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

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

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 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.

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.

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 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.

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

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.

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

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system

PubMed Central

Lall, Ramona; Levin-Rector, Alison; Sell, Jessica; Paladini, Marc; Konty, Kevin J.; Olson, Don; Weiss, Don

2017-01-01

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method’s implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System’s C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis. PMID:28886112

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system.

PubMed

Mathes, Robert W; Lall, Ramona; Levin-Rector, Alison; Sell, Jessica; Paladini, Marc; Konty, Kevin J; Olson, Don; Weiss, Don

2017-01-01

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System's C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis.

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.

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.

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

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.

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

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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.

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.

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.

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring.

PubMed

Takahashi, Kunihiko; Kulldorff, Martin; Tango, Toshiro; Yih, Katherine

2008-04-11

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. Based on the flexible purely spatial scan statistic, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic. The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

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

Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan.

PubMed

Abbas, Tariq; Younus, Muhammad; Muhammad, Sayyad Aun

2015-01-01

Crimean Congo hemorrhagic fever (CCHF) is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in Pakistan. The aim of this study was to identify spatial clusters of human cases of CCHF reported in country. Kulldorff's spatial scan statisitc, Anselin's Local Moran's I and Getis Ord Gi* tests were applied on data (i.e. number of laboratory confirmed cases reported from each district during year 2013). The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with Afghanistan. The cluster comprised the following districts: Qilla Abdullah; Qilla Saifullah; Loralai, Quetta, Sibi, Chagai, and Mastung. Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad. We provide empirical evidence of spatial clustering of human CCHF cases in the country. The districts in the clusters should be given priority in surveillance, control programs, and further research.

[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.

Accounting for Limited Detection Efficiency and Localization Precision in Cluster Analysis in Single Molecule Localization Microscopy

PubMed Central

Shivanandan, Arun; Unnikrishnan, Jayakrishnan; Radenovic, Aleksandra

2015-01-01

Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques’ inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10 – 30nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley’s L(r) – r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization. PMID:25794150

Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia.

PubMed

Tadesse, Sebsibe; Enqueselassie, Fikre; Hagos, Seifu

2018-01-01

Spatial targeting is advocated as an effective method that contributes for achieving tuberculosis control in high-burden countries. However, there is a paucity of studies clarifying the spatial nature of the disease in these countries. This study aims to identify the location, size and risk of purely spatial and space-time clusters for high occurrence of tuberculosis in Gurage Zone, Southern Ethiopia during 2007 to 2016. A total of 15,805 patient data that were retrieved from unit TB registers were included in the final analyses. The spatial and space-time cluster analyses were performed using the global Moran's I, Getis-Ord [Formula: see text] and Kulldorff's scan statistics. Eleven purely spatial and three space-time clusters were detected (P <0.001).The clusters were concentrated in border areas of the Gurage Zone. There were considerable spatial variations in the risk of tuberculosis by year during the study period. This study showed that tuberculosis clusters were mainly concentrated at border areas of the Gurage Zone during the study period, suggesting that there has been sustained transmission of the disease within these locations. The findings may help intensify the implementation of tuberculosis control activities in these locations. Further study is warranted to explore the roles of various ecological factors on the observed spatial distribution of tuberculosis.

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.

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.

Detection of Tuberculosis Infection Hotspots Using Activity Spaces Based Spatial Approach in an Urban Tokyo, from 2003 to 2011.

PubMed

Izumi, Kiyohiko; Ohkado, Akihiro; Uchimura, Kazuhiro; Murase, Yoshiro; Tatsumi, Yuriko; Kayebeta, Aya; Watanabe, Yu; Ishikawa, Nobukatsu

2015-01-01

Identifying ongoing tuberculosis infection sites is crucial for breaking chains of transmission in tuberculosis-prevalent urban areas. Previous studies have pointed out that detection of local accumulation of tuberculosis patients based on their residential addresses may be limited by a lack of matching between residences and tuberculosis infection sites. This study aimed to identify possible tuberculosis hotspots using TB genotype clustering statuses and a concept of "activity space", a place where patients spend most of their waking hours. We further compared the spatial distribution by different residential statuses and describe urban environmental features of the detected hotspots. Culture-positive tuberculosis patients notified to Shinjuku city from 2003 to 2011 were enrolled in this case-based cross-sectional study, and their demographic and clinical information, TB genotype clustering statuses, and activity space were collected. Spatial statistics (Global Moran's I and Getis-Ord Gi* statistics) identified significant hotspots in 152 census tracts, and urban environmental features and tuberculosis patients' characteristics in these hotspots were assessed. Of the enrolled 643 culture-positive tuberculosis patients, 416 (64.2%) were general inhabitants, 42 (6.5%) were foreign-born people, and 184 were homeless people (28.6%). The percentage of overall genotype clustering was 43.7%. Genotype-clustered general inhabitants and homeless people formed significant hotspots around a major railway station, whereas the non-clustered general inhabitants formed no hotspots. This suggested the detected hotspots of activity spaces may reflect ongoing tuberculosis transmission sites and were characterized by smaller residential floor size and a higher proportion of non-working households. Activity space-based spatial analysis suggested possible TB transmission sites around the major railway station and it can assist in further comprehension of TB transmission dynamics in an urban setting in Japan.

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

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.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

PubMed Central

Hampton, Kristen H.; Fitch, Molly K.; Allshouse, William B.; Doherty, Irene A.; Gesink, Dionne C.; Leone, Peter A.; Serre, Marc L.; Miller, William C.

2010-01-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest. PMID:20817785

Mapping health data: improved privacy protection with donut method geomasking.

PubMed

Hampton, Kristen H; Fitch, Molly K; Allshouse, William B; Doherty, Irene A; Gesink, Dionne C; Leone, Peter A; Serre, Marc L; Miller, William C

2010-11-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Spatial analysis of malaria in Anhui province, China

PubMed Central

Zhang, Wenyi; Wang, Liping; Fang, Liqun; Ma, Jiaqi; Xu, Youfu; Jiang, Jiafu; Hui, Fengming; Wang, Jianjun; Liang, Song; Yang, Hong; Cao, Wuchun

2008-01-01

Background Malaria has re-emerged in Anhui Province, China, and this province was the most seriously affected by malaria during 2005–2006. It is necessary to understand the spatial distribution of malaria cases and to identify highly endemic areas for future public health planning and resource allocation in Anhui Province. Methods The annual average incidence at the county level was calculated using malaria cases reported between 2000 and 2006 in Anhui Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of malaria incidence at the county level. Results The spatial distribution of malaria cases in Anhui Province from 2000 to 2006 was mapped at the county level to show crude incidence, excess hazard and spatial smoothed incidence. Spatial cluster analysis suggested 10 and 24 counties were at increased risk for malaria (P < 0.001) with the maximum spatial cluster sizes at < 50% and < 25% of the total population, respectively. Conclusion The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit malaria risks and to further identify environmental factors responsible for the re-emerged malaria risks. Future public health planning and resource allocation in Anhui Province should be focused on the maximum spatial cluster region. PMID:18847489

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

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).

Spatial, temporal and spatio-temporal clusters of measles incidence at the county level in Guangxi, China during 2004-2014: flexibly shaped scan statistics.

