warmer climate may outweigh the savings generated by lessened space heating. Spatial cluster analysis of fossil fuel CO2 emissions confirms that counties with high (low) CO2 emissions tend to be clustered close to other counties with high (low) CO2 emissions and some of the spatial clustering extends to multi-state spatial domains. This is particularly true for the residential and transportation sectors, suggesting that emissions mitigation policy might best be approached from the regional or multi-state perspective. Our findings underscore the potential for geographically focused, sector-specific emissions mitigation strategies and the importance of accurate spatial distribution of emitting sources when combined with atmospheric monitoring via aircraft, satellite and in situ measurements. Keywords: Fossil-fuel; Carbon dioxide emissions; Sectoral; Spatial cluster; Emissions mitigation policy« less

Spatial patterns and origins of heavy metals in Sheyang River catchment in Jiangsu, China based on geographically weighted regression.

PubMed

Wu, Shan-Shan; Yang, Hao; Guo, Fei; Han, Rui-Ming

2017-02-15

Multivariate statistical analyses combined with geographically weighted regression (GWR) were used to identify spatial variations of heavy metals in sediments and to examine relationships between metal pollution and land use practices in watersheds, including urban land, agriculture land, forest and water bodies. Seven metals (Cu, Zn, Pb, Cr, Ni, Mn and Fe) of sediments were measured at 31 sampling sites in Sheyang River. Most metals were under a certain degree enrichment based on the enrichment factors. Cluster analysis grouped all sites into four statistically significant cluster, severely contaminated areas were concentrated in areas with intensive human activities. Correlation analysis and PCA indicated Cu, Zn and Pb were derived from anthropogenic activities, while the sources of Cr and Ni were complicated. However, Fe and Mn originated from natural sources. According to results of GWR, there are stronger association between metal pollution with urban land than agricultural land and forest. Moreover, the relationships between land use and metal concentration were affected by the urbanization level of watersheds. Agricultural land had a weak associated with heavy metal pollution and the relationships might be stronger in less-urbanized. This study provided useful information for the assessment and management of heavy metal hazards in studied area. Copyright © 2016 Elsevier B.V. All rights reserved.

Wavelet investigation of preferential concentration in particle-laden turbulence

NASA Astrophysics Data System (ADS)

Bassenne, Maxime; Urzay, Javier; Schneider, Kai; Moin, Parviz

2017-11-01

Direct numerical simulations of particle-laden homogeneous-isotropic turbulence are employed in conjunction with wavelet multi-resolution analyses to study preferential concentration in both physical and spectral spaces. Spatially-localized energy spectra for velocity, vorticity and particle-number density are computed, along with their spatial fluctuations that enable the quantification of scale-dependent probability density functions, intermittency and inter-phase conditional statistics. The main result is that particles are found in regions of lower turbulence spectral energy than the corresponding mean. This suggests that modeling the subgrid-scale turbulence intermittency is required for capturing the small-scale statistics of preferential concentration in large-eddy simulations. Additionally, a method is defined that decomposes a particle number-density field into the sum of a coherent and an incoherent components. The coherent component representing the clusters can be sparsely described by at most 1.6% of the total number of wavelet coefficients. An application of the method, motivated by radiative-heat-transfer simulations, is illustrated in the form of a grid-adaptation algorithm that results in non-uniform meshes refined around particle clusters. It leads to a reduction of the number of control volumes by one to two orders of magnitude. PSAAP-II Center at Stanford (Grant DE-NA0002373).

Unravelling the genetic differentiation among varieties of the Neotropical savanna tree Hancornia speciosa Gomes.

PubMed

Collevatti, Rosane G; Rodrigues, Eduardo E; Vitorino, Luciana C; Lima-Ribeiro, Matheus S; Chaves, Lázaro J; Telles, Mariana P C

2018-04-20

Spatial distribution of species genetic diversity is often driven by geographical distance (isolation by distance) or environmental conditions (isolation by environment), especially under climate change scenarios such as Quaternary glaciations. Here, we used coalescent analyses coupled with ecological niche modelling (ENM), spatially explicit quantile regression analyses and the multiple matrix regression with randomization (MMRR) approach to unravel the patterns of genetic differentiation in the widely distributed Neotropical savanna tree, Hancornia speciosa (Apocynaceae). Due to its high morphological differentiation, the species was originally classified into six botanical varieties by Monachino, and has recently been recognized as only two varieties by Flora do Brasil 2020. Thus, H. speciosa is a good biological model for learning about evolution of phenotypic plasticity under genetic and ecological effects, and predicting their responses to changing environmental conditions. We sampled 28 populations (777 individuals) of Monachino's four varieties of H. speciosa and used seven microsatellite loci to genotype them. Bayesian clustering showed five distinct genetic groups (K = 5) with high admixture among Monachino's varieties, mainly among populations in the central area of the species geographical range. Genetic differentiation among Monachino's varieties was lower than the genetic differentiation among populations within varieties, with higher within-population inbreeding. A high historical connectivity among populations of the central Cerrado shown by coalescent analyses may explain the high admixture among varieties. In addition, areas of higher climatic suitability also presented higher genetic diversity in such a way that the wide historical refugium across central Brazil might have promoted the long-term connectivity among populations. Yet, FST was significantly related to geographic distances, but not to environmental distances, and coalescent analyses and ENM predicted a demographical scenario of quasi-stability through time. Our findings show that demographical history and isolation by distance, but not isolation by environment, drove genetic differentiation of populations. Finally, the genetic clusters do not support the two recently recognized botanical varieties of H. speciosa, but partially support Monachino's classification at least for the four sampled varieties, similar to morphological variation.

Present spatial diversity patterns of Theobroma cacao L. in the neotropics reflect genetic differentiation in pleistocene refugia followed by human-influenced dispersal.

PubMed

Thomas, Evert; van Zonneveld, Maarten; Loo, Judy; Hodgkin, Toby; Galluzzi, Gea; van Etten, Jacob

2012-01-01

Cacao (Theobroma cacao L.) is indigenous to the Amazon basin, but is generally believed to have been domesticated in Mesoamerica for the production of chocolate beverage. However, cacao's distribution of genetic diversity in South America is also likely to reflect pre-Columbian human influences that were superimposed on natural processes of genetic differentiation. Here we present the results of a spatial analysis of the intra-specific diversity of cacao in Latin America, drawing on a dataset of 939 cacao trees genotypically characterized by means of 96 SSR markers. To assess continental diversity patterns we performed grid-based calculations of allelic richness, Shannon diversity and Nei gene diversity, and distinguished different spatially coherent genetic groups by means of cluster analysis. The highest levels of genetic diversity were observed in the Upper Amazon areas from southern Peru to the Ecuadorian Amazon and the border areas between Colombia, Peru and Brazil. On the assumption that the last glaciation (22,000-13,000 BP) had the greatest pre-human impact on the current distribution and diversity of cacao, we modeled the species' Pleistocene niche suitability and overlaid this with present-day diversity maps. The results suggest that cacao was already widely distributed in the Western Amazon before the onset of glaciation. During glaciations, cacao populations were likely to have been restricted to several refugia where they probably underwent genetic differentiation, resulting in a number of genetic clusters which are representative for, or closest related to, the original wild cacao populations. The analyses also suggested that genetic differentiation and geographical distribution of a number of other clusters seem to have been significantly affected by processes of human management and accompanying genetic bottlenecks. We discuss the implications of these results for future germplasm collection and in situ, on farm and ex situ conservation of cacao.

Present Spatial Diversity Patterns of Theobroma cacao L. in the Neotropics Reflect Genetic Differentiation in Pleistocene Refugia Followed by Human-Influenced Dispersal

PubMed Central

Thomas, Evert; van Zonneveld, Maarten; Loo, Judy; Hodgkin, Toby; Galluzzi, Gea; van Etten, Jacob

2012-01-01

Cacao (Theobroma cacao L.) is indigenous to the Amazon basin, but is generally believed to have been domesticated in Mesoamerica for the production of chocolate beverage. However, cacao’s distribution of genetic diversity in South America is also likely to reflect pre-Columbian human influences that were superimposed on natural processes of genetic differentiation. Here we present the results of a spatial analysis of the intra-specific diversity of cacao in Latin America, drawing on a dataset of 939 cacao trees genotypically characterized by means of 96 SSR markers. To assess continental diversity patterns we performed grid-based calculations of allelic richness, Shannon diversity and Nei gene diversity, and distinguished different spatially coherent genetic groups by means of cluster analysis. The highest levels of genetic diversity were observed in the Upper Amazon areas from southern Peru to the Ecuadorian Amazon and the border areas between Colombia, Peru and Brazil. On the assumption that the last glaciation (22,000–13,000 BP) had the greatest pre-human impact on the current distribution and diversity of cacao, we modeled the species’ Pleistocene niche suitability and overlaid this with present-day diversity maps. The results suggest that cacao was already widely distributed in the Western Amazon before the onset of glaciation. During glaciations, cacao populations were likely to have been restricted to several refugia where they probably underwent genetic differentiation, resulting in a number of genetic clusters which are representative for, or closest related to, the original wild cacao populations. The analyses also suggested that genetic differentiation and geographical distribution of a number of other clusters seem to have been significantly affected by processes of human management and accompanying genetic bottlenecks. We discuss the implications of these results for future germplasm collection and in situ, on farm and ex situ conservation of cacao. PMID:23112832

Spatio-Temporal Distribution of Mycobacterium tuberculosis Complex Strains in Ghana.

PubMed

Yeboah-Manu, Dorothy; Asare, P; Asante-Poku, A; Otchere, I D; Osei-Wusu, S; Danso, E; Forson, A; Koram, K A; Gagneux, Sebastien

2016-01-01

There is a perception that genomic differences in the species/lineages of the nine species making the Mycobacterium tuberculosis complex (MTBC) may affect the efficacy of distinct control tools in certain geographical areas. We therefore analyzed the prevalence and spatial distribution of MTBC species and lineages among isolates from pulmonary TB cases over an 8-year period, 2007-2014. Mycobacterial species isolated by culture from consecutively recruited pulmonary tuberculosis patients presenting at selected district/sub-district health facilities were confirmed as MTBC by IS6110 and rpoß PCR and further assigned lineages and sub lineages by spoligotyping and large sequence polymorphism PCR (RDs 4, 9, 12, 702, 711) assays. Patient characteristics, residency, and risks were obtained with a structured questionnaire. We used SaTScan and ArcMap analyses to identify significantly clustered MTBC lineages within selected districts and spatial display, respectively. Among 2,551 isolates, 2,019 (79.1%), 516 (20.2%) and 16 (0.6%) were identified as M. tuberculosis sensu stricto (MTBss), M. africanum (Maf), 15 M. bovis and 1 M. caprae, respectively. The proportions of MTBss and Maf were fairly constant within the study period. Maf spoligotypes were dominated by Spoligotype International Type (SIT) 331 (25.42%), SIT 326 (15.25%) and SIT 181 (14.12%). We found M. bovis to be significantly higher in Northern Ghana (1.9% of 212) than Southern Ghana (0.5% of 2339) (p = 0.020). Using the purely spatial and space-time analysis, seven significant MTBC lineage clusters (p< 0.05) were identified. Notable among the clusters were Ghana and Cameroon sub-lineages found to be associated with north and south, respectively. This study demonstrated that overall, 79.1% of TB in Ghana is caused by MTBss and 20% by M. africanum. Unlike some West African Countries, we did not observe a decline of Maf prevalence in Ghana.

Discovering shared segments on the migration route of the bar-headed goose by time-based plane-sweeping trajectory clustering

USGS Publications Warehouse

Luo, Ze; Baoping, Yan; Takekawa, John Y.; Prosser, Diann J.

2012-01-01

We propose a new method to help ornithologists and ecologists discover shared segments on the migratory pathway of the bar-headed geese by time-based plane-sweeping trajectory clustering. We present a density-based time parameterized line segment clustering algorithm, which extends traditional comparable clustering algorithms from temporal and spatial dimensions. We present a time-based plane-sweeping trajectory clustering algorithm to reveal the dynamic evolution of spatial-temporal object clusters and discover common motion patterns of bar-headed geese in the process of migration. Experiments are performed on GPS-based satellite telemetry data from bar-headed geese and results demonstrate our algorithms can correctly discover shared segments of the bar-headed geese migratory pathway. We also present findings on the migratory behavior of bar-headed geese determined from this new analytical approach.