PubMed

Tang, Xianyan; Geater, Alan; McNeil, Edward; Deng, Qiuyun; Dong, Aihu; Zhong, Ge

2017-04-04

Outbreaks of measles re-emerged in Guangxi province during 2013-2014, where measles again became a major public health concern. A better understanding of the patterns of measles cases would help in identifying high-risk areas and periods for optimizing preventive strategies, yet these patterns remain largely unknown. Thus, this study aimed to determine the patterns of measles clusters in space, time and space-time at the county level over the period 2004-2014 in Guangxi. Annual data on measles cases and population sizes for each county were obtained from Guangxi CDC and Guangxi Bureau of Statistics, respectively. Epidemic curves and Kulldorff's temporal scan statistics were used to identify seasonal peaks and high-risk periods. Tango's flexible scan statistics were implemented to determine irregular spatial clusters. Spatio-temporal clusters in elliptical cylinder shapes were detected by Kulldorff's scan statistics. Population attributable risk percent (PAR%) of children aged ≤24 months was used to identify regions with a heavy burden of measles. Seasonal peaks occurred between April and June, and a temporal measles cluster was detected in 2014. Spatial clusters were identified in West, Southwest and North Central Guangxi. Three phases of spatio-temporal clusters with high relative risk were detected: Central Guangxi during 2004-2005, Midwest Guangxi in 2007, and West and Southwest Guangxi during 2013-2014. Regions with high PAR% were mainly clustered in West, Southwest, North and Central Guangxi. A temporal uptrend of measles incidence existed in Guangxi between 2010 and 2014, while downtrend during 2004-2009. The hotspots shifted from Central to West and Southwest Guangxi, regions overburdened with measles. Thus, intensifying surveillance of timeliness and completeness of routine vaccination and implementing supplementary immunization activities for measles should prioritized in these regions.

Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering

PubMed Central

Li, Jie; Fang, Xiangming

2010-01-01

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods. However, there are reasons to believe that the errors may often be positively spatially correlated and that this may mitigate their deleterious effects on spatial analyses. In this article, we demonstrate explicitly that the positional errors associated with automated geocoding of a dataset of more than 6000 addresses in Carroll County, Iowa are spatially autocorrelated. Furthermore, through two simulation studies of disease processes, including one in which the disease process is overlain upon the Carroll County addresses, we show that spatial autocorrelation among geocoding errors maintains the power of two tests for disease clustering at a level higher than that which would occur if the errors were independent. Implications of these results for cluster detection, privacy protection, and measurement-error modeling of geographic health data are discussed. PMID:20087879

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.

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

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

Spatial Patterns of Plasmodium falciparum Clinical Incidence, Asymptomatic Parasite Carriage and Anopheles Density in Two Villages in Mali

PubMed Central

Sissoko, Mahamadou S.; van den Hoogen, Lotus L.; Samake, Yacouba; Tapily, Amadou; Diarra, Adama Z.; Coulibaly, Maimouna; Bouare, Madama; Gaudart, Jean; Knight, Philip; Sauerwein, Robert W.; Takken, Willem; Bousema, Teun; Doumbo, Ogobara K.

2015-01-01

Heterogeneity in malaria exposure is most readily recognized in areas with low-transmission patterns. By comparison, little research has been done on spatial patterns in malaria exposure in high-endemic settings. We determined the spatial clustering of clinical malaria incidence, asymptomatic parasite carriage, and Anopheles density in two villages in Mali exposed to low- and mesoendemic-malaria transmission. In the two study areas that were < 1 km2 in size, we observed evidence for spatial clustering of Anopheles densities or malaria parasite carriage during the dry season. Anopheles density and malaria prevalence appeared associated in some of our detected hotspots. However, many households with high parasite prevalence or high Anopheles densities were located outside the identified hotspots. Our findings indicate that within small villages exposed to low- or mesoendemic-malaria transmission, spatial patterns in mosquito densities and parasite carriage are best detected in the dry season. Considering the high prevalence of parasite carriage outside detected hotspots, the suitability of the area for targeting control efforts to households or areas of more intense malaria transmission may be limited. PMID:26324728

The Swift BAT Perspective on Non-Thermal Emission in HIFLUGCS Galaxy Clusters

NASA Technical Reports Server (NTRS)

Wik, Daniel R.

2011-01-01

The search for diffuse non-thermal, inverse Compton (IC) emission from galaxy clusters at hard X-ray energies has been underway for many years, with most detections being either of low significance or controversial. Until recently, comprehensive surveys of hard X-ray emission from clusters were not possible; instead, individually proposed-for. long observations would be collated from the archive. With the advent of the Swift BAT all sky survey, any c1u,;ter's emission above 14 keV can be probed with nearly uniform sensitivity. which is comparable to that of RXTE, Beppo-SAX, and Suzaku with the 58-month version of the survey. In this work. we search for non-thermal excess emission above the exponentially decreasing, high energy thermal emission in the flux-limited HIFLUGCS sample. The BAT emission from many of the detected clusters is marginally extended; we are able to extract the total flux for these clusters using fiducial models for their spatial extent. To account for thermal emission at BAT energies, XMM-Newton EPIC spectra are extracted from coincident spatial regions so that both the thermal and non-thermal spectral components can be determined simultaneou,;ly in joint fits. We find marginally significant IC components in 6 clusters, though after closer inspection and consideration of systematic errors we are unable to claim a clear detection in any of them. The spectra of all clusters are also summed to enhance a cumulative non-thermal signal not quite detectable in individual clusters. After constructing a model based on single temperature

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

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

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.

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

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.

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.

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

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012.

PubMed

Polonsky, Jonathan A; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J

2014-01-01

Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.

Descriptive Epidemiology of Typhoid Fever during an Epidemic in Harare, Zimbabwe, 2012

PubMed Central

Polonsky, Jonathan A.; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J.

2014-01-01

Background Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. Methods A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. Principal Findings We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. Conclusions This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range. PMID:25486292

GPU Accelerated Clustering for Arbitrary Shapes in Geoscience Data

NASA Astrophysics Data System (ADS)

Pankratius, V.; Gowanlock, M.; Rude, C. M.; Li, J. D.

2016-12-01

Clustering algorithms have become a vital component in intelligent systems for geoscience that helps scientists discover and track phenomena of various kinds. Here, we outline advances in Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which detects clusters of arbitrary shape that are common in geospatial data. In particular, we propose a hybrid CPU-GPU implementation of DBSCAN and highlight new optimization approaches on the GPU that allows clustering detection in parallel while optimizing data transport during CPU-GPU interactions. We employ an efficient batching scheme between the host and GPU such that limited GPU memory is not prohibitive when processing large and/or dense datasets. To minimize data transfer overhead, we estimate the total workload size and employ an execution that generates optimized batches that will not overflow the GPU buffer. This work is demonstrated on space weather Total Electron Content (TEC) datasets containing over 5 million measurements from instruments worldwide, and allows scientists to spot spatially coherent phenomena with ease. Our approach is up to 30 times faster than a sequential implementation and therefore accelerates discoveries in large datasets. We acknowledge support from NSF ACI-1442997.

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

PubMed Central

Jackson, Monica C; Huang, Lan; Luo, Jun; Hachey, Mark; Feuer, Eric

2009-01-01

Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan. PMID:19822013

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers.

PubMed

Jackson, Monica C; Huang, Lan; Luo, Jun; Hachey, Mark; Feuer, Eric

2009-10-12

The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated. We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*(pop); and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. For simulated data with outlier patterns, Tango's MEET, Moran's I and I*(pop) had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*(pop) (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*(pop) perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan.

Characterization of high explosive particles using cluster secondary ion mass spectrometry.

PubMed

Gillen, Greg; Mahoney, Christine; Wight, Scott; Lareau, Richard

2006-01-01

The use of secondary ion mass spectrometry (SIMS) for the detection and spatially resolved analysis of individual high explosive particles is described. A C(8) (-) carbon cluster primary ion beam was used in a commercial SIMS instrument to analyze samples of high explosives dispersed as particles on silicon substrates. In comparison with monatomic primary ion bombardment, the carbon cluster primary ion beam was found to greatly enhance characteristic secondary ion signals from the explosive compounds while causing minimal beam-induced degradation. The resistance of these compounds to degradation under ion bombardment allows explosive particles to be analyzed under high primary ion dose bombardment (dynamic SIMS) conditions, facilitating the rapid acquisition of spatially resolved molecular information. The use of cluster SIMS combined with computer control of the sample stage position allows for the automated identification and counting of explosive particle distributions on silicon surfaces. This will be useful for characterizing the efficiency of transfer of particulates in trace explosive detection portal collectors and/or swipes utilized for ion mobility spectrometry applications.