Spatial evolution of laser filaments in turbulent air

NASA Astrophysics Data System (ADS)

Zeng, Tao; Zhu, Shiping; Zhou, Shengling; He, Yan

2018-04-01

In this study, the spatial evolution properties of laser filament clusters in turbulent air were evaluated using numerical simulations. Various statistical parameters were calculated, such as the percolation probability, filling factor, and average cluster size. The results indicate that turbulence-induced multi-filamentation can be described as a new phase transition universality class. In addition, during this process, the relationship between the average cluster size and filling factor could be fit by a power function. Our results are valuable for applications involving filamentation that can be influenced by the geometrical features of multiple filaments.

Identifying clusters of active transportation using spatial scan statistics.

PubMed

Huang, Lan; Stinchcomb, David G; Pickle, Linda W; Dill, Jennifer; Berrigan, David

2009-08-01

There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

Identifying Clusters of Active Transportation Using Spatial Scan Statistics

PubMed Central

Huang, Lan; Stinchcomb, David G.; Pickle, Linda W.; Dill, Jennifer; Berrigan, David

2009-01-01

Background There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Methods Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007–2008. Results Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. Conclusions The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units. PMID:19589451

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast

PubMed Central

2013-01-01

Background The distribution of anopheline mosquitoes is determined by temporally dynamic environmental and human-associated variables, operating over a range of spatial scales. Macro-spatial short-term trends are driven predominantly by prior (lagged) seasonal changes in climate, which regulate the abundance of suitable aquatic larval habitats. Micro-spatial distribution is determined by the location of these habitats, proximity and abundance of available human bloodmeals and prevailing micro-climatic conditions. The challenge of analysing—in a single coherent statistical framework—the lagged and distributed effect of seasonal climate changes simultaneously with the effects of an underlying hierarchy of spatial factors has hitherto not been addressed. Methods Data on Anopheles gambiae sensu stricto and A. funestus collected from households in Kilifi district, Kenya, were analysed using polynomial distributed lag generalized linear mixed models (PDL GLMMs). Results Anopheline density was positively and significantly associated with amount of rainfall between 4 to 47 days, negatively and significantly associated with maximum daily temperature between 5 and 35 days, and positively and significantly associated with maximum daily temperature between 29 and 48 days in the past (depending on Anopheles species). Multiple-occupancy households harboured greater mosquito numbers than single-occupancy households. A significant degree of mosquito clustering within households was identified. Conclusions The PDL GLMMs developed here represent a generalizable framework for analysing hierarchically-structured data in combination with explanatory variables which elicit lagged effects. The framework is a valuable tool for facilitating detailed understanding of determinants of the spatio-temporal distribution of Anopheles. Such understanding facilitates delivery of targeted, cost-effective and, in certain circumstances, preventative antivectorial interventions against malaria. PMID:24330615

Using satellite telemetry to define spatial population structure in polar bears in the Norwegian and western Russian Arctic

USGS Publications Warehouse

Mauritzen, Mette; Derocher, Andrew E.; Wiig, Øystein; Belikov, Stanislav; Boltunov, Andrei N.; Garner, Gerald W.

2002-01-01

1. Animal populations, defined by geographical areas within a species’ distribution where population dynamics are largely regulated by births and deaths rather than by migration from surrounding areas, may be the correct unit for wildlife management. However, in heterogeneous landscapes varying habitat quality may yield subpopulations with distinct patterns in resource use and demography significant to the dynamics of populations.2. To define the spatial population structure of polar bears Ursus maritimus in the Norwegian and western Russian Arctic, and to assess the existence of a shared population between the two countries, we analysed satellite telemetry data obtained from 105 female polar bears over 12 years.3. Using both cluster analyses and home-range estimation methods, we identified five population units inhabiting areas with different sea-ice characteristics and prey availability.4. The continuous distribution of polar bear positions indicated that the different subpopulations formed one continuous polar bear population in the Norwegian and western Russian Arctic. Hence, Norway and Russia have a shared management responsibility.5. The spatial population structure identified will provide a guide for evaluating geographical patterns in polar bear ecology, the dynamics of polar bear–seal relationships and the effects of habitat alteration due to climate change. The work illustrates the importance of defining population borders and subpopulation structure in understanding the dynamics and management of larger animals.

Optical/Near-IR spatially resolved study of the H II galaxy Tol 02★

NASA Astrophysics Data System (ADS)

Torres-Campos, A.; Terlevich, E.; Rosa-González, D.; Terlevich, R.; Telles, E.; Díaz, A. I.

2017-11-01

The main goal of this study is to characterize the stellar populations in very low-metallicity galaxies. We have obtained broad U, B, R, I, J, H, K, intermediate Strömgren y and narrow H α and [O III] deep images of the Wolf-Rayet, blue compact dwarf, H II galaxy Tol 02. We have analysed the low surface brightness component, the stellar cluster complexes and the H II regions. The stellar populations in the galaxy have been characterized by comparing the observed broad-band colours with those of single stellar population models. The main results are consistent with Tol 02 being formed by a 1.5 Gyr old disc component at the centre of which a group of eight massive (>104 M⊙) stellar cluster clumps is located. Six of these clumps are 10 Myr old and their near-infrared colours suggest that their light is dominated by Red Supergiant (RSG) stars, the other two are young Wolf-Rayet cluster candidates of ages 3 and 5 Myr, respectively. 12 H II regions in the star-forming region of the galaxy are also identified. These are immersed in a diffuse H α and [O III] emission that spreads towards the north and south covering the old stellar disc. Our spatial-temporal analysis shows that star formation is more likely stochastic and simultaneous within short time-scales. The mismatch between observations and models cannot be attributed alone to a mistreat of the RSG phase and still needs to be further investigated.

Space-time variations in child mortality in a rural South African population with high HIV prevalence (2000-2014).

PubMed

Tlou, Boikhutso; Sartorius, Benn; Tanser, Frank

2017-01-01

The aim of the study was to identify the key determinants of child mortality 'hot-spots' in space and time. Comprehensive population-based mortality data collected between 2000 and 2014 by the Africa Centre Demographic Information System located in the UMkhanyakude District of KwaZulu-Natal Province, South Africa, was analysed. We assigned all mortality events and person-time of observation for children <5 years of age to an exact homestead of residence (mapped to <2m accuracy as part of the DSA platform). Using these exact locations, both the Kulldorff and Tango spatial scan statistics for regular and irregular shaped cluster detection were used to identify clusters of childhood mortality events in both space and time. Of the 49 986 children aged < 5 years who resided in the study area between 2000 and 2014, 2010 (4.0%) died. Childhood mortality decreased by 80% over the period from >20 per 1000 person-years in 2001-2003 to 4 per 1000 person-years in 2014. The two scanning spatial techniques identified two high-risk clusters for child mortality along the eastern border of the study site near the national highway, with a relative risk of 2.10 and 1.91 respectively. The high-risk communities detected in this work, and the differential risk factor profile of these communities, can assist public health professionals to identify similar populations in other parts of rural South Africa. Identifying child mortality hot-spots will potentially guide policy interventions in rural, resource-limited settings.

Uncovering multiple Wolf-Rayet star clusters and the ionized ISM in Mrk 178: the closest metal-poor Wolf-Rayet H II galaxy

NASA Astrophysics Data System (ADS)

Kehrig, C.; Pérez-Montero, E.; Vílchez, J. M.; Brinchmann, J.; Kunth, D.; García-Benito, R.; Crowther, P. A.; Hernández-Fernández, J.; Durret, F.; Contini, T.; Fernández-Martín, A.; James, B. L.

2013-07-01

New integral field spectroscopy (IFS) has been obtained for the nearby metal-poor Wolf-Rayet (WR) galaxy Mrk 178 to examine the spatial correlation between its WR stars and the neighbouring ionized interstellar medium (ISM). The strength of the broad WR features and its low metallicity make Mrk 178 an intriguing object. We have detected the blue and red WR bumps in different locations across the field of view (˜300 pc × 230 pc) in Mrk 178. The study of the WR content has been extended, for the first time, beyond its brightest star-forming knot uncovering new WR star clusters. Using Large/Small Magellanic Cloud-template WR stars, we empirically estimate a minimum of ˜20 WR stars within the region sampled. Maps of the spatial distribution of the emission lines and of the physical-chemical properties of the ionized ISM have been created and analysed. Here, we refine the statistical methodology by Pérez-Montero et al. (2011) to probe the presence of variations in the ISM properties. An error-weighted mean of 12+log(O/H) = 7.72 ± 0.01 is taken as the representative oxygen abundance for Mrk 178. A localized N and He enrichment, spatially correlated with WR stars, is suggested by this analysis. Nebular He II λ4686 emission is shown to be spatially extended reaching well beyond the location of the WR stars. This spatial offset between WRs and He II emission can be explained based on the mechanical energy input into the ISM by the WR star winds, and does not rule out WR stars as the He II ionization source. We study systematic aperture effects on the detection and measurement of the WR features, using Sloan Digital Sky Survey spectra combined with the power of IFS. In this regard, the importance of targeting low metallicity nearby systems is discussed.

The Not So Simple Globular Cluster ω Cen. I. Spatial Distribution of the Multiple Stellar Populations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calamida, A.; Saha, A.; Strampelli, G.

2017-04-01

We present a multi-band photometric catalog of ≈1.7 million cluster members for a field of view of ≈2° × 2° across ω Cen. Photometry is based on images collected with the Dark Energy Camera on the 4 m Blanco telescope and the Advanced Camera for Surveys on the Hubble Space Telescope . The unprecedented photometric accuracy and field coverage allowed us, for the first time, to investigate the spatial distribution of ω Cen multiple populations from the core to the tidal radius, confirming its very complex structure. We found that the frequency of blue main-sequence stars is increasing compared to red main-sequencemore » stars starting from a distance of ≈25′ from the cluster center. Blue main-sequence stars also show a clumpy spatial distribution, with an excess in the northeast quadrant of the cluster pointing toward the direction of the Galactic center. Stars belonging to the reddest and faintest red-giant branch also show a more extended spatial distribution in the outskirts of ω Cen, a region never explored before. Both these stellar sub-populations, according to spectroscopic measurements, are more metal-rich compared to the cluster main stellar population. These findings, once confirmed, make ω Cen the only stellar system currently known where metal-rich stars have a more extended spatial distribution compared to metal-poor stars. Kinematic and chemical abundance measurements are now needed for stars in the external regions of ω Cen to better characterize the properties of these sub-populations.« less

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

PubMed Central

Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

2016-01-01

The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS) and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP) congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data. PMID:27690035

Spatial suicide clusters in Australia between 2010 and 2012: a comparison of cluster and non-cluster among young people and adults.

PubMed

Robinson, Jo; Too, Lay San; Pirkis, Jane; Spittal, Matthew J

2016-11-22

A suicide cluster has been defined as a group of suicides that occur closer together in time and space than would normally be expected. We aimed to examine the extent to which suicide clusters exist among young people and adults in Australia and to determine whether differences exist between cluster and non-cluster suicides. Suicide data were obtained from the National Coronial Information System for the period 2010 and 2012. Data on date of death, postcode, age at the time of death, sex, suicide method, ICD-10 code for cause of death, marital status, employment status, and aboriginality were retrieved. We examined the presence of spatial clusters separately for youth suicides and adult suicides using the Scan statistic. Pearson's chi-square was used to compare the characteristics of cluster suicides with non-cluster suicides. We identified 12 spatial clusters between 2010 and 2012. Five occurred among young people (n = 53, representing 5.6% [53/940] of youth suicides) and seven occurred among adults (n = 137, representing 2.3% [137/5939] of adult suicides). Clusters ranged in size from three to 21 for youth and from three to 31 for adults. When compared to adults, suicides by young people were significantly more likely to occur as part of a cluster (difference = 3.3%, 95% confidence interval [CI] = 1.8 to 4.8, p < 0.0001). Suicides by people with an Indigenous background were also significantly more likely to occur in a cluster than suicide by non-Indigenous people and this was the case among both young people and adults. Suicide clusters have a significant negative impact on the communities in which they occur. As a result it is important to find effective ways of managing and containing suicide clusters. To date there is limited evidence for the effectiveness of those strategies typically employed, in particular in Indigenous settings, and developing this evidence base needs to be a future priority. Future research that examines in more depth the socio-demographic and clinical factors associated with suicide clusters is also warranted in order that appropriate interventions can be developed.

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

PubMed

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

Interactive visual exploration and analysis of origin-destination data

NASA Astrophysics Data System (ADS)

Ding, Linfang; Meng, Liqiu; Yang, Jian; Krisp, Jukka M.

2018-05-01

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Spatial patterns of frequent floods in Switzerland

NASA Astrophysics Data System (ADS)

Schneeberger, Klaus; Rössler, Ole; Weingartner, Rolf

2017-04-01

Information about the spatial characteristics of high and extreme streamflow is often needed for an accurate analysis of flood risk and effective co-ordination of flood related activities, such as flood defence planning. In this study we analyse the spatial dependence of frequent floods in Switzerland across different scales. Firstly, we determine the average length of high and extreme flow events for 56 runoff time series of Swiss rivers. Secondly, a dependence measure expressing the probability that streamflow peaks are as high as peaks at a conditional site is used to describe and map the spatial extend of joint occurrence of frequent floods across Switzerland. Thirdly, we apply a cluster analysis to identify groups of sites that are likely to react similarly in terms of joint occurrence of high flow events. The results indicate that a time interval with a length of 3 days seems to be most appropriate to characterise the average length of high streamflow events across spatial scales. In the main Swiss basins, high and extreme streamflows were found to be asymptotically independent. In contrast, at the meso-scale distinct flood regions, which react similarly in terms of occurrence of frequent flood, were found. The knowledge about these regions can help to optimise flood defence planning or to estimate regional flood risk properly.

Environmental filtering structures tree functional traits combination and lineages across space in tropical tree assemblages.

PubMed

Asefa, Mengesha; Cao, Min; Zhang, Guocheng; Ci, Xiuqin; Li, Jie; Yang, Jie

2017-03-09

Environmental filtering consistently shapes the functional and phylogenetic structure of species across space within diverse forests. However, poor descriptions of community functional and lineage distributions across space hamper the accurate understanding of coexistence mechanisms. We combined environmental variables and geographic space to explore how traits and lineages are filtered by environmental factors using extended RLQ and fourth-corner analyses across different spatial scales. The dispersion patterns of traits and lineages were also examined in a 20-ha tropical rainforest dynamics plot in southwest China. We found that environmental filtering was detected across all spatial scales except the largest scale (100 × 100 m). Generally, the associations between functional traits and environmental variables were more or less consistent across spatial scales. Species with high resource acquisition-related traits were associated with the resource-rich part of the plot across the different spatial scales, whereas resource-conserving functional traits were distributed in limited-resource environments. Furthermore, we found phylogenetic and functional clustering at all spatial scales. Similar functional strategies were also detected among distantly related species, suggesting that phylogenetic distance is not necessarily a proxy for functional distance. In summary, environmental filtering considerably structured the trait and lineage assemblages in this species-rich tropical rainforest.

Dynamical evolution and spatial mixing of multiple population globular clusters

NASA Astrophysics Data System (ADS)

Vesperini, Enrico; McMillan, Stephen L. W.; D'Antona, Francesca; D'Ercole, Annibale

2013-03-01

Numerous spectroscopic and photometric observational studies have provided strong evidence for the widespread presence of multiple stellar populations in globular clusters. In this paper, we study the long-term dynamical evolution of multiple population clusters, focusing on the evolution of the spatial distributions of the first- (FG) and second-generation (SG) stars. In previous studies, we have suggested that SG stars formed from the ejecta of FG AGB stars are expected initially to be concentrated in the cluster inner regions. Here, by means of N-body simulations, we explore the time-scales and the dynamics of the spatial mixing of the FG and the SG populations and their dependence on the SG initial concentration. Our simulations show that, as the evolution proceeds, the radial profile of the SG/FG number ratio, NSG/NFG, is characterized by three regions: (1) a flat inner part; (2) a declining part in which FG stars are increasingly dominant and (3) an outer region where the NSG/NFG profile flattens again (the NSG/NFG profile may rise slightly again in the outermost cluster regions). Until mixing is complete and the NSG/NFG profile is flat over the entire cluster, the radial variation of NSG/NFG implies that the fraction of SG stars determined by observations covering a limited range of radial distances is not, in general, equal to the SG global fraction, (NSG/NFG)glob. The distance at which NSG/NFG equals (NSG/NFG)glob is approximately between 1 and 2 cluster half-mass radii. The time-scale for complete mixing depends on the SG initial concentration, but in all cases complete mixing is expected only for clusters in advanced evolutionary phases, having lost at least 60-70 per cent of their mass due to two-body relaxation (in addition to the early FG loss due to the cluster expansion triggered by SNII ejecta and gas expulsion).The results of our simulations suggest that in many Galactic globular clusters the SG should still be more spatially concentrated than the FG.

Spatial pattern and temporal trend of mortality due to tuberculosis 10

PubMed Central

de Queiroz, Ana Angélica Rêgo; Berra, Thaís Zamboni; Garcia, Maria Concebida da Cunha; Popolin, Marcela Paschoal; Belchior, Aylana de Souza; Yamamura, Mellina; dos Santos, Danielle Talita; Arroyo, Luiz Henrique; Arcêncio, Ricardo Alexandre

2018-01-01

ABSTRACT Objectives: To describe the epidemiological profile of mortality due to tuberculosis (TB), to analyze the spatial pattern of these deaths and to investigate the temporal trend in mortality due to tuberculosis in Northeast Brazil. Methods: An ecological study based on secondary mortality data. Deaths due to TB were included in the study. Descriptive statistics were calculated and gross mortality rates were estimated and smoothed by the Local Empirical Bayesian Method. Prais-Winsten’s regression was used to analyze the temporal trend in the TB mortality coefficients. The Kernel density technique was used to analyze the spatial distribution of TB mortality. Results: Tuberculosis was implicated in 236 deaths. The burden of tuberculosis deaths was higher amongst males, single people and people of mixed ethnicity, and the mean age at death was 51 years. TB deaths were clustered in the East, West and North health districts, and the tuberculosis mortality coefficient remained stable throughout the study period. Conclusions: Analyses of the spatial pattern and temporal trend in mortality revealed that certain areas have higher TB mortality rates, and should therefore be prioritized in public health interventions targeting the disease. PMID:29742272

[The occurrence of Echinococcus multilocularis in red foxes in lower Saxony: identification of a high risk area by spatial epidemiological cluster analysis].

PubMed

Berke, Olaf; von Keyserlingk, Michael; Broll, Susanne; Kreienbrock, Lothar

2002-01-01

There is considerable interest in the spatial distribution of Echinococcus multilocularis in red foxes (Vulpes vulpes L.), because this parasite causes the zoonoses of alveolar echinococcosis which is potentially of high fatality rate. High risk areas are known from France, Switzerland and the Swabian Alb in Germany for a long time. In this work, the spatial scan statistic is introduced as an instrument for identification and localisation of high risk areas, so called disease clusters in spatial epidemiology. The use of the spatial scan statistic along with data about the distribution of the parasite in 5365 red foxes in Lower Saxony, that were collected during 1991 to 1997, led to the identification of another high risk area. The relative risk for this disease cluster is approximated by RR = 5.03 (CI0.95(RR) = [4.27; 6.58]) for the period of 1991 to 1994 and by RR = 4.45 (CI0.95(RR) = [3.53; 5.59]) for the period of 1994 to 1997, respectively.

Microseismic Monitoring of Stimulating Shale Gas Reservoir in SW China: 2. Spatial Clustering Controlled by the Preexisting Faults and Fractures

NASA Astrophysics Data System (ADS)

Chen, Haichao; Meng, Xiaobo; Niu, Fenglin; Tang, Youcai; Yin, Chen; Wu, Furong

2018-02-01

Microseismic monitoring is crucial to improving stimulation efficiency of hydraulic fracturing treatment, as well as to mitigating potential induced seismic hazard. We applied an improved matching and locating technique to the downhole microseismic data set during one treatment stage along a horizontal well within the Weiyuan shale gas play inside Sichuan Basin in SW China, resulting in 3,052 well-located microseismic events. We employed this expanded catalog to investigate the spatiotemporal evolution of the microseismicity in order to constrain migration of the injected fluids and the associated dynamic processes. The microseismicity is generally characterized by two distinctly different clusters, both of which are highly correlated with the injection activity spatially and temporarily. The distant and well-confined cluster (cluster A) is featured by relatively large-magnitude events, with 40 events of M -1 or greater, whereas the cluster in the immediate vicinity of the wellbore (cluster B) includes two apparent lineations of seismicity with a NE-SW trending, consistent with the predominant orientation of natural fractures. We calculated the b-value and D-value, an index of fracture complexity, and found significant differences between the two seismicity clusters. Particularly, the distant cluster showed an extremely low b-value ( 0.47) and D-value ( 1.35). We speculate that the distant cluster is triggered by reactivation of a preexisting critically stressed fault, whereas the two lineations are induced by shear failures of optimally oriented natural fractures associated with fluid diffusion. In both cases, the spatially clustered microseismicity related to hydraulic stimulation is strongly controlled by the preexisting faults and fractures.

Geographic analysis of road accident severity index in Nigeria.

PubMed

Iyanda, Ayodeji E

2018-05-28

Before 2030, deaths from road traffic accidents (RTAs) will surpass cerebrovascular disease, tuberculosis, and HIV/AIDS. Yet, there is little knowledge on the geographic distribution of RTA severity in Nigeria. Accident Severity Index is the proportion of deaths that result from a road accident. This study analysed the geographic pattern of RTA severity based on the data retrieved from Federal Road Safety Corps (FRSC). The study predicted a two-year data from a historic road accident data using exponential smoothing technique. To determine spatial autocorrelation, global and local indicators of spatial association were implemented in a geographic information system. Results show significant clusters of high RTA severity among states in the northeast and the northwest of Nigeria. Hence, the findings are discussed from two perspectives: Road traffic law compliance and poor emergency response. Conclusion, the severity of RTA is high in the northern states of Nigeria, hence, RTA remains a public health concern.

A spatial analysis of population dynamics and climate change in Africa: potential vulnerability hot spots emerge where precipitation declines and demographic pressures coincide

USGS Publications Warehouse

López-Carr, David; Pricope, Narcisa G.; Aukema, Juliann E.; Jankowska, Marta M.; Funk, Christopher C.; Husak, Gregory J.; Michaelsen, Joel C.

2014-01-01

We present an integrative measure of exposure and sensitivity components of vulnerability to climatic and demographic change for the African continent in order to identify “hot spots” of high potential population vulnerability. Getis-Ord Gi* spatial clustering analyses reveal statistically significant locations of spatio-temporal precipitation decline coinciding with high population density and increase. Statistically significant areas are evident, particularly across central, southern, and eastern Africa. The highly populated Lake Victoria basin emerges as a particularly salient hot spot. People located in the regions highlighted in this analysis suffer exceptionally high exposure to negative climate change impacts (as populations increase on lands with decreasing rainfall). Results may help inform further hot spot mapping and related research on demographic vulnerabilities to climate change. Results may also inform more suitable geographical targeting of policy interventions across the continent.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience.

PubMed

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

PubMed Central

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992

Structure preserving clustering-object tracking via subgroup motion pattern segmentation

NASA Astrophysics Data System (ADS)

Fan, Zheyi; Zhu, Yixuan; Jiang, Jiao; Weng, Shuqin; Liu, Zhiwen

2018-01-01

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects' destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Spatio-temporal Analysis for New York State SPARCS Data

PubMed Central

Chen, Xin; Wang, Yu; Schoenfeld, Elinor; Saltz, Mary; Saltz, Joel; Wang, Fusheng

2017-01-01

Increased accessibility of health data provides unique opportunities to discover spatio-temporal patterns of diseases. For example, New York State SPARCS (Statewide Planning and Research Cooperative System) data collects patient level detail on patient demographics, diagnoses, services, and charges for each hospital inpatient stay and outpatient visit. Such data also provides home addresses for each patient. This paper presents our preliminary work on spatial, temporal, and spatial-temporal analysis of disease patterns for New York State using SPARCS data. We analyzed spatial distribution patterns of typical diseases at ZIP code level. We performed temporal analysis of common diseases based on 12 years’ historical data. We then compared the spatial variations for diseases with different levels of clustering tendency, and studied the evolution history of such spatial patterns. Case studies based on asthma demonstrated that the discovered spatial clusters are consistent with prior studies. We visualized our spatial-temporal patterns as animations through videos. PMID:28815148

Spatial methods for deriving crop rotation history

NASA Astrophysics Data System (ADS)

Mueller-Warrant, George W.; Trippe, Kristin M.; Whittaker, Gerald W.; Anderson, Nicole P.; Sullivan, Clare S.