Advances in Significance Testing for Cluster Detection

NASA Astrophysics Data System (ADS)

Coleman, Deidra Andrea

Over the past two decades, much attention has been given to data driven project goals such as the Human Genome Project and the development of syndromic surveillance systems. A major component of these types of projects is analyzing the abundance of data. Detecting clusters within the data can be beneficial as it can lead to the identification of specified sequences of DNA nucleotides that are related to important biological functions or the locations of epidemics such as disease outbreaks or bioterrorism attacks. Cluster detection techniques require efficient and accurate hypothesis testing procedures. In this dissertation, we improve upon the hypothesis testing procedures for cluster detection by enhancing distributional theory and providing an alternative method for spatial cluster detection using syndromic surveillance data. In Chapter 2, we provide an efficient method to compute the exact distribution of the number and coverage of h-clumps of a collection of words. This method involves defining a Markov chain using a minimal deterministic automaton to reduce the number of states needed for computation. We allow words of the collection to contain other words of the collection making the method more general. We use our method to compute the distributions of the number and coverage of h-clumps in the Chi motif of H. influenza.. In Chapter 3, we provide an efficient algorithm to compute the exact distribution of multiple window discrete scan statistics for higher-order, multi-state Markovian sequences. This algorithm involves defining a Markov chain to efficiently keep track of probabilities needed to compute p-values of the statistic. We use our algorithm to identify cases where the available approximation does not perform well. We also use our algorithm to detect unusual clusters of made free throw shots by National Basketball Association players during the 2009-2010 regular season. In Chapter 4, we give a procedure to detect outbreaks using syndromic surveillance data while controlling the Bayesian False Discovery Rate (BFDR). The procedure entails choosing an appropriate Bayesian model that captures the spatial dependency inherent in epidemiological data and considers all days of interest, selecting a test statistic based on a chosen measure that provides the magnitude of the maximumal spatial cluster for each day, and identifying a cutoff value that controls the BFDR for rejecting the collective null hypothesis of no outbreak over a collection of days for a specified region.We use our procedure to analyze botulism-like syndrome data collected by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

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.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

PubMed

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

PubMed Central

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

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.

Prevalence and clustering of soil-transmitted helminth infections in a tribal area in southern India.

PubMed

Kaliappan, Saravanakumar Puthupalayam; George, Santosh; Francis, Mark Rohit; Kattula, Deepthi; Sarkar, Rajiv; Minz, Shantidani; Mohan, Venkata Raghava; George, Kuryan; Roy, Sheela; Ajjampur, Sitara Swarna Rao; Muliyil, Jayaprakash; Kang, Gagandeep

2013-12-01

To estimate the prevalence, spatial patterns and clustering in the distribution of soil-transmitted helminth (STH) infections, and factors associated with hookworm infections in a tribal population in Tamil Nadu, India. Cross-sectional study with one-stage cluster sampling of 22 clusters. Demographic and risk factor data and stool samples for microscopic ova/cysts examination were collected from 1237 participants. Geographical information systems mapping assessed spatial patterns of infection. The overall prevalence of STH was 39% (95% CI 36%–42%), with hookworm 38% (95% CI 35–41%) and Ascaris lumbricoides 1.5% (95% CI 0.8–2.2%). No Trichuris trichiura infection was detected. People involved in farming had higher odds of hookworm infection (1.68, 95% CI 1.31–2.17, P < 0.001). In the multiple logistic regression, adults (2.31, 95% CI 1.80–2.96, P < 0.001), people with pet cats (1.55, 95% CI 1.10–2.18, P = 0.011) and people who did not wash their hands with soap after defecation (1.84, 95% CI 1.27–2.67, P = 0.001) had higher odds of hookworm infection, but gender and poor usage of foot wear did not significantly increase risk. Cluster analysis, based on design effect calculation, did not show any clustering of cases among the study population; however, spatial scan statistic detected a significant cluster for hookworm infections in one village. Multiple approaches including health education, improving the existing sanitary practices and regular preventive chemotherapy are needed to control the burden of STH in similar endemic areas.

WordCluster: detecting clusters of DNA words and genomic elements

PubMed Central

2011-01-01

Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. PMID:21261981

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 and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011.

PubMed

Shields, Timothy; Pinchoff, Jessie; Lubinda, Jailos; Hamapumbu, Harry; Searle, Kelly; Kobayashi, Tamaki; Thuma, Philip E; Moss, William J; Curriero, Frank C

2016-05-31

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

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.

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

Fine-scale population genetic structure of arctic foxes (Vulpes lagopus) in the High Arctic.

PubMed

Lai, Sandra; Quiles, Adrien; Lambourdière, Josie; Berteaux, Dominique; Lalis, Aude

2017-12-01

The arctic fox (Vulpes lagopus) is a circumpolar species inhabiting all accessible Arctic tundra habitats. The species forms a panmictic population over areas connected by sea ice, but recently, kin clustering and population differentiation were detected even in regions where sea ice was present. The purpose of this study was to examine the genetic structure of a population in the High Arctic using a robust panel of highly polymorphic microsatellites. We analyzed the genotypes of 210 individuals from Bylot Island, Nunavut, Canada, using 15 microsatellite loci. No pattern of isolation-by-distance was detected, but a spatial principal component analysis (sPCA) revealed the presence of genetic subdivisions. Overall, the sPCA revealed two spatially distinct genetic clusters corresponding to the northern and southern parts of the study area, plus another subdivision within each of these two clusters. The north-south genetic differentiation partly matched the distribution of a snow goose colony, which could reflect a preference for settling into familiar ecological environments. Secondary clusters may result from higher-order social structures (neighbourhoods) that use landscape features to delimit their borders. The cryptic genetic subdivisions found in our population may highlight ecological processes deserving further investigations in arctic foxes at larger, regional spatial scales.

Application of a XMM-Newton EPIC Monte Carlo to Analysis And Interpretation of Data for Abell 1689, RXJ0658-55 And the Centaurus Clusters of Galaxies

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

Andersson, Karl E.; /Stockholm U. /SLAC; Peterson, J.R.

2007-04-17

We propose a new Monte Carlo method to study extended X-ray sources with the European Photon Imaging Camera (EPIC) aboard XMM Newton. The Smoothed Particle Inference (SPI) technique, described in a companion paper, is applied here to the EPIC data for the clusters of galaxies Abell 1689, Centaurus and RXJ 0658-55 (the ''bullet cluster''). We aim to show the advantages of this method of simultaneous spectral-spatial modeling over traditional X-ray spectral analysis. In Abell 1689 we confirm our earlier findings about structure in temperature distribution and produce a high resolution temperature map. We also confirm our findings about velocity structuremore » within the gas. In the bullet cluster, RXJ 0658-55, we produce the highest resolution temperature map ever to be published of this cluster allowing us to trace what looks like the motion of the bullet in the cluster. We even detect a south to north temperature gradient within the bullet itself. In the Centaurus cluster we detect, by dividing up the luminosity of the cluster in bands of gas temperatures, a striking feature to the north-east of the cluster core. We hypothesize that this feature is caused by a subcluster left over from a substantial merger that slightly displaced the core. We conclude that our method is very powerful in determining the spatial distributions of plasma temperatures and very useful for systematic studies in cluster structure.« less

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

GIS based spatial pattern analysis: Children with Hepatitis A in Turkey.