2017-08-01

Benefits of converting 11 years of remote sensing classification data into cropping history of agricultural fields included measuring lengths of rotation cycles and identifying specific sequences of intervening crops grown between final years of old grass seed stands and establishment of new ones. Spatial and non-spatial methods were complementary. Individual-year classification errors were often correctable in spreadsheet-based non-spatial analysis, whereas their presence in spatial data generally led to exclusion of fields from further analysis. Markov-model testing of non-spatial data revealed that year-to-year cropping sequences did not match average frequencies for transitions among crops grown in western Oregon, implying that rotations into new grass seed stands were influenced by growers' desires to achieve specific objectives. Moran's I spatial analysis of length of time between consecutive grass seed stands revealed that clustering of fields was relatively uncommon, with high and low value clusters only accounting for 7.1 and 6.2% of fields.

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

USGS Publications Warehouse

Saman, D.M.; Cole, H.P.; Odoi, A.; Myers, M.L.; Carey, D.I.; Westneat, S.C.

2012-01-01

Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001). Conclusions: This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky. ?? 2012 Saman et al.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Smith, J.H.; Wade, T.G.; Yang, L.

2004-01-01

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priorievaluation was used as a decision-making tool when implementing the NLCD design.

Spatial and temporal trends in water quality in a Mediterranean temporary river impacted by sewage effluents.

PubMed

David, Arthur; Tournoud, Marie-George; Perrin, Jean-Louis; Rosain, David; Rodier, Claire; Salles, Christian; Bancon-Montigny, Chrystelle; Picot, Bernadette

2013-03-01

This paper analyzes how changes in hydrological conditions can affect the water quality of a temporary river that receives direct inputs of sewage effluents. Data from 12 spatial surveys of the Vène river were examined. Physico-chemical parameters, major ion, and nutrient concentrations were measured. Analyses of variance (ANOVA) and multivariate analyses were performed. ANOVA revealed significant spatial differences for conductivity and major ion but no significant spatial differences for nutrient concentrations even if higher average concentrations were observed at stations located downstream from sewage effluent discharge points. Significant temporal differences were observed among all the parameters. Karstic springs had a marked dilution effect on the direct disposal of sewage effluents. During high-flow periods, nutrient concentrations were high to moderate whereas nutrient concentrations ranged from moderate to bad at stations located downstream from the direct inputs of sewage effluents during low-flow periods. Principal component analysis showed that water quality parameters that explained the water quality of the Vène river were highly dependent on hydrological conditions. Cluster analysis showed that when the karstic springs were flowing, water quality was homogeneous all along the river, whereas when karstic springs were dry, water quality at the monitoring stations was more fragmented. These results underline the importance of considering hydrological conditions when monitoring the water quality of temporary rivers. In view of the pollution observed in the Vène river, "good water chemical status" can probably only be achieved by improving the management of sewage effluents during low-flow periods.

[Analysis of epidemic features of scrub typhus between year 2006 and 2010 in Shandong province, China].

PubMed

Ding, Lei; Li, Zhong; Wang, Xian-jun; Ding, Shu-jun; Zhang, Meng; Zhao, Zhong-tang

2012-04-01

To explore the epidemic features of scrub typhus between year 2006 and 2010 in Shandong Province. Based on the data collected through Diseases Reporting Information System between year 2006 and 2010 in Shandong province, 1291 cases of scrub typhus were selected. The study described the population distribution features of the scrub typhus patients, and explored the temporal and spatial distribution features of the disease by applying the methods of spatial thematic mapping, inverse distance weighted, spatial autocorrelation analysis, spatial clustering analysis, temporal clustering analysis and spatial variation analysis in temporal trends based on Geographic Information software (ArcGIS 9.3) and Spatial Clustering Software (SatScan 7.0). The onset age of the 1291 patients ranged between 1 and 92 years old.639 out of 1291 patients were over 55 years old, accounting for 49.5%.640 patients were male and the other 651 patients were female, occupying 49.6% and 50.4% respectively. The gender ratio was 1:1.02. Patients were found in farmers, workers, students and preschool children. However, most of the cases were farmers, up to 84.8% (1095/1291). Global Moran's I index was 0.324 (P < 0.01). The local Moran's I index in 8 locations were proved to have statistical significance (P < 0.01); all of which were H-H clustering areas. Gangcheng (38 cases), Laicheng (154 cases), Xintai (160 cases) and Donggang (105 cases) were important locations, whose local Moran's I index were 2.111, 1.642, 1.277 and 0.775 respectively. The clustering period of scrub typhus in respective year were as follows: 2006.09.23 - 2006.11.20 (202 cases), 2007.10.02 - 2007.11.11 (197 cases), 2008.09.30 - 2008.11.07 (302 cases), 2009.09.25 - 2009.11.10 (204 cases), and 2010.10.05 - 2010.11.13 (226 cases), whose RR values were separately 45.55, 34.60, 50.64, 53.09 and 79.84 (P < 0.01). Two spatial clustering area were found in the study, one was the area centered Taian and Xintai with radiation radius at 58.28 km (542 cases) and the other one was the area centered Rizhao and Donggang with radiation radius at 22.68 km (134 cases), whose RR values were 4.52 and 3.96 (P < 0.01). The spatial features of the two clustering areas were inland low hills area and coastal hills area. The highest annual growth rate of the disease was 45.04%, found in the area centered Linyi and Mengyin counties, with the radiation radius at 45.82 km. The RR value was 3.68 (P < 0.01). The majority of the scrub typhus patients were middle-aged and elderly farmers. The epidemic peak was between the last 10 days of September and the first 10 days of November. A positive spatial correlation of the disease was found; and most cases clustered in inland low hills area and costal hills area; especially the area around Linyi and Mengyin, with the highest annual growth rates of the disease.

Chromosome inversions and ecological plasticity in the main African malaria mosquitoes

PubMed Central

Ayala, Diego; Acevedo, Pelayo; Pombi, Marco; Dia, Ibrahima; Boccolini, Daniela; Costantini, Carlo; Simard, Frédéric; Fontenille, Didier

2017-01-01

Chromosome inversions have fascinated the scientific community, mainly because of their role in the rapid adaption of different taxa to changing environments. However, the ecological traits linked to chromosome inversions have been poorly studied. Here, we investigated the roles played by 23 chromosome inversions in the adaptation of the four major African malaria mosquitoes to local environments in Africa. We studied their distribution patterns by using spatially explicit modeling and characterized the ecogeographical determinants of each inversion range. We then performed hierarchical clustering and constrained ordination analyses to assess the spatial and ecological similarities among inversions. Our results show that most inversions are environmentally structured, suggesting that they are actively involved in processes of local adaptation. Some inversions exhibited similar geographical patterns and ecological requirements among the four mosquito species, providing evidence for parallel evolution. Conversely, common inversion polymorphisms between sibling species displayed divergent ecological patterns, suggesting that they might have a different adaptive role in each species. These results are in agreement with the finding that chromosomal inversions play a role in Anopheles ecotypic adaptation. This study establishes a strong ecological basis for future genome-based analyses to elucidate the genetic mechanisms of local adaptation in these four mosquitoes. PMID:28071788

Spatial patterns in vegetation fires in the Indian region.

PubMed

Vadrevu, Krishna Prasad; Badarinath, K V S; Anuradha, Eaturu

2008-12-01

In this study, we used fire count datasets derived from Along Track Scanning Radiometer (ATSR) satellite to characterize spatial patterns in fire occurrences across highly diverse geographical, vegetation and topographic gradients in the Indian region. For characterizing the spatial patterns of fire occurrences, observed fire point patterns were tested against the hypothesis of a complete spatial random (CSR) pattern using three different techniques, the quadrat analysis, nearest neighbor analysis and Ripley's K function. Hierarchical nearest neighboring technique was used to depict the 'hotspots' of fire incidents. Of the different states, highest fire counts were recorded in Madhya Pradesh (14.77%) followed by Gujarat (10.86%), Maharastra (9.92%), Mizoram (7.66%), Jharkhand (6.41%), etc. With respect to the vegetation categories, highest number of fires were recorded in agricultural regions (40.26%) followed by tropical moist deciduous vegetation (12.72), dry deciduous vegetation (11.40%), abandoned slash and burn secondary forests (9.04%), tropical montane forests (8.07%) followed by others. Analysis of fire counts based on elevation and slope range suggested that maximum number of fires occurred in low and medium elevation types and in very low to low-slope categories. Results from three different spatial techniques for spatial pattern suggested clustered pattern in fire events compared to CSR. Most importantly, results from Ripley's K statistic suggested that fire events are highly clustered at a lag-distance of 125 miles. Hierarchical nearest neighboring clustering technique identified significant clusters of fire 'hotspots' in different states in northeast and central India. The implications of these results in fire management and mitigation were discussed. Also, this study highlights the potential of spatial point pattern statistics in environmental monitoring and assessment studies with special reference to fire events in the Indian region.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

Coastal Bacterioplankton Community Dynamics in Response to a Natural Disturbance

PubMed Central

Rappé, Michael S.

2013-01-01

In order to characterize how disturbances to microbial communities are propagated over temporal and spatial scales in aquatic environments, the dynamics of bacterial assemblages throughout a subtropical coastal embayment were investigated via SSU rRNA gene analyses over an 8-month period, which encompassed a large storm event. During non-perturbed conditions, sampling sites clustered into three groups based on their microbial community composition: an offshore oceanic group, a freshwater group, and a distinct and persistent coastal group. Significant differences in measured environmental parameters or in the bacterial community due to the storm event were found only within the coastal cluster of sampling sites, and only at 5 of 12 locations; three of these sites showed a significant response in both environmental and bacterial community characteristics. These responses were most pronounced at sites close to the shoreline. During the storm event, otherwise common bacterioplankton community members such as marine Synechococcus sp. and members of the SAR11 clade of Alphaproteobacteria decreased in relative abundance in the affected coastal zone, whereas several lineages of Gammaproteobacteria, Betaproteobacteria, and members of the Roseobacter clade of Alphaproteobacteria increased. The complex spatial patterns in both environmental conditions and microbial community structure related to freshwater runoff and wind convection during the perturbation event leads us to conclude that spatial heterogeneity was an important factor influencing both the dynamics and the resistance of the bacterioplankton communities to disturbances throughout this complex subtropical coastal system. This heterogeneity may play a role in facilitating a rapid rebound of regions harboring distinctly coastal bacterioplankton communities to their pre-disturbed taxonomic composition. PMID:23409156

Potentially inappropriate medication prescribing is associated with socioeconomic factors: a spatial analysis in the French Nord-Pas-de-Calais Region.

PubMed

Beuscart, Jean-Baptiste; Genin, Michael; Dupont, Corrine; Verloop, David; Duhamel, Alain; Defebvre, Marguerite-Marie; Puisieux, François

2017-07-01

potentially inappropriate medication (PIM) prescribing is common in older people and leads to adverse events and hospital admissions. to determine whether prevalence of PIM prescribing varies according to healthcare supply and socioeconomic status. all prescriptions dispensed at community pharmacies for patients aged 75 and older between 1 January  and 31 March 2012 were retrieved from French Health Insurance Information System of the Nord-Pas-de-Calais Region for patients affiliated to the Social Security scheme. PIM was defined according to the French list of Laroche. The geographic distribution of PIM prescribing in this area was analysed using spatial scan statistics. overall, 65.6% (n = 207,979) of people aged 75 years and over living in the Nord-Pas-de-Calais Region were included. Among them, 32.6% (n = 67,863) received at least one PIM. The spatial analysis identified 16 and 10 clusters of municipalities with a high and a low prevalence of PIM prescribing, respectively. Municipalities with a low prevalence of PIM were characterised by a high socioeconomic status whereas those with a high prevalence of PIM were mainly characterised by a low socioeconomic status, such as a high unemployment rate and low household incomes. Markers of healthcare supply were weakly associated with high or low prevalence clusters. significant geographic variation in PIM prescribing was observed in the study territory and was mainly associated with socioeconomic factors. © The Author 2017. Published by Oxford University Press on behalf of the British Geriatrics Society.All rights reserved. For permissions, please email: journals.permissions@oup.com

Multiscale temporal variability and regional patterns in 555 years of conterminous U.S. streamflow

NASA Astrophysics Data System (ADS)

Ho, Michelle; Lall, Upmanu; Sun, Xun; Cook, Edward R.

2017-04-01

The development of paleoclimate streamflow reconstructions in the conterminous United States (CONUS) has provided water resource managers with improved insights into multidecadal and centennial scale variability that cannot be reliably detected using shorter instrumental records. Paleoclimate streamflow reconstructions have largely focused on individual catchments limiting the ability to quantify variability across the CONUS. The Living Blended Drought Atlas (LBDA), a spatially and temporally complete 555 year long paleoclimate record of summer drought across the CONUS, provides an opportunity to reconstruct and characterize streamflow variability at a continental scale. We explore the validity of the first paleoreconstructions of streamflow that span the CONUS informed by the LBDA targeting a set of U.S. Geological Survey streamflow sites. The reconstructions are skillful under cross validation across most of the country, but the variance explained is generally low. Spatial and temporal structures of streamflow variability are analyzed using hierarchical clustering, principal component analysis, and wavelet analyses. Nine spatially coherent clusters are identified. The reconstructions show signals of contemporary droughts such as the Dust Bowl (1930s) and 1950s droughts. Decadal-scale variability was detected in the late 1900s in the western U.S., however, similar modes of temporal variability were rarely present prior to the 1950s. The twentieth century featured longer wet spells and shorter dry spells compared with the preceding 450 years. Streamflows in the Pacific Northwest and Northeast are negatively correlated with the central U.S. suggesting the potential to mitigate some drought impacts by balancing economic activities and insurance pools across these regions during major droughts.

Spatial study of homicide rates in the state of Bahia, Brazil, 1996-2010

PubMed Central

de Souza, Tiago Oliveira; Pinto, Liana Wernersbach; de Souza, Edinilsa Ramos

2014-01-01

OBJECTIVE To analyze the spatial distribution of homicide mortality in the state of Bahia, Northeastern Brazil. METHODS Ecological study of the 15 to 39-year old male population in the state of Bahia in the period 1996-2010. Data from the Mortality Information System, relating to homicide (X85-Y09) and population estimates from the Brazilian Institute of Geography and Statistics were used. The existence of spatial correlation, the presence of clusters and critical areas of the event studied were analyzed using Moran’s I Global and Local indices. RESULTS A non-random spatial pattern was observed in the distribution of rates, as was the presence of three clusters, the first in the north health district, the second in the eastern region, and the third cluster included townships in the south and the far south of Bahia. CONCLUSIONS The homicide mortality in the three different critical areas requires further studies that consider the socioeconomic, cultural and environmental characteristics in order to guide specific preventive and interventionist practices. PMID:25119942

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

PubMed

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-11-11

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

Spatial clustering by disease severity among reported Rocky Mountain spotted fever cases in the United States, 2001-2005.

PubMed

Adjemian, Jennifer Zipser; Krebs, John; Mandel, Eric; McQuiston, Jennifer

2009-01-01

Rocky Mountain spotted fever (RMSF) occurs throughout much of the United States, ranging in clinical severity from moderate to fatal infection. Yet, little is known about possible differences among severity levels across geographic locations. To identify significant spatial clusters of severe and non-severe disease, RMSF cases reported to Centers for Disease Control and Prevention (CDC) were geocoded by county and classified by severity level. The statistical software program SaTScan was used to detect significant spatial clusters. Of 4,533 RMSF cases reported, 1,089 hospitalizations (168 with complications) and 23 deaths occurred. Significant clusters of 6 deaths (P = 0.05, RR = 11.4) and 19 hospitalizations with complications (P = 0.02, RR = 3.45) were detected in southwestern Tennessee. Two geographic areas were identified in north-central North Carolina with unusually low rates of severity (P = 0.001, RR = 0.62 and P = 0.001, RR = 0.45, respectively). Of all hospitalizations, 20% were clustered in central Oklahoma (P = 0.02, RR = 1.43). Significant geographic differences in severity were observed, suggesting that biologic and/or anthropogenic factors may be impacting RMSF epidemiology in the United States.

The Role of Semantic Clustering in Optimal Memory Foraging.

PubMed

Montez, Priscilla; Thompson, Graham; Kello, Christopher T

2015-11-01

Recent studies of semantic memory have investigated two theories of optimal search adopted from the animal foraging literature: Lévy flights and marginal value theorem. Each theory makes different simplifying assumptions and addresses different findings in search behaviors. In this study, an experiment is conducted to test whether clustering in semantic memory may play a role in evidence for both theories. Labeled magnets and a whiteboard were used to elicit spatial representations of semantic knowledge about animals. Category recall sequences from a separate experiment were used to trace search paths over the spatial representations of animal knowledge. Results showed that spatial distances between animal names arranged on the whiteboard were correlated with inter-response intervals (IRIs) during category recall, and distributions of both dependent measures approximated inverse power laws associated with Lévy flights. In addition, IRIs were relatively shorter when paths first entered animal clusters, and longer when they exited clusters, which is consistent with marginal value theorem. In conclusion, area-restricted searches over clustered semantic spaces may account for two different patterns of results interpreted as supporting two different theories of optimal memory foraging. Copyright © 2015 Cognitive Science Society, Inc.

Automated feature extraction and spatial organization of seafloor pockmarks, Belfast Bay, Maine, USA

USGS Publications Warehouse

Andrews, Brian D.; Brothers, Laura L.; Barnhardt, Walter A.

2010-01-01

Seafloor pockmarks occur worldwide and may represent millions of m3 of continental shelf erosion, but few numerical analyses of their morphology and spatial distribution of pockmarks exist. We introduce a quantitative definition of pockmark morphology and, based on this definition, propose a three-step geomorphometric method to identify and extract pockmarks from high-resolution swath bathymetry. We apply this GIS-implemented approach to 25 km2 of bathymetry collected in the Belfast Bay, Maine USA pockmark field. Our model extracted 1767 pockmarks and found a linear pockmark depth-to-diameter ratio for pockmarks field-wide. Mean pockmark depth is 7.6 m and mean diameter is 84.8 m. Pockmark distribution is non-random, and nearly half of the field's pockmarks occur in chains. The most prominent chains are oriented semi-normal to the steepest gradient in Holocene sediment thickness. A descriptive model yields field-wide spatial statistics indicating that pockmarks are distributed in non-random clusters. Results enable quantitative comparison of pockmarks in fields worldwide as well as similar concave features, such as impact craters, dolines, or salt pools.

Shallow plumbing systems inferred from spatial analysis of pockmark arrays

NASA Astrophysics Data System (ADS)

Maia, A.; Cartwright, J. A.; Andersen, E.

2016-12-01

This study describes and analyses an extraordinary array of pockmarks at the modern seabed of the Lower Congo Basin (offshore Angola), in order to understand the fluid migration routes and shallow plumbing system of the area. The 3D seismic visualization of feeding conduits (pipes) allowed the identification of the source interval for the fluids expelled during pockmark formation. Spatial statistics are used to show the relationship between the underlying (polarised) polygonal fault (PPFs) patterns and seabed pockmarks distributions. Our results show PPFs control the linear arrangement of pockmarks and feeder pipes along fault strike, but faults do not act as conduits. Spatial statistics also revealed pockmark occurrence is not considered to be random, especially at short distances to nearest neighbours (<200m) where anti-clustering distributions suggest the presence of an exclusion zone around each pockmark in which no other pockmark will form. The results of this study are relevant for the understanding of shallow fluid plumbing systems in offshore settings, with implications on our current knowledge of overall fluid flow systems in hydrocarbon-rich continental margins.

Spatial-temporal clustering of tornadoes

NASA Astrophysics Data System (ADS)

Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

2016-12-01

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated individual tornadoes with specified supercell thunderstorms. Our analysis of the 3 May 1999 tornado outbreak directly associated linear features in the largely random spatial-temporal analysis with several supercell thunderstorms, which we then confirmed using model scenarios of synthetic tornado outbreaks. We suggest that it may be possible to develop a semi-automated modelling of tornado touchdowns to match the type of observations made on the 3 May 1999 outbreak.

Spatial-Temporal Clustering of Tornadoes

NASA Astrophysics Data System (ADS)

Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

2017-04-01

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated individual tornadoes with specified supercell thunderstorms. Our analysis of the 3 May 1999 tornado outbreak directly associated linear features in the largely random spatial-temporal analysis with several supercell thunderstorms, which we then confirmed using model scenarios of synthetic tornado outbreaks. We suggest that it may be possible to develop a semi-automated modelling of tornado touchdowns to match the type of observations made on the 3 May 1999 outbreak.

Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2015-12-01

Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was observed in different plot levels in the paddy fields from the two images. However, no such significant variation in rice genotypes at growth level was observed. Hence, the spectral information acquired from space platform can be linearly scaled to map the variation in field levels of rice crop which will be act as an informative system for rice agriculture practice.

Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles.

PubMed

Sani-Kast, Nicole; Scheringer, Martin; Slomberg, Danielle; Labille, Jérôme; Praetorius, Antonia; Ollivier, Patrick; Hungerbühler, Konrad

2015-12-01

Engineered nanoparticle (ENP) fate models developed to date - aimed at predicting ENP concentration in the aqueous environment - have limited applicability because they employ constant environmental conditions along the modeled system or a highly specific environmental representation; both approaches do not show the effects of spatial and/or temporal variability. To address this conceptual gap, we developed a novel modeling strategy that: 1) incorporates spatial variability in environmental conditions in an existing ENP fate model; and 2) analyzes the effect of a wide range of randomly sampled environmental conditions (representing variations in water chemistry). This approach was employed to investigate the transport of nano-TiO2 in the Lower Rhône River (France) under numerous sets of environmental conditions. The predicted spatial concentration profiles of nano-TiO2 were then grouped according to their similarity by using cluster analysis. The analysis resulted in a small number of clusters representing groups of spatial concentration profiles. All clusters show nano-TiO2 accumulation in the sediment layer, supporting results from previous studies. Analysis of the characteristic features of each cluster demonstrated a strong association between the water conditions in regions close to the ENP emission source and the cluster membership of the corresponding spatial concentration profiles. In particular, water compositions favoring heteroaggregation between the ENPs and suspended particulate matter resulted in clusters of low variability. These conditions are, therefore, reliable predictors of the eventual fate of the modeled ENPs. The conclusions from this study are also valid for ENP fate in other large river systems. Our results, therefore, shift the focus of future modeling and experimental research of ENP environmental fate to the water characteristic in regions near the expected ENP emission sources. Under conditions favoring heteroaggregation in these regions, the fate of the ENPs can be readily predicted. Copyright © 2014 Elsevier B.V. All rights reserved.

IRAS galaxies and the large-scale structure in the CfA slice

NASA Technical Reports Server (NTRS)

Babul, Arif; Postman, Marc

1990-01-01

The spatial distributions of the IRAS and the optical galaxies in the first CfA slice are compared. The IRAS galaxies are generally less clustered than optical ones, but their distribution is essentially identical to that of late-type optical galaxies. The discrepancy between the clustering properties of the IRAS and optical samples in the CfA slice region is found to be entirely due to the paucity of IRAS galaxies in the core of the Coma cluster. The spatial distributions of the IRAS and the optical galaxies, both late and early types, outside the dense core of the Coma cluster are entirely consistent with each other. This conflicts with the prediction of the linear biasing scenario.

Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study

PubMed Central

Zhu, Bin; Fu, Yang; Liu, Jinlin

2018-01-01

Background China is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China. Methods Global and local Moran’s I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps. Results Of the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high–low (unit with high incidence surrounded by units with high incidence, the same below) and high–high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low–low and low–high spatial cluster areas abound in provincial units in north and east China. Conclusion Despite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high–high/high–low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders. PMID:29621351

EVIDENCE FOR AN ACCRETION ORIGIN FOR THE OUTER HALO GLOBULAR CLUSTER SYSTEM OF M31

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mackey, A. D.; Huxor, A. P.; Ferguson, A. M. N.