PubMed

Dogru, Ahmet Ozgur; David, Ruusa Magano; Ulugtekin, Necla; Goksel, Cigdem; Seker, Dursun Zafer; Sözen, Seval

2017-07-01

This study aimed to provide an insight into the geographic distribution of Hepatitis A incidence considering their temporal distribution, spatial patterns, hot spots and clusters identification in three different age-group (0-4, 5-9 and 10-14) in Turkey. Province based tabular data, including monthly numbers of Hepatitis A cases in children, and the populations from 2001 to 2011 were used as the basic input of the study. Time series maps were created using Geographic Information Systems (GIS) to introduce the temporal changes in the morbidity rates of Hepatitis A. The spatial variation of Hepatitis A was measured using Moran's I at the global level and the local indicators of spatial associations (LISAs) Moran's I and Getis-Ord G i *(d) in order to identify influential locations through clusters and hot spots detection of Hepatitis A cases. The morbidity rates in children under the age of 5 were found significantly lower than the other age-groups, whereas the age-group 5-9 revealed the highest morbidity rates in the study area. The morbidity of Hepatitis A was detected very high for the years 2001, and 2005-2007. The identification of the highly vulnerable provinces was conducted using local Moran's I and local Getis-Ord G i *(d). The majority of clusters and hot spots were detected to be agglomerated in the Eastern Mediterranean and South-Eastern Anatolian Regions and Ceyhan, Asi and Southeast part of Firat-Dicle river basins in Turkey. Copyright © 2017 Elsevier Inc. All rights reserved.

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

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.

Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-04-01

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

NASA Astrophysics Data System (ADS)

Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

2017-06-01

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

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.

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

DOEpatents

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Spatial patterns of leprosy in a hyperendemic state in Northern Brazil, 2001-2012

PubMed Central

Monteiro, Lorena Dias; Martins-Melo, Francisco Rogerlândio; Brito, Aline Lima; Alencar, Carlos Henrique; Heukelbach, Jorg

2015-01-01

ABSTRACT OBJECTIVE To describe the spatial patterns of leprosy in the Brazilian state of Tocantins. METHODS This study was based on morbidity data obtained from the Sistema de Informações de Agravos de Notificação (SINAN – Brazilian Notifiable Diseases Information System), of the Ministry of Health. All new leprosy cases in individuals residing in the state of Tocantins, between 2001 and 2012, were included. In addition to the description of general disease indicators, a descriptive spatial analysis, empirical Bayesian analysis and spatial dependence analysis were performed by means of global and local Moran’s indexes. RESULTS A total of 14,542 new cases were recorded during the period under study. Based on the annual case detection rate, 77.0% of the municipalities were classified as hyperendemic (> 40 cases/100,000 inhabitants). Regarding the annual case detection rate in < 15 years-olds, 65.4% of the municipalities were hyperendemic (10.0 to 19.9 cases/100,000 inhabitants); 26.6% had a detection rate of grade 2 disability cases between 5.0 and 9.9 cases/100,000 inhabitants. There was a geographical overlap of clusters of municipalities with high detection rates in hyperendemic areas. Clusters with high disease risk (global Moran’s index: 0.51; p < 0.001), ongoing transmission (0.47; p < 0.001) and late diagnosis (0.44; p < 0.001) were identified mainly in the central-north and southwestern regions of Tocantins. CONCLUSIONS We identified high-risk clusters for transmission and late diagnosis of leprosy in the Brazilian state of Tocantins. Surveillance and control measures should be prioritized in these high-risk municipalities. PMID:26603352

Effect of spatial smoothing on t-maps: arguments for going back from t-maps to masked contrast images.

PubMed

Reimold, Matthias; Slifstein, Mark; Heinz, Andreas; Mueller-Schauenburg, Wolfgang; Bares, Roland

2006-06-01

Voxelwise statistical analysis has become popular in explorative functional brain mapping with fMRI or PET. Usually, results are presented as voxelwise levels of significance (t-maps), and for clusters that survive correction for multiple testing the coordinates of the maximum t-value are reported. Before calculating a voxelwise statistical test, spatial smoothing is required to achieve a reasonable statistical power. Little attention is being given to the fact that smoothing has a nonlinear effect on the voxel variances and thus the local characteristics of a t-map, which becomes most evident after smoothing over different types of tissue. We investigated the related artifacts, for example, white matter peaks whose position depend on the relative variance (variance over contrast) of the surrounding regions, and suggest improving spatial precision with 'masked contrast images': color-codes are attributed to the voxelwise contrast, and significant clusters (e.g., detected with statistical parametric mapping, SPM) are enlarged by including contiguous pixels with a contrast above the mean contrast in the original cluster, provided they satisfy P < 0.05. The potential benefit is demonstrated with simulations and data from a [11C]Carfentanil PET study. We conclude that spatial smoothing may lead to critical, sometimes-counterintuitive artifacts in t-maps, especially in subcortical brain regions. If significant clusters are detected, for example, with SPM, the suggested method is one way to improve spatial precision and may give the investigator a more direct sense of the underlying data. Its simplicity and the fact that no further assumptions are needed make it a useful complement for standard methods of statistical mapping.

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.

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.

Spatial dynamics of bovine tuberculosis in the Autonomous Community of Madrid, Spain (2010-2012).

PubMed

de la Cruz, Maria Luisa; Perez, Andres; Bezos, Javier; Pages, Enrique; Casal, Carmen; Carpintero, Jesus; Romero, Beatriz; Dominguez, Lucas; Barker, Christopher M; Diaz, Rosa; Alvarez, Julio

2014-01-01

Progress in control of bovine tuberculosis (bTB) is often not uniform, usually due to the effect of one or more sometimes unknown epidemiological factors impairing the success of eradication programs. Use of spatial analysis can help to identify clusters of persistence of disease, leading to the identification of these factors thus allowing the implementation of targeted control measures, and may provide some insights of disease transmission, particularly when combined with molecular typing techniques. Here, the spatial dynamics of bTB in a high prevalence region of Spain were assessed during a three year period (2010-2012) using data from the eradication campaigns to detect clusters of positive bTB herds and of those infected with certain Mycobacterium bovis strains (characterized using spoligotyping and VNTR typing). In addition, the within-herd transmission coefficient (β) was estimated in infected herds and its spatial distribution and association with other potential outbreak and herd variables was evaluated. Significant clustering of positive herds was identified in the three years of the study in the same location ("high risk area"). Three spoligotypes (SB0339, SB0121 and SB1142) accounted for >70% of the outbreaks detected in the three years. VNTR subtyping revealed the presence of few but highly prevalent strains within the high risk area, suggesting maintained transmission in the area. The spatial autocorrelation found in the distribution of the estimated within-herd transmission coefficients in herds located within distances <14 km and the results of the spatial regression analysis, support the hypothesis of shared local factors affecting disease transmission in farms located at a close proximity.

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.

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

Spatial Analysis of the Human Immunodeficiency Virus Epidemic among Men Who Have Sex with Men in China, 2006-2015.