2010-07-01

We use a sample of newly discovered globular clusters from the Pan-Andromeda Archaeological Survey (PAndAS) in combination with previously cataloged objects to map the spatial distribution of globular clusters in the M31 halo. At projected radii beyond {approx}30 kpc, where large coherent stellar streams are readily distinguished in the field, there is a striking correlation between these features and the positions of the globular clusters. Adopting a simple Monte Carlo approach, we test the significance of this association by computing the probability that it could be due to the chance alignment of globular clusters smoothly distributed in the M31 halo.more » We find that the likelihood of this possibility is low, below 1%, and conclude that the observed spatial coincidence between globular clusters and multiple tidal debris streams in the outer halo of M31 reflects a genuine physical association. Our results imply that the majority of the remote globular cluster system of M31 has been assembled as a consequence of the accretion of cluster-bearing satellite galaxies. This constitutes the most direct evidence to date that the outer halo globular cluster populations in some galaxies are largely accreted.« less

Galaxy And Mass Assembly (GAMA): the signatures of galaxy interactions as viewed from small scale galaxy clustering

NASA Astrophysics Data System (ADS)

Gunawardhana, M. L. P.; Norberg, P.; Zehavi, I.; Farrow, D. J.; Loveday, J.; Hopkins, A. M.; Davies, L. J. M.; Wang, L.; Alpaslan, M.; Bland-Hawthorn, J.; Brough, S.; Holwerda, B. W.; Owers, M. S.; Wright, A. H.

2018-06-01

Statistical studies of galaxy-galaxy interactions often utilise net change in physical properties of progenitors as a function of the separation between their nuclei to trace both the strength and the observable timescale of their interaction. In this study, we use two-point auto, cross and mark correlation functions to investigate the extent to which small-scale clustering properties of star forming galaxies can be used to gain physical insight into galaxy-galaxy interactions between galaxies of similar optical brightness and stellar mass. The Hα star formers, drawn from the highly spatially complete Galaxy And Mass Assembly (GAMA) survey, show an increase in clustering on small separations. Moreover, the clustering strength shows a strong dependence on optical brightness and stellar mass, where (1) the clustering amplitude of optically brighter galaxies at a given separation is larger than that of optically fainter systems, (2) the small scale clustering properties (e.g. the strength, the scale at which the signal relative to the fiducial power law plateaus) of star forming galaxies appear to differ as a function of increasing optical brightness of galaxies. According to cross and mark correlation analyses, the former result is largely driven by the increased dust content in optically bright star forming galaxies. The latter could be interpreted as evidence of a correlation between interaction-scale and optical brightness of galaxies, where physical evidence of interactions between optically bright star formers, likely hosted within relatively massive halos, persist over larger separations than those between optically faint star formers.

Regional and Temporal Variation in Methamphetamine-Related Incidents: Applications of Spatial and Temporal Scan Statistics

PubMed Central

Sudakin, Daniel L.

2009-01-01

Introduction This investigation utilized spatial scan statistics, geographic information systems and multiple data sources to assess spatial clustering of statewide methamphetamine-related incidents. Temporal and spatial associations with regulatory interventions to reduce access to precursor chemicals (pseudoephedrine) were also explored. Methods Four statewide data sources were utilized including regional poison control center statistics, fatality incidents, methamphetamine laboratory seizures, and hazardous substance releases involving methamphetamine laboratories. Spatial clustering of methamphetamine incidents was assessed using SaTScan™. SaTScan™ was also utilized to assess space-time clustering of methamphetamine laboratory incidents, in relation to the enactment of regulations to reduce access to pseudoephedrine. Results Five counties with a significantly higher relative risk of methamphetamine-related incidents were identified. The county identified as the most likely cluster had a significantly elevated relative risk of methamphetamine laboratories (RR=11.5), hazardous substance releases (RR=8.3), and fatalities relating to methamphetamine (RR=1.4). A significant increase in relative risk of methamphetamine laboratory incidents was apparent in this same geographic area (RR=20.7) during the time period when regulations were enacted in 2004 and 2005, restricting access to pseudoephedrine. Subsequent to the enactment of these regulations, a significantly lower rate of incidents (RR 0.111, p=0.0001) was observed over a large geographic area of the state, including regions that previously had significantly higher rates. Conclusions Spatial and temporal scan statistics can be effectively applied to multiple data sources to assess regional variation in methamphetamine-related incidents, and explore the impact of preventive regulatory interventions. PMID:19225949

Projected alignment of non-sphericities of stellar, gas, and dark matter distributions in galaxy clusters: analysis of the Horizon-AGN simulation

NASA Astrophysics Data System (ADS)

Okabe, Taizo; Nishimichi, Takahiro; Oguri, Masamune; Peirani, Sébastien; Kitayama, Tetsu; Sasaki, Shin; Suto, Yasushi

2018-07-01

While various observations measured ellipticities of galaxy clusters and alignments between orientations of the brightest cluster galaxies and their host clusters, there are only a handful of numerical simulations that implement realistic baryon physics to allow direct comparisons with those observations. Here, we investigate ellipticities of galaxy clusters and alignments between various components of them and the central galaxies in the state-of-the-art cosmological hydrodynamical simulation Horizon-AGN, which contains dark matter, stellar, and gas components in a large simulation box of (100h-1 Mpc)3 with high spatial resolution (˜1 kpc). We estimate ellipticities of total matter, dark matter, stellar, gas surface mass density distributions, X-ray surface brightness, and the Compton y-parameter of the Sunyaev-Zel'dovich effect, as well as alignments between these components and the central galaxies for 120 projected images of galaxy clusters with masses M200 > 5 × 1013 M⊙. Our results indicate that the distributions of these components are well aligned with the major axes of the central galaxies, with the root-mean-square value of differences of their position angles of ˜20°, which vary little from inner to the outer regions. We also estimate alignments of these various components with total matter distributions, and find tighter alignments than those for central galaxies with the root-mean-square value of ˜15°. We compare our results with previous observations of ellipticities and position angle alignments and find reasonable agreements. The comprehensive analysis presented in this paper provides useful prior information for analysing stacked lensing signals as well as designing future observations to study ellipticities and alignments of galaxy clusters.

Spatial patterns of the congenital heart disease prevalence among 0- to 14-year-old children in Sichuan Basin, P. R China, from 2004 to 2009

PubMed Central

2014-01-01

Background Congenital heart disease (CHD) is the most common type of major birth defects in Sichuan, the most populous province in China. The detailed etiology of CHD is unknown but some environmental factors are suspected as the cause of this disease. However, the geographical variations in CHD prevalence would be highly valuable in providing a clue on the role of the environment in CHD etiology. Here, we investigate the spatial patterns and geographic differences in CHD prevalence among 0- to 14-year-old children, discuss the possible environmental risk factors that might be associated with CHD prevalence in Sichuan Basin from 2004 to 2009. Methods The hierarchical Bayesian model was used to estimate CHD prevalence at the township level. Spatial autocorrelation statistics were performed, and a hot-spot analysis with different distance thresholds was used to identify the spatial pattern of CHD prevalence. Distribution and clustering maps were drawn using geographic information system tools. Results CHD prevalence was significantly clustered in Sichuan Basin in different spatial scale. Typical hot/cold clusters were identified, and possible CHD causes were discussed. The association between selected hypothetical environmental factors of maternal exposure and CHD prevalence was evaluated. Conclusions The largest hot-spot clustering phenomena and the CHD prevalence clustering trend among 0- to 14-year-old children in the study area showed a plausibly close similarity with those observed in the Tuojiang River Basin. The high ecological risk of heavy metal(Cd, As, and Pb)sediments in the middle and lower streams of the Tuojiang River watershed and ammonia–nitrogen pollution may have contribution to the high prevalence of CHD in this area. PMID:24924350

Mapping the spatial distribution of star formation in cluster galaxies at z ~0.5 with the Grism Lens-Amplified Survey from Space (GLASS)

NASA Astrophysics Data System (ADS)

Vulcani, Benedetta

2015-08-01

What physical processes regulate star formation in dense environments? Understanding why galaxy evolution is environment dependent is one of the key questions of current astrophysics. I will present the first characterization of the spatial distribution of star formation in cluster galaxies at z~0.5, in order to quantify the role of different physical processes that are believed to be responsible for shutting down star formation. The analysis makes use of data from the Grism Lens-Amplified Survey from Space (GLASS), a large HST cycle-21 program targeting 10 massive galaxy clusters with extensive HST imaging from CLASH and the Frontier Field Initiative. The program consists of 140 primary and 140 parallel orbits of near-infrared WCF3 and optical ACS slitless grism observations, which result in 3D spectroscopy of hundreds of galaxies. The grism data are used to produce spatially resolved maps of the star formation density, while the stellar mass density and optical surface brightness are obtained from multiband imaging. I will describe quantitative measures of the spatial location and extend of the star formation rate, showing that about half of the cluster members with significant Halpha detection have diffused star formation, larger than the optical counterpart. This suggests that star formation occurs out to larger radii than the rest frame continuum. For some systems, nuclear star forming regions are found. I will also present a comparison between the Halpha distribution observed in cluster and field galaxies. The characterization of the spatial distribution of Halpha provides a new window, yet poorly exploited, on the mechanisms that regulate star formation and morphological transformation in dense environments.

Evolution and molecular epidemiology of classical swine fever virus during a multi-annual outbreak amongst European wild boar.

PubMed

Goller, Katja V; Gabriel, Claudia; Dimna, Mireille Le; Le Potier, Marie-Frédérique; Rossi, Sophie; Staubach, Christoph; Merboth, Matthias; Beer, Martin; Blome, Sandra

2016-03-01

Classical swine fever is a viral disease of pigs that carries tremendous socio-economic impact. In outbreak situations, genetic typing is carried out for the purpose of molecular epidemiology in both domestic pigs and wild boar. These analyses are usually based on harmonized partial sequences. However, for high-resolution analyses towards the understanding of genetic variability and virus evolution, full-genome sequences are more appropriate. In this study, a unique set of representative virus strains was investigated that was collected during an outbreak in French free-ranging wild boar in the Vosges-du-Nord mountains between 2003 and 2007. Comparative sequence and evolutionary analyses of the nearly full-length sequences showed only slow evolution of classical swine fever virus strains over the years and no impact of vaccination on mutation rates. However, substitution rates varied amongst protein genes; furthermore, a spatial and temporal pattern could be observed whereby two separate clusters were formed that coincided with physical barriers.

Finer parcellation reveals detailed correlational structure of resting-state fMRI signals.

PubMed

Dornas, João V; Braun, Jochen

2018-01-15

Even in resting state, the human brain generates functional signals (fMRI) with complex correlational structure. To simplify this structure, it is common to parcellate a standard brain into coarse chunks. Finer parcellations are considered less reproducible and informative, due to anatomical and functional variability of individual brains. Grouping signals with similar local correlation profiles, restricted to each anatomical region (Tzourio-Mazoyer et al., 2002), we divide a standard brain into 758 'functional clusters' averaging 1.7cm 3 gray matter volume ('MD758' parcellation). We compare 758 'spatial clusters' of similar size ('S758'). 'Functional clusters' are spatially contiguous and cluster quality (integration and segregation of temporal variance) is far superior to 'spatial clusters', comparable to multi-modal parcellations of half the resolution (Craddock et al., 2012; Glasser et al., 2016). Moreover, 'functional clusters' capture many long-range functional correlations, with O(10 5 ) reproducibly correlated cluster pairs in different anatomical regions. The pattern of functional correlations closely mirrors long-range anatomical connectivity established by fibre tracking. MD758 is comparable to coarser parcellations (Craddock et al., 2012; Glasser et al., 2016) in terms of cluster quality, correlational structure (54% relative mutual entropy vs 60% and 61%), and sparseness (35% significant pairwise correlations vs 36% and 44%). We describe and evaluate a simple path to finer functional parcellations of the human brain. Detailed correlational structure is surprisingly consistent between individuals, opening new possibilities for comparing functional correlations between cognitive conditions, states of health, or pharmacological interventions. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

The Use of Hotspot Spatial Clustering and Multitemporal Satellite Imagery to Facilitate Peat Land Degradation in West Kalimantan, Indonesia (Case Study in Mensiku Miniwatershed of Kapuas River)

NASA Astrophysics Data System (ADS)

Yanuarsyah, I.; Suwarno, Y.; Hudjimartsu, S.

2016-11-01

Peat land in Indonesia is currently a matter of interest to economic activity. In addition to having the uniqueness of the ecosystem which is reserve a huge of biodiversity and carbon storage, peat land is grow an alternative expansion of agriculture and plantation. Mensiku miniwatershed is a subset of Kapuas Watershed with the domination of the peat soil type. It located in the upstream from the Kapuas River and supporting for the continuation of the river ecosystem. The research objective is to facilitate peat land degradation by using hotspot spatial clustering and multitemporal satellite imagery. There have three main processes which are image processing, geoprocessing and statistical process using DBSCAN to determine hotspot clustering. The trend of LUC changes for 14 years (2002 to 2016) shows that the downward occurred in secondary peat forest (0.9% per year) and swampy shrub (0.6% per year). The upward occurred in mixed farms (0.6% per year) and plantations (0.8% per year). degradation rate of peat land over 14 years about 4.6 km2 per year. Hotspot predominantly occurrence in secondary peat forest with 200-250 centimeter depth and Saprists type. DBSCAN clustering obtain 2 clusters in 2002, obtain 4 clusters in 2009 and obtain 1 clusters in 2016. Regarding LUC platform, average density value over 14 years about 0.063 hotspot per km2. DBSCAN is common used to examine the cluster and perform the distribution and density with spatial analysis

Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes

DOE PAGES

Graw, Frederik; Balagopal, Ashwin; Kandathil, Abraham J.; ...

2014-11-13

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, wemore » are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from 4-50 infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Lastly, our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.« less

Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graw, Frederik; Balagopal, Ashwin; Kandathil, Abraham J.

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, wemore » are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from 4-50 infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Lastly, our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.« less

Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure

NASA Astrophysics Data System (ADS)

Chai, C.; Larmat, C. S.; Maceira, M.; Ammon, C. J.; He, R.; Zhang, H.

2017-12-01

The acquisition of high-quality data from permanent and temporary dense seismic networks provides the opportunity to apply statistical and machine learning techniques to a broad range of geophysical observations. Lekic and Romanowicz (2011) used clustering analysis on tomographic velocity models of the western United States to perform tectonic regionalization and the velocity-profile clusters agree well with known geomorphic provinces. A complementary and somewhat less restrictive approach is to apply cluster analysis directly to geophysical observations. In this presentation, we apply clustering analysis to teleseismic P-wave receiver functions (RFs) continuing efforts of Larmat et al. (2015) and Maceira et al. (2015). These earlier studies validated the approach with surface waves and stacked EARS RFs from the USArray stations. In this study, we experiment with both the K-means and hierarchical clustering algorithms. We also test different distance metrics defined in the vector space of RFs following Lekic and Romanowicz (2011). We cluster data from two distinct data sets. The first, corresponding to the western US, was by smoothing/interpolation of receiver-function wavefield (Chai et al. 2015). Spatial coherence and agreement with geologic region increase with this simpler, spatially smoothed set of observations. The second data set is composed of RFs for more than 800 stations of the China Digital Seismic Network (CSN). Preliminary results show a first order agreement between clusters and tectonic region and each region cluster includes a distinct Ps arrival, which probably reflects differences in crustal thickness. Regionalization remains an important step to characterize a model prior to application of full waveform and/or stochastic imaging techniques because of the computational expense of these types of studies. Machine learning techniques can provide valuable information that can be used to design and characterize formal geophysical inversion, providing information on spatial variability in the subsurface geology.

Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.

PubMed

Rusk, Andria; Highfield, Linda; Wilkerson, J Michael; Harrell, Melissa; Obala, Andrew; Amick, Benjamin

2016-02-19

Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions. Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less. A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area. Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.

Cluster in the Auroral Acceleration Region

NASA Technical Reports Server (NTRS)

Pickett, Jolene S.; Fazakerley, Andrew N.; Marklund, Gorun; Dandouras, Iannis; Christopher, Ivar W.; Kistler, Lynn; Lucek, Elizabeth; Masson, Arnaud; Taylor, Matthew G.; Mutel, Robert L.;

Relations Between Residential Proximity to EPA-Designated Toxic Release Sites and Diffuse Large B-Cell Lymphoma Incidence.

PubMed

Bulka, Catherine; Nastoupil, Loretta J; Koff, Jean L; Bernal-Mizrachi, Leon; Ward, Kevin C; Williams, Jessica N; Bayakly, A Rana; Switchenko, Jeffrey M; Waller, Lance A; Flowers, Christopher R

2016-10-01

Examining the spatial patterns of diffuse large B-cell lymphoma (DLBCL) incidence and residential proximity to toxic release locations may provide insight regarding environmental and sociodemographic risk factors. We linked and geocoded cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry with population data from the US Census and the Environmental Protection Agency's Toxics Release Inventory. We conducted cluster analyses and constructed Poisson regression models to assess DLBCL incidence as a function of mean distance to the toxic release sites. In total, 3851 incident DLBCL cases occurred among adults residing in Georgia between 1999 and 2008. Significant focal clustering was observed around 57% of ethylene oxide sites, 5% of benzene sites, 9% of tetrachloroethylene sites, 7% of styrene sites, 10% of formaldehyde sites, 5% of trichloroethylene sites, and 10% of all release sites. Mean distance to sites was significantly associated with DLBCL risk for all chemicals. Proximity to Toxics Release Inventory sites can be linked to increased DLBCL risk as assessed through focal clustering and Poisson regression, and confirmatory studies using geospatial mapping can aid in further specifying risk factors for DLBCL.

Zika virus infection, associated microcephaly, and low yellow fever vaccination coverage in Brazil: is there any causal link?

PubMed

De Góes Cavalcanti, Luciano Pamplona; Tauil, Pedro Luiz; Alencar, Carlos Henrique; Oliveira, Wanderson; Teixeira, Mauro Martins; Heukelbach, Jorg

2016-06-30

Since the end of 2014, Zika virus (ZIKV) infection has been rapidly spreading in Brazil. To analyze the possible association of yellow fever vaccine with a protective effect against ZIKV-related microcephaly, the following spatial analyses were performed, using Brazilian municipalities as units: i) yellow fever vaccination coverage in Brazilian municipalities in individuals aged 15-49; ii) reported cases of microcephaly by municipality; and iii) confirmed cases of microcephaly related to ZIKV, by municipality. SaTScan software was used to identify clusters of municipalities for high risk of microcephaly. There were seven significant high risk clusters of confirmed microcephaly cases, with four of them located in the Northeast where yellow fever vaccination rates were the lowest. The clusters harbored only 2.9% of the total population of Brazil, but 15.2% of confirmed cases of microcephaly. We hypothesize that pregnant women in regions with high yellow fever vaccination coverage may pose their offspring to lower risk for development of microcephaly. There is an urgent need for systematic studies to confirm the possible link between low yellow fever vaccination coverage, Zika virus infection and microcephaly.

Evaluating outcome-correlated recruitment and geographic recruitment bias in a respondent-driven sample of people who inject drugs in Tijuana, Mexico

PubMed Central

Rudolph, Abby E.; Gaines, Tommi L.; Lozada, Remedios; Vera, Alicia; Brouwer, Kimberly C.

2015-01-01

Respondent-driven sampling’s (RDS) widespread use and reliance on untested assumptions suggests a need for new exploratory/diagnostic tests. We assessed geographic recruitment bias and outcome-correlated recruitment among 1048 RDS-recruited people who inject drugs (Tijuana, Mexico). Surveys gathered demographics, drug/sex behaviors, activity locations, and recruiter-recruit pairs. Simulations assessed geographic and network clustering of active syphilis (RPR titers≥1:8). Gender-specific predicted probabilities were estimated using logistic regression with GEE and robust standard errors. Active syphilis prevalence was 7% (crude: men=5.7% and women=16.6%; RDS-adjusted: men=6.7% and women=7.6%). Syphilis clustered in the Zona Norte, a neighborhood known for drug and sex markets. Network simulations revealed geographic recruitment bias and non-random recruitment by syphilis status. Gender-specific prevalence estimates accounting for clustering were highest among those living/working/injecting/buying drugs in the Zona Norte and directly/indirectly connected to syphilis cases (men:15.9%, women:25.6%) and lowest among those with neither exposure (men:3.0%, women:6.1%). Future RDS analyses should assess/account for network and spatial dependencies. PMID:24969586

Evaluating outcome-correlated recruitment and geographic recruitment bias in a respondent-driven sample of people who inject drugs in Tijuana, Mexico.

PubMed

Rudolph, Abby E; Gaines, Tommi L; Lozada, Remedios; Vera, Alicia; Brouwer, Kimberly C

2014-12-01

Respondent-driven sampling's (RDS) widespread use and reliance on untested assumptions suggests a need for new exploratory/diagnostic tests. We assessed geographic recruitment bias and outcome-correlated recruitment among 1,048 RDS-recruited people who inject drugs (Tijuana, Mexico). Surveys gathered demographics, drug/sex behaviors, activity locations, and recruiter-recruit pairs. Simulations assessed geographic and network clustering of active syphilis (RPR titers ≥1:8). Gender-specific predicted probabilities were estimated using logistic regression with GEE and robust standard errors. Active syphilis prevalence was 7 % (crude: men = 5.7 % and women = 16.6 %; RDS-adjusted: men = 6.7 % and women = 7.6 %). Syphilis clustered in the Zona Norte, a neighborhood known for drug and sex markets. Network simulations revealed geographic recruitment bias and non-random recruitment by syphilis status. Gender-specific prevalence estimates accounting for clustering were highest among those living/working/injecting/buying drugs in the Zona Norte and directly/indirectly connected to syphilis cases (men: 15.9 %, women: 25.6 %) and lowest among those with neither exposure (men: 3.0 %, women: 6.1 %). Future RDS analyses should assess/account for network and spatial dependencies.

The Elbmarsch leukemia cluster: are there conceptual limitations in controlling immission from nuclear establishments in Germany?

PubMed

Schmitz-Feuerhake, I; Dieckmann, H; Hoffmann, W; Lengfelder, E; Pflugbeil, S; Stevenson, A F

2005-11-01

The childhood leukemia cluster in the proximity of the German nuclear establishments of Geesthacht is unique in its spatial and temporal concentration. After a steep increase in cases in 1990, the cluster continues to show a significant increase up to the present. Early investigations of blood samples from a casual sample of local residents showed an increase in dicentric chromosomes in lymphocytes, indicating exposure exceeding dose limits. Analyses of the immission data revealed several unexpected deliveries of fission and activation products in the environment but provided no explanation of the source. Because of the observed overdispersion of dicentric chromosomes in cells, the idea of a contribution by densely ionizing emitters was compelling. The routine programs, however, do not include alpha emitters. These were measured in specific studies that proved contamination by transuranic nuclides. As shown in the present investigation, routine environmental surveillance programs support the occurrence of an accidental event near Geesthacht in September 1986. Until now, neither the cause nor the complete scenario of the activity release could be established. The ongoing discussion highlights limitations in the immission-control concept, which is predominantly based on gamma-radiation monitoring.

Skyscrapers in the Desert: Observing Ongoing, Active Star Formation in the Low-Density Wing of the Small Magellanic Cloud

NASA Astrophysics Data System (ADS)

Fulmer, Leah M.; Gallagher, John S.; Hamann, Wolf-Rainer; Oskinova, Lida; Ramachandran, Varsha

2018-01-01

The low-density Wing of the Small Magellanic Cloud exhibits ongoing, active star formation despite a distinctive lack of dense ambient gas and dust, or resources from which to form stars. Our continued work in studying this region reveals that these paradoxical observations may be explained by a process of sequential star formation. We present photometric, clustering, and spatial analyses in support of this scenario, along with a proposed star formation history based on the following evidence: matches to isochrone models, stellar and ionized gas kinematics (VLT, SALT), and regional HI gas kinematics (ATCA, PKS).

Earthquake Predictability: Results From Aggregating Seismicity Data And Assessment Of Theoretical Individual Cases Via Synthetic Data

NASA Astrophysics Data System (ADS)

Adamaki, A.; Roberts, R.

2016-12-01

For many years an important aim in seismological studies has been forecasting the occurrence of large earthquakes. Despite some well-established statistical behavior of earthquake sequences, expressed by e.g. the Omori law for aftershock sequences and the Gutenburg-Richter distribution of event magnitudes, purely statistical approaches to short-term earthquake prediction have in general not been successful. It seems that better understanding of the processes leading to critical stress build-up prior to larger events is necessary to identify useful precursory activity, if this exists, and statistical analyses are an important tool in this context. There has been considerable debate on the usefulness or otherwise of foreshock studies for short-term earthquake prediction. We investigate generic patterns of foreshock activity using aggregated data and by studying not only strong but also moderate magnitude events. Aggregating empirical local seismicity time series prior to larger events observed in and around Greece reveals a statistically significant increasing rate of seismicity over 20 days prior to M>3.5 earthquakes. This increase cannot be explained by tempo-spatial clustering models such as ETAS, implying genuine changes in the mechanical situation just prior to larger events and thus the possible existence of useful precursory information. Because of tempo-spatial clustering, including aftershocks to foreshocks, even if such generic behavior exists it does not necessarily follow that foreshocks have the potential to provide useful precursory information for individual larger events. Using synthetic catalogs produced based on different clustering models and different presumed system sensitivities we are now investigating to what extent the apparently established generic foreshock rate acceleration may or may not imply that the foreshocks have potential in the context of routine forecasting of larger events. Preliminary results suggest that this is the case, but that it is likely that physically-based models of foreshock clustering will be a necessary, but not necessarily sufficient, basis for successful forecasting.