PubMed

Qin, Qianqian; Guo, Wei; Tang, Weiming; Mahapatra, Tanmay; Wang, Liyan; Zhang, Nanci; Ding, Zhengwei; Cai, Chang; Cui, Yan; Sun, Jiangping

2017-04-01

Studies have shown a recent upsurge in human immunodeficiency virus (HIV) burden among men who have sex with men (MSM) in China, especially in urban areas. For intervention planning and resource allocation, spatial analyses of HIV/AIDS case-clusters were required to identify epidemic foci and trends among MSM in China. Information regarding MSM recorded as HIV/AIDS cases during 2006-2015 were extracted from the National Case Reporting System. Demographic trends were determined through Cochran-Armitage trend tests. Distribution of case-clusters was examined using spatial autocorrelation. Spatial-temporal scan was used to detect disease clustering. Spatial correlations between cases and socioenvironmental factors were determined by spatial regression. Between 2006 and 2015, in China, 120 371 HIV/AIDS cases were identified among MSM. Newly identified HIV/AIDS cases among self-reported MSM increased from 487 cases in 2006 to >30 000 cases in 2015. Among those HIV/AIDS cases recorded during 2006-2015, 47.0% were 20-29 years old and 24.9% were aged 30-39 years. Based on clusters of HIV/AIDS cases identified through spatial analysis, the epidemic was concentrated among MSM in large cities. Spatial-temporal clusters contained municipalities, provincial capitals, and main cities such as Beijing, Shanghai, Chongqing, Chengdu, and Guangzhou. Spatial regression analysis showed that sociodemographic indicators such as population density, per capita gross domestic product, and number of county-level medical institutions had statistically significant positive correlations with HIV/AIDS among MSM. Assorted spatial analyses revealed an increasingly concentrated HIV epidemic among young MSM in Chinese cities, calling for targeted health education and intensive interventions at an early age. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

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 Definitive Optical Detection of a Supercluster at Z ~ 0.91

NASA Astrophysics Data System (ADS)

Lubin, Lori M.; Brunner, Robert; Metzger, Mark R.; Postman, Marc; Oke, J. B.

2000-03-01

We present the results from a multiband optical imaging program that has definitively confirmed the existence of a supercluster at z~0.91. Two massive clusters of galaxies, Cl 1604+4304 at z=0.897 and Cl 1604+4321 at z=0.924, were originally observed in the high-redshift cluster survey of Oke, Postman, & Lubin. They are separated by 4300 km s-1 in radial velocity and 17' on the plane of the sky. Their physical and redshift proximity suggested a promising supercluster candidate. Deep BRi imaging of the region between the two clusters indicates a large population of red galaxies. This population forms a tight, red sequence in the color-magnitude diagram at (R-i)~1.4. The characteristic color is identical to that of the spectroscopically confirmed early-type galaxies in the two member clusters. The red galaxies are spread throughout the 5 h-1 Mpc region between Cl 1604+4304 and Cl 1604+4321. Their spatial distribution delineates the entire large-scale structure with high concentrations at the cluster centers. In addition, we detect a significant overdensity of red galaxies directly between Cl 1604+4304 and Cl 1604+4321 which is the signature of a third, rich cluster associated with this system. The strong sequence of red galaxies and their spatial distribution clearly indicate that we have discovered a supercluster at z~0.91.

Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

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

Moody, Daniela Irina

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detectmore » geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.« less

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Automated thematic mapping and change detection of ERTS-A images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.

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.

Dengue Fever Occurrence and Vector Detection by Larval Survey, Ovitrap and MosquiTRAP: A Space-Time Clusters Analysis

PubMed Central

de Melo, Diogo Portella Ornelas; Scherrer, Luciano Rios; Eiras, Álvaro Eduardo

2012-01-01

The use of vector surveillance tools for preventing dengue disease requires fine assessment of risk, in order to improve vector control activities. Nevertheless, the thresholds between vector detection and dengue fever occurrence are currently not well established. In Belo Horizonte (Minas Gerais, Brazil), dengue has been endemic for several years. From January 2007 to June 2008, the dengue vector Aedes (Stegomyia) aegypti was monitored by ovitrap, the sticky-trap MosquiTRAP™ and larval surveys in an study area in Belo Horizonte. Using a space-time scan for clusters detection implemented in SaTScan software, the vector presence recorded by the different monitoring methods was evaluated. Clusters of vectors and dengue fever were detected. It was verified that ovitrap and MosquiTRAP vector detection methods predicted dengue occurrence better than larval survey, both spatially and temporally. MosquiTRAP and ovitrap presented similar results of space-time intersections to dengue fever clusters. Nevertheless ovitrap clusters presented longer duration periods than MosquiTRAP ones, less acuratelly signalizing the dengue risk areas, since the detection of vector clusters during most of the study period was not necessarily correlated to dengue fever occurrence. It was verified that ovitrap clusters occurred more than 200 days (values ranged from 97.0±35.35 to 283.0±168.4 days) before dengue fever clusters, whereas MosquiTRAP clusters preceded dengue fever clusters by approximately 80 days (values ranged from 65.5±58.7 to 94.0±14. 3 days), the former showing to be more temporally precise. Thus, in the present cluster analysis study MosquiTRAP presented superior results for signaling dengue transmission risks both geographically and temporally. Since early detection is crucial for planning and deploying effective preventions, MosquiTRAP showed to be a reliable tool and this method provides groundwork for the development of even more precise tools. PMID:22848729

Spatial Dynamics of Bovine Tuberculosis in the Autonomous Community of Madrid, Spain (2010–2012)

PubMed Central

de la Cruz, Maria Luisa; Perez, Andres; Bezos, Javier; Pages, Enrique; Casal, Carmen; Carpintero, Jesus; Romero, Beatriz; Dominguez, Lucas; Barker, Christopher M.; Diaz, Rosa; Alvarez, Julio

2014-01-01

Progress in control of bovine tuberculosis (bTB) is often not uniform, usually due to the effect of one or more sometimes unknown epidemiological factors impairing the success of eradication programs. Use of spatial analysis can help to identify clusters of persistence of disease, leading to the identification of these factors thus allowing the implementation of targeted control measures, and may provide some insights of disease transmission, particularly when combined with molecular typing techniques. Here, the spatial dynamics of bTB in a high prevalence region of Spain were assessed during a three year period (2010–2012) using data from the eradication campaigns to detect clusters of positive bTB herds and of those infected with certain Mycobacterium bovis strains (characterized using spoligotyping and VNTR typing). In addition, the within-herd transmission coefficient (β) was estimated in infected herds and its spatial distribution and association with other potential outbreak and herd variables was evaluated. Significant clustering of positive herds was identified in the three years of the study in the same location (“high risk area”). Three spoligotypes (SB0339, SB0121 and SB1142) accounted for >70% of the outbreaks detected in the three years. VNTR subtyping revealed the presence of few but highly prevalent strains within the high risk area, suggesting maintained transmission in the area. The spatial autocorrelation found in the distribution of the estimated within-herd transmission coefficients in herds located within distances <14 km and the results of the spatial regression analysis, support the hypothesis of shared local factors affecting disease transmission in farms located at a close proximity. PMID:25536514

Spatial Autocorrelation of Cancer Incidence in Saudi Arabia

PubMed Central

Al-Ahmadi, Khalid; Al-Zahrani, Ali

2013-01-01

Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran’s I index values, indicating a tendency toward clustering. The Anselin’s local Moran’s I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin’s disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin’s disease (r² = 0.49–0.67 using OLS and r² = 0.52–0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations. PMID:24351742

Spatial-temporal epidemiology of human Salmonella Enteritidis infections with major phage types (PTs 1, 4, 5b, 8, 13, and 13a) in Ontario, Canada, 2008-2009.

PubMed

Varga, Csaba; Pearl, David L; McEwen, Scott A; Sargeant, Jan M; Pollari, Frank; Guerin, Michele T

2015-12-17

In Ontario and Canada, the incidence of human Salmonella enterica serotype Enteritidis (S. Enteritidis) infections have increased steadily during the last decade. Our study evaluated the spatial and temporal epidemiology of the major phage types (PTs) of S. Enteritidis infections to aid public health practitioners design effective prevention and control programs. Data on S. Enteritidis infections between January 1, 2008 and December 31, 2009 were obtained from Ontario's disease surveillance system. Salmonella Enteritidis infections with major phage types were classified by their annual health region-level incidence rates (IRs), monthly IRs, clinical symptoms, and exposure settings. A scan statistic was employed to detect retrospective phage type-specific spatial, temporal, and space-time clusters of S. Enteritidis infections. Space-time cluster cases' exposure settings were evaluated to identify common exposures. 1,336 cases were available for analysis. The six most frequently reported S. Enteritidis PTs were 8 (n = 398), 13a (n = 218), 13 (n = 198), 1 (n = 132), 5b (n = 83), and 4 (n = 76). Reported rates of S. Enteritidis infections with major phage types varied by health region and month. International travel and unknown exposure settings were the most frequently reported settings for PT 5b, 4, and 1 cases, whereas unknown exposure setting, private home, food premise, and international travel were the most frequently reported settings for PT 8, 13, and 13a cases. Diarrhea, abdominal pain, and fever were the most commonly reported clinical symptoms. A number of phage type-specific spatial, temporal, and space-time clusters were identified. Space-time clusters of PTs 1, 4, and 5b occurred mainly during the winter and spring months in the North West, North East, Eastern, Central East, and Central West regions. Space-time clusters of PTs 13 and 13a occurred at different times of the year in the Toronto region. Space-time clusters of PT 8 occurred at different times of the year in the North West and South West regions. Phage type-specific differences in exposure settings, and spatial-temporal clustering of S. Enteritidis infections were demonstrated that might guide public health surveillance of disease outbreaks. Our study methodology could be applied to other foodborne disease surveillance data to detect retrospective high disease rate clusters, which could aid public health authorities in developing effective prevention and control programs.

Spatial analysis improves the detection of early corneal nerve fiber loss in patients with recently diagnosed type 2 diabetes

PubMed Central

Winter, Karsten; Strom, Alexander; Zhivov, Andrey; Allgeier, Stephan; Papanas, Nikolaos; Ziegler, Iris; Brüggemann, Jutta; Ringel, Bernd; Peschel, Sabine; Köhler, Bernd; Stachs, Oliver; Guthoff, Rudolf F.; Roden, Michael

2017-01-01

Corneal confocal microscopy (CCM) has revealed reduced corneal nerve fiber (CNF) length and density (CNFL, CNFD) in patients with diabetes, but the spatial pattern of CNF loss has not been studied. We aimed to determine whether spatial analysis of the distribution of corneal nerve branching points (CNBPs) may contribute to improving the detection of early CNF loss. We hypothesized that early CNF decline follows a clustered rather than random distribution pattern of CNBPs. CCM, nerve conduction studies (NCS), and quantitative sensory testing (QST) were performed in a cross-sectional study including 86 patients recently diagnosed with type 2 diabetes and 47 control subjects. In addition to CNFL, CNFD, and branch density (CNBD), CNBPs were analyzed using spatial point pattern analysis (SPPA) including 10 indices and functional statistics. Compared to controls, patients with diabetes showed lower CNBP density and higher nearest neighbor distances, and all SPPA parameters indicated increased clustering of CNBPs (all P<0.05). SPPA parameters were abnormally increased >97.5th percentile of controls in up to 23.5% of patients. When combining an individual SPPA parameter with CNFL, ≥1 of 2 indices were >99th or <1st percentile of controls in 28.6% of patients compared to 2.1% of controls, while for the conventional CNFL/CNFD/CNBD combination the corresponding rates were 16.3% vs 2.1%. SPPA parameters correlated with CNFL and several NCS and QST indices in the controls (all P<0.001), whereas in patients with diabetes these correlations were markedly weaker or lost. In conclusion, SPPA reveals increased clustering of early CNF loss and substantially improves its detection when combined with a conventional CCM measure in patients with recently diagnosed type 2 diabetes. PMID:28296936

Mapping concentrations of posttraumatic stress and depression trajectories following Hurricane Ike

PubMed Central

Gruebner, Oliver; Lowe, Sarah R.; Tracy, Melissa; Joshi, Spruha; Cerdá, Magdalena; Norris, Fran H.; Subramanian, S. V.; Galea, Sandro

2016-01-01

We investigated geographic concentration in elevated risk for a range of postdisaster trajectories of chronic posttraumatic stress symptom (PTSS) and depression symptoms in a longitudinal study (N = 561) of a Hurricane Ike affected population in Galveston and Chambers counties, TX. Using an unadjusted spatial scan statistic, we detected clusters of elevated risk of PTSS trajectories, but not depression trajectories, on Galveston Island. We then tested for predictors of membership in each trajectory of PTSS and depression (e.g., demographic variables, trauma exposure, social support), not taking the geographic nature of the data into account. After adjusting for significant predictors in the spatial scan statistic, we noted that spatial clusters of PTSS persisted and additional clusters of depression trajectories emerged. This is the first study to show that longitudinal trajectories of postdisaster mental health problems may vary depending on the geographic location and the individual- and community-level factors present at these locations. Such knowledge is crucial to identifying vulnerable regions and populations within them, to provide guidance for early responders, and to mitigate mental health consequences through early detection of mental health needs in the population. As human-made disasters increase, our approach may be useful also in other regions in comparable settings worldwide. PMID:27558011

Mapping concentrations of posttraumatic stress and depression trajectories following Hurricane Ike.

PubMed

Gruebner, Oliver; Lowe, Sarah R; Tracy, Melissa; Joshi, Spruha; Cerdá, Magdalena; Norris, Fran H; Subramanian, S V; Galea, Sandro

2016-08-25

We investigated geographic concentration in elevated risk for a range of postdisaster trajectories of chronic posttraumatic stress symptom (PTSS) and depression symptoms in a longitudinal study (N = 561) of a Hurricane Ike affected population in Galveston and Chambers counties, TX. Using an unadjusted spatial scan statistic, we detected clusters of elevated risk of PTSS trajectories, but not depression trajectories, on Galveston Island. We then tested for predictors of membership in each trajectory of PTSS and depression (e.g., demographic variables, trauma exposure, social support), not taking the geographic nature of the data into account. After adjusting for significant predictors in the spatial scan statistic, we noted that spatial clusters of PTSS persisted and additional clusters of depression trajectories emerged. This is the first study to show that longitudinal trajectories of postdisaster mental health problems may vary depending on the geographic location and the individual- and community-level factors present at these locations. Such knowledge is crucial to identifying vulnerable regions and populations within them, to provide guidance for early responders, and to mitigate mental health consequences through early detection of mental health needs in the population. As human-made disasters increase, our approach may be useful also in other regions in comparable settings worldwide.

Micro-epidemiology and spatial heterogeneity of P. vivax parasitaemia in riverine communities of the Peruvian Amazon: A multilevel analysis.

PubMed

Carrasco-Escobar, Gabriel; Gamboa, Dionicia; Castro, Marcia C; Bangdiwala, Shrikant I; Rodriguez, Hugo; Contreras-Mancilla, Juan; Alava, Freddy; Speybroeck, Niko; Lescano, Andres G; Vinetz, Joseph M; Rosas-Aguirre, Angel; Llanos-Cuentas, Alejandro

2017-08-14

Malaria has steadily increased in the Peruvian Amazon over the last five years. This study aimed to determine the parasite prevalence and micro-geographical heterogeneity of Plasmodium vivax parasitaemia in communities of the Peruvian Amazon. Four cross-sectional active case detection surveys were conducted between May and July 2015 in four riverine communities in Mazan district. Analysis of 2785 samples of 820 individuals nested within 154 households for Plasmodium parasitaemia was carried out using light microscopy and qPCR. The spatio-temporal distribution of Plasmodium parasitaemia, dominated by P. vivax, was shown to cluster at both household and community levels. Of enrolled individuals, 47% had at least one P. vivax parasitaemia and 10% P. falciparum, by qPCR, both of which were predominantly sub-microscopic and asymptomatic. Spatial analysis detected significant clustering in three communities. Our findings showed that communities at small-to-moderate spatial scales differed in P. vivax parasite prevalence, and multilevel Poisson regression models showed that such differences were influenced by factors such as age, education, and location of households within high-risk clusters, as well as factors linked to a local micro-geographic context, such as travel and occupation. Complex transmission patterns were found to be related to human mobility among communities in the same micro-basin.

Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

PubMed Central

Kanas, Vasileios G.; Mporas, Iosif; Benz, Heather L.; Sgarbas, Kyriakos N.; Bezerianos, Anastasios; Crone, Nathan E.

2014-01-01

Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication. PMID:24658248

Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

NASA Astrophysics Data System (ADS)

Verma, S.; Gupta, R. D.

2014-11-01

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

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

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.

Automated detection of case clusters of waterborne acute gastroenteritis from health insurance data - pilot study in three French districts.

PubMed

Rambaud, Loïc; Galey, Catherine; Beaudeau, Pascal

2016-04-01

This pilot study was conducted to assess the utility of using a health insurance database for the automated detection of waterborne outbreaks of acute gastroenteritis (AGE). The weekly number of AGE cases for which the patient consulted a doctor (cAGE) was derived from this database for 1,543 towns in three French districts during the 2009-2012 period. The method we used is based on a spatial comparison of incidence rates and of their time trends between the target town and the district. Each municipality was tested, week by week, for the entire study period. Overall, 193 clusters were identified, 10% of the municipalities were involved in at least one cluster and less than 2% in several. We can infer that nationwide more than 1,000 clusters involving 30,000 cases of cAGE each year may be linked to tap water. The clusters discovered with this automated detection system will be reported to local operators for investigation of the situations at highest risk. This method will be compared with others before automated detection is implemented on a national level.

Analysis of variables affecting unemployment rate and detecting for cluster in West Java, Central Java, and East Java in 2012

NASA Astrophysics Data System (ADS)

Samuel, Putra A.; Widyaningsih, Yekti; Lestari, Dian

2016-02-01

The objective of this study is modeling the Unemployment Rate (UR) in West Java, Central Java, and East Java, with rate of disease, infant mortality rate, educational level, population size, proportion of married people, and GDRP as the explanatory variables. Spatial factors are also considered in the modeling since the closer the distance, the higher the correlation. This study uses the secondary data from BPS (Badan Pusat Statistik). The data will be analyzed using Moran I test, to obtain the information about spatial dependence, and using Spatial Autoregressive modeling to obtain the information, which variables are significant affecting UR and how great the influence of the spatial factors. The result is, variables proportion of married people, rate of disease, and population size are related significantly to UR. In all three regions, the Hotspot of unemployed will also be detected districts/cities using Spatial Scan Statistics Method. The results are 22 districts/cities as a regional group with the highest unemployed (Most likely cluster) in the study area; 2 districts/cities as a regional group with the highest unemployed in West Java; 1 district/city as a regional groups with the highest unemployed in Central Java; 15 districts/cities as a regional group with the highest unemployed in East Java.

Hierarchical modeling of cluster size in wildlife surveys

USGS Publications Warehouse

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

A robust fuzzy local Information c-means clustering algorithm with noise detection

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Huang, Junwei

2018-04-01

Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.

Spatial clustering and risk factors of malaria infections in Bata district, Equatorial Guinea.

PubMed

Gómez-Barroso, Diana; García-Carrasco, Emely; Herrador, Zaida; Ncogo, Policarpo; Romay-Barja, María; Ondo Mangue, Martín Eka; Nseng, Gloria; Riloha, Matilde; Santana, Maria Angeles; Valladares, Basilio; Aparicio, Pilar; Benito, Agustín

2017-04-12

The transmission of malaria is intense in the majority of the countries of sub-Saharan Africa, particularly in those that are located along the Equatorial strip. The present study aimed to describe the current distribution of malaria prevalence among children and its environment-related factors as well as to detect malaria spatial clusters in the district of Bata, in Equatorial Guinea. From June to August 2013 a representative cross-sectional survey using a multistage, stratified, cluster-selected sample was carried out of children in urban and rural areas of Bata District. All children were tested for malaria using rapid diagnostic tests (RDTs). Results were linked to each household by global position system data. Two cluster analysis methods were used: hot spot analysis using the Getis-Ord Gi statistic, and the SaTScan™ spatial statistic estimates, based on the assumption of a Poisson distribution to detect spatial clusters. In addition, univariate associations and Poisson regression model were used to explore the association between malaria prevalence at household level with different environmental factors. A total of 1416 children aged 2 months to 15 years living in 417 households were included in this study. Malaria prevalence by RDTs was 47.53%, being highest in the age group 6-15 years (63.24%, p < 0.001). Those children living in rural areas were there malaria risk is greater (65.81%) (p < 0.001). Malaria prevalence was higher in those houses located <1 km from a river and <3 km to a forest (IRR: 1.31; 95% CI 1.13-1.51 and IRR: 1.44; 95% CI 1.25-1.66, respectively). Poisson regression analysis also showed a decrease in malaria prevalence with altitude (IRR: 0.73; 95% CI 0.62-0.86). A significant cluster inland of the district, in rural areas has been found. This study reveals a high prevalence of RDT-based malaria among children in Bata district. Those households situated in inland rural areas, near to a river, a green area and/or at low altitude were a risk factor for malaria. Spatial tools can help policy makers to promote new recommendations for malaria control.

Actual distribution of Cronobacter spp. in industrial batches of powdered infant formula and consequences for performance of sampling strategies.

PubMed

Jongenburger, I; Reij, M W; Boer, E P J; Gorris, L G M; Zwietering, M H

2011-11-15

The actual spatial distribution of microorganisms within a batch of food influences the results of sampling for microbiological testing when this distribution is non-homogeneous. In the case of pathogens being non-homogeneously distributed, it markedly influences public health risk. This study investigated the spatial distribution of Cronobacter spp. in powdered infant formula (PIF) on industrial batch-scale for both a recalled batch as well a reference batch. Additionally, local spatial occurrence of clusters of Cronobacter cells was assessed, as well as the performance of typical sampling strategies to determine the presence of the microorganisms. The concentration of Cronobacter spp. was assessed in the course of the filling time of each batch, by taking samples of 333 g using the most probable number (MPN) enrichment technique. The occurrence of clusters of Cronobacter spp. cells was investigated by plate counting. From the recalled batch, 415 MPN samples were drawn. The expected heterogeneous distribution of Cronobacter spp. could be quantified from these samples, which showed no detectable level (detection limit of -2.52 log CFU/g) in 58% of samples, whilst in the remainder concentrations were found to be between -2.52 and 2.75 log CFU/g. The estimated average concentration in the recalled batch was -2.78 log CFU/g and a standard deviation of 1.10 log CFU/g. The estimated average concentration in the reference batch was -4.41 log CFU/g, with 99% of the 93 samples being below the detection limit. In the recalled batch, clusters of cells occurred sporadically in 8 out of 2290 samples of 1g taken. The two largest clusters contained 123 (2.09 log CFU/g) and 560 (2.75 log CFU/g) cells. Various sampling strategies were evaluated for the recalled batch. Taking more and smaller samples and keeping the total sampling weight constant, considerably improved the performance of the sampling plans to detect such a type of contaminated batch. Compared to random sampling, stratified random sampling improved the probability to detect the heterogeneous contamination. Copyright © 2011 Elsevier B.V. All rights reserved.

The Arecibo Galaxy Environment Survey - VI. The Virgo cluster (II)

NASA Astrophysics Data System (ADS)

Taylor, R.; Davies, J. I.; Auld, R.; Minchin, R. F.; Smith, R.

2013-01-01

We present 21-cm observations of a 5 × 1 deg2 region in the Virgo cluster, obtained as part of the Arecibo Galaxy Environment Survey. 13 cluster members are detected, together with 36 objects in the background. We compare and contrast the results from this area with a larger 10 × 2 deg2 region. We combine the two data sets to produce an H i mass function, which shows a higher detection rate at low masses (but finds fewer massive galaxies) than less sensitive wider area surveys, such as ALFALFA. We find that the H i-detected galaxies are distributed differently to the non-detections, both spatially and in velocity, providing further evidence that the cluster is still assembling. We use the Tully-Fisher relation to examine the possibility of morphological evolution. We find that highly deficient galaxies, as well as some early-type galaxies, have much lower velocity widths than the Tully-Fisher relation predicts, indicating gas loss via ram-pressure stripping. We also find that H i detections without optical counterparts do not fit the predictions of the baryonic Tully-Fisher relation, implying that they are not primordial objects.