Comparison of Poisson and Bernoulli spatial cluster analyses of pediatric injuries in a fire district

PubMed Central

Warden, Craig R

2008-01-01

Background With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms. Results There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001–2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical) differed in age distribution (7.7 ± 4.4 years versus 5.4 ± 4.8 years, p < 0.001) and location type of incident (school or church 12% versus 15%, multifamily residence 22% versus 13%, single family residence 51% versus 28%, sport, park or recreational facility 3% versus 8%, public building 8% versus 7%, and street or road 3% versus 30%, respectively, p < 0.001). Using the medical incident locations as controls, there was no significant clustering for environmental or assault injuries using the Bernoulli method while there were four significant clusters for all injury mechanisms combined, 13 clusters for motor vehicle collisions, one for falls, and two for pedestrian or bicycle injuries. Using the Poisson cluster method on incidence rates by census tract identified four clusters for all injuries, three for motor vehicle collisions, four for fall injuries, and one each for environmental and assault injuries. The two detection methods shared a minority of overlapping geographical clusters. Conclusion Significant clustering occurs overall for all injury mechanisms combined and for each mechanism depending on the cluster detection method used. There was some overlap in geographic clusters identified by both methods. The Bernoulli method allows more focused cluster mapping and evaluation since it directly uses location data. Once clusters are found, interventions can be targeted to specific geographic locations, location types, ages of victims, and mechanisms of injury. PMID:18808720

Comparison of Poisson and Bernoulli spatial cluster analyses of pediatric injuries in a fire district.

PubMed

Warden, Craig R

2008-09-22

With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms. There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001-2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical) differed in age distribution (7.7 +/- 4.4 years versus 5.4 +/- 4.8 years, p < 0.001) and location type of incident (school or church 12% versus 15%, multifamily residence 22% versus 13%, single family residence 51% versus 28%, sport, park or recreational facility 3% versus 8%, public building 8% versus 7%, and street or road 3% versus 30%, respectively, p < 0.001). Using the medical incident locations as controls, there was no significant clustering for environmental or assault injuries using the Bernoulli method while there were four significant clusters for all injury mechanisms combined, 13 clusters for motor vehicle collisions, one for falls, and two for pedestrian or bicycle injuries. Using the Poisson cluster method on incidence rates by census tract identified four clusters for all injuries, three for motor vehicle collisions, four for fall injuries, and one each for environmental and assault injuries. The two detection methods shared a minority of overlapping geographical clusters. Significant clustering occurs overall for all injury mechanisms combined and for each mechanism depending on the cluster detection method used. There was some overlap in geographic clusters identified by both methods. The Bernoulli method allows more focused cluster mapping and evaluation since it directly uses location data. Once clusters are found, interventions can be targeted to specific geographic locations, location types, ages of victims, and mechanisms of injury.

Spatial and temporal clustering of dengue virus transmission in Thai villages.

PubMed

Mammen, Mammen P; Pimgate, Chusak; Koenraadt, Constantianus J M; Rothman, Alan L; Aldstadt, Jared; Nisalak, Ananda; Jarman, Richard G; Jones, James W; Srikiatkhachorn, Anon; Ypil-Butac, Charity Ann; Getis, Arthur; Thammapalo, Suwich; Morrison, Amy C; Libraty, Daniel H; Green, Sharone; Scott, Thomas W

2008-11-04

Transmission of dengue viruses (DENV), the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted. Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters) and without (negative clusters) acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1) define the spatial and temporal dimensions of DENV transmission, (2) correlate these factors with variation in DENV transmission, and (3) determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees) occurred in positive clusters (p < 0.01; attributable risk [AR] = 10.4 per 100; 95% confidence interval 1-19.8 per 100]. In positive clusters, 12.4% of enrollees became infected in a 15-d period and DENV infections were aggregated centrally near homes of index cases. As only 1 of 217 pairs of serologic specimens tested in positive clusters revealed a recent DENV infection that occurred prior to cluster initiation, we attribute the observed DENV transmission subsequent to cluster investigation to recent DENV transmission activity. Of the 1,022 female adult Ae. aegypti collected, all eight (0.8%) dengue-infected mosquitoes came from houses in positive clusters; none from control clusters or schools. Distinguishing features between positive and negative clusters were greater availability of piped water in negative clusters (p < 0.01) and greater number of Ae. aegypti pupae per person in positive clusters (p = 0.04). During primarily DENV-4 transmission seasons, the ratio of inapparent to symptomatic infections was nearly 1:1 among child enrollees. Study limitations included inability to sample all children and mosquitoes within each cluster and our reliance on serologic rather than virologic evidence of interval infections in enrollees given restrictions on the frequency of blood collections in children. Our data reveal the remarkably focal nature of DENV transmission within a hyperendemic rural area of Thailand. These data suggest that active school-based dengue case detection prompting local spraying could contain recent virus introductions and reduce the longitudinal risk of virus spread within rural areas. Our results should prompt future cluster studies to explore how host immune and behavioral aspects may impact DENV transmission and prevention strategies. Cluster methodology could serve as a useful research tool for investigation of other temporally and spatially clustered infectious diseases.

Spatial and Temporal Clustering of Dengue Virus Transmission in Thai Villages

PubMed Central

Mammen, Mammen P; Pimgate, Chusak; Koenraadt, Constantianus J. M; Rothman, Alan L; Aldstadt, Jared; Nisalak, Ananda; Jarman, Richard G; Jones, James W; Srikiatkhachorn, Anon; Ypil-Butac, Charity Ann; Getis, Arthur; Thammapalo, Suwich; Morrison, Amy C; Libraty, Daniel H; Green, Sharone; Scott, Thomas W

2008-01-01

Background Transmission of dengue viruses (DENV), the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted. Methods and Findings Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters) and without (negative clusters) acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1) define the spatial and temporal dimensions of DENV transmission, (2) correlate these factors with variation in DENV transmission, and (3) determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees) occurred in positive clusters (p < 0.01; attributable risk [AR] = 10.4 per 100; 95% confidence interval 1–19.8 per 100]. In positive clusters, 12.4% of enrollees became infected in a 15-d period and DENV infections were aggregated centrally near homes of index cases. As only 1 of 217 pairs of serologic specimens tested in positive clusters revealed a recent DENV infection that occurred prior to cluster initiation, we attribute the observed DENV transmission subsequent to cluster investigation to recent DENV transmission activity. Of the 1,022 female adult Ae. aegypti collected, all eight (0.8%) dengue-infected mosquitoes came from houses in positive clusters; none from control clusters or schools. Distinguishing features between positive and negative clusters were greater availability of piped water in negative clusters (p < 0.01) and greater number of Ae. aegypti pupae per person in positive clusters (p = 0.04). During primarily DENV-4 transmission seasons, the ratio of inapparent to symptomatic infections was nearly 1:1 among child enrollees. Study limitations included inability to sample all children and mosquitoes within each cluster and our reliance on serologic rather than virologic evidence of interval infections in enrollees given restrictions on the frequency of blood collections in children. Conclusions Our data reveal the remarkably focal nature of DENV transmission within a hyperendemic rural area of Thailand. These data suggest that active school-based dengue case detection prompting local spraying could contain recent virus introductions and reduce the longitudinal risk of virus spread within rural areas. Our results should prompt future cluster studies to explore how host immune and behavioral aspects may impact DENV transmission and prevention strategies. Cluster methodology could serve as a useful research tool for investigation of other temporally and spatially clustered infectious diseases. PMID:18986209

Demographic characterization and spatial cluster analysis of human Salmonella 1,4,[5],12:i:- infections in Portugal: A 10year study.

PubMed

Seixas, R; Nunes, T; Machado, J; Tavares, L; Owen, S P; Bernardo, F; Oliveira, M

Salmonella 1,4,[5],12:i:- is presently considered one of the major serovars responsible for human salmonellosis worldwide. Due to its recent emergence, studies assessing the demographic characterization and spatial epidemiology of salmonellosis 1,4,[5],12:i:- at local- or country-level are lacking. In this study, a analysis was conducted over a 10year period, from 2000 to the first quarter of 2011 at the Portuguese National Laboratory in Portugal mainland, with a total of 215 Salmonella 1,4,[5],12:i:- serotyped isolates obtained from human infections by a passive surveillance system. Data regarding source, year and month of sampling, gender, age, district and municipality of the patients were registered. Descriptive statistical analysis and a spatial scan statistic combined with a geographic information system were employed to characterize the epidemiology and identify spatial clusters. Results showed that most districts have reports of Salmonella 1,4,[5],12:i:-, with a higher number of cases at the Portuguese coastland, including districts like Porto (n=60, 27.9%), Lisboa (n=29, 13.5%) and Aveiro (n=28, 13.0%). An increased incidence was observed in the period from 2004 to 2011 and most infections occurred during May and October. Spatial analysis revealed 4 clusters of higher than expected infection rates. Three were located in the north of Portugal, including two at the coastland (Cluster 1 [RR=3.58, p≤0.001] and 4 [RR=10.42 p≤0.230]), and one at the countryside (Cluster 3 [RR=17.76, p≤0.001]). A larger cluster was detected involving the center and south of Portugal (Cluster 2 [RR=4.85, p≤0.001]). The present study was elaborated with data provided by a passive surveillance system, which may originate an underestimation of disease burden. However, this is the first report describing the incidence and the distribution of areas with higher risk of infection in Portugal, revealing that Salmonella 1,4,[5],12:i:- displayed a significant geographic clustering and these areas should be further evaluated to identify risk factors in order to establish prevention programs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A spatial approach for the epidemiology of antibiotic use and resistance in community-based studies: the emergence of urban clusters of Escherichia coli quinolone resistance in Sao Paulo, Brasil

PubMed Central

2011-01-01

Background Population antimicrobial use may influence resistance emergence. Resistance is an ecological phenomenon due to potential transmissibility. We investigated spatial and temporal patterns of ciprofloxacin (CIP) population consumption related to E. coli resistance emergence and dissemination in a major Brazilian city. A total of 4,372 urinary tract infection E. coli cases, with 723 CIP resistant, were identified in 2002 from two outpatient centres. Cases were address geocoded in a digital map. Raw CIP consumption data was transformed into usage density in DDDs by CIP selling points influence zones determination. A stochastic model coupled with a Geographical Information System was applied for relating resistance and usage density and for detecting city areas of high/low resistance risk. Results E. coli CIP resistant cluster emergence was detected and significantly related to usage density at a level of 5 to 9 CIP DDDs. There were clustered hot-spots and a significant global spatial variation in the residual resistance risk after allowing for usage density. Conclusions There were clustered hot-spots and a significant global spatial variation in the residual resistance risk after allowing for usage density. The usage density of 5-9 CIP DDDs per 1,000 inhabitants within the same influence zone was the resistance triggering level. This level led to E. coli resistance clustering, proving that individual resistance emergence and dissemination was affected by antimicrobial population consumption. PMID:21356088

Detection of moving clusters by a method of cinematic pairs

NASA Astrophysics Data System (ADS)

Khodjachikh, M. F.; Romanovsky, E. A.

2000-01-01

The algorithm of revealing of pairs stars with common movement is offered and is realized. The basic source is the catalogue HIPPARCOS. On concentration of kinematic pairs it is revealed three unknown earlier moving clusters in constellations: 1) Phe, 2) Cae, 3) Hor and, well known, in 4) UMa are revealed. On an original technique the members of clusters -- all 87 stars are allocated. Coordinates of the clusters convergent point α, delta; (in degrees), spatial speed (in km/s) and age (in 106 yr) from isochrone fitting have made: 1) 51, -29, 19.0, 500, 5/6; 2) 104, -32, 23.7, 300, 9/12; 3) 119, -27, 22.3, 100, 9/22; 4) 303, -31, 16.7, 500, 16/8 accordingly. Numerator of fraction -- number of stars identified as the members of clusters, denominator -- number of the probable members (with unknown radial speeds). The preliminary qualitative analysis of clusters spatial structure is carried in view of their dynamic evolution.