Regional Patterns and Spatial Clusters of Nonstationarities in Annual Peak Instantaneous Streamflow

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

Information about hydrologic changes resulting from changes in climate, land use, and land cover is a necessity planning and design or water resources infrastructure. The United States Army Corps of Engineers (USACE) evaluated and selected 12 methods to detect abrupt and slowly varying nonstationarities in records of maximum peak annual flows. They deployed a publicly available tool[1]in 2016 and a guidance document in 2017 to support identification of nonstationarities in a reproducible manner using a robust statistical framework. This statistical framework has now been applied to streamflow records across the continental United States to explore the presence of regional patterns and spatial clusters of nonstationarities in peak annual flow. Incorporating this geographic dimension into the detection of nonstationarities provides valuable insight for the process of attribution of these significant changes. This poster summarizes the methods used and provides the results of the regional analysis. [1] Available here - http://www.corpsclimate.us/ptcih.cfm

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.

Using estimates of natural variation to detect ecologically important change in forest spatial patterns: a case study, Cascade Range, eastern Washington.

Treesearch

Paul F. Hessburg; Bradley G. Smith; R. Brion Salter

1999-01-01

Using hierarchical clustering techniques, we grouped subwatersheds on the eastern slope of the Cascade Range in Washington State into ecological subregions by similarity of area in potential vegetation and climate attributes. We then built spatially continuous historical and current vegetation maps for 48 randomly selected subwatersheds from interpretations of 1938-49...

Detecting spatial regimes in ecosystems | Science Inventory ...

EPA Pesticide Factsheets

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 (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect 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. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning

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.

Investigating the Spatial Dimension of Food Access.

PubMed

Yenerall, Jackie; You, Wen; Hill, Jennie

2017-08-02

The purpose of this article is to investigate the sensitivity of food access models to a dataset's spatial distribution and the empirical definition of food access, which contributes to understanding the mixed findings of previous studies. Data was collected in the Dan River Region in the United States using a telephone survey for individual-level variables ( n = 784) and a store audit for the location of food retailers and grocery store quality. Spatial scanning statistics assessed the spatial distribution of obesity and detected a cluster of grocery stores overlapping with a cluster of obesity centered on a grocery store suggesting that living closer to a grocery store increased the likelihood of obesity. Logistic regression further examined this relationship while controlling for demographic and other food environment variables. Similar to the cluster analysis results, increased distance to a grocery store significantly decreased the likelihood of obesity in the urban subsample (average marginal effects, AME = -0.09, p -value = 0.02). However, controlling for grocery store quality nullified these results (AME = -0.12, p -value = 0.354). Our findings suggest that measuring grocery store accessibility as the distance to the nearest grocery store captures variability in the spatial distribution of the health outcome of interest that may not reflect a causal relationship between the food environment and health.

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 Analysis of Rice Blast in China at Three Different Scales.

PubMed

Guo, Fangfang; Chen, Xinglong; Lu, Minghong; Yang, Li; Wang, Shi Wei; Wu, Bo Ming

2018-05-22

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from June 10 th to Sep. 10 th during 2009-2014, and surveyed in 143 fields in September, 2016; at county scale, 3 surveys were done covering 1-5 counties in 2015-2016; and at field scale, blast was evaluated in 6 fields in 2015-2016. Spatial cluster and hot spot analyses were conducted in GIS on the geographical pattern of the disease at regional scale, and geostatistical analysis performed at all the three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1080 km at regional scale, and 5 to 10 m at field scale, but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Investigating the Spatial Dimension of Food Access

PubMed Central

Yenerall, Jackie; You, Wen

2017-01-01

The purpose of this article is to investigate the sensitivity of food access models to a dataset’s spatial distribution and the empirical definition of food access, which contributes to understanding the mixed findings of previous studies. Data was collected in the Dan River Region in the United States using a telephone survey for individual-level variables (n = 784) and a store audit for the location of food retailers and grocery store quality. Spatial scanning statistics assessed the spatial distribution of obesity and detected a cluster of grocery stores overlapping with a cluster of obesity centered on a grocery store suggesting that living closer to a grocery store increased the likelihood of obesity. Logistic regression further examined this relationship while controlling for demographic and other food environment variables. Similar to the cluster analysis results, increased distance to a grocery store significantly decreased the likelihood of obesity in the urban subsample (average marginal effects, AME = −0.09, p-value = 0.02). However, controlling for grocery store quality nullified these results (AME = −0.12, p-value = 0.354). Our findings suggest that measuring grocery store accessibility as the distance to the nearest grocery store captures variability in the spatial distribution of the health outcome of interest that may not reflect a causal relationship between the food environment and health. PMID:28767093

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Resolving galaxy cluster gas properties at z ˜ 1 with XMM-Newton and Chandra

NASA Astrophysics Data System (ADS)

Bartalucci, I.; Arnaud, M.; Pratt, G. W.; Démoclès, J.; van der Burg, R. F. J.; Mazzotta, P.

2017-02-01

Massive, high-redshift, galaxy clusters are useful laboratories to test cosmological models and to probe structure formation and evolution, but observations are challenging due to cosmological dimming and angular distance effects. Here we present a pilot X-ray study of the five most massive (M500 > 5 × 1014M⊙), distant (z 1), clusters detected via the Sunyaev-Zel'Dovich effect. We optimally combine XMM-Newton and Chandra X-ray observations by leveraging the throughput of XMM-Newton to obtain spatially-resolved spectroscopy, and the spatial resolution of Chandra to probe the bright inner parts and to detect embedded point sources. Capitalising on the excellent agreement in flux-related measurements, we present a new method to derive the density profiles, which are constrained in the centre by Chandra and in the outskirts by XMM-Newton. We show that the Chandra-XMM-Newton combination is fundamental for morphological analysis at these redshifts, the Chandra resolution being required to remove point source contamination, and the XMM-Newton sensitivity allowing higher significance detection of faint substructures. Measuring the morphology using images from both instruments, we found that the sample is dominated by dynamically disturbed objects. We use the combined Chandra-XMM-Newton density profiles and spatially-resolved temperature profiles to investigate thermodynamic quantities including entropy and pressure. From comparison of the scaled profiles with the local REXCESS sample, we find no significant departure from standard self-similar evolution, within the dispersion, at any radius, except for the entropy beyond 0.7 R500. The baryon mass fraction tends towards the cosmic value, with a weaker dependence on mass than that observed in the local Universe. We make a comparison with the predictions from numerical simulations. The present pilot study demonstrates the utility and feasibility of spatially-resolved analysis of individual objects at high-redshift through the combination of XMM-Newton and Chandra observations. Observations of a larger sample will allow a fuller statistical analysis to be undertaken, in particular of the intrinsic scatter in the structural and scaling properties of the cluster population.

A log-Weibull spatial scan statistic for time to event data.

PubMed

Usman, Iram; Rosychuk, Rhonda J

2018-06-13

Spatial scan statistics have been used for the identification of geographic clusters of elevated numbers of cases of a condition such as disease outbreaks. These statistics accompanied by the appropriate distribution can also identify geographic areas with either longer or shorter time to events. Other authors have proposed the spatial scan statistics based on the exponential and Weibull distributions. We propose the log-Weibull as an alternative distribution for the spatial scan statistic for time to events data and compare and contrast the log-Weibull and Weibull distributions through simulation studies. The effect of type I differential censoring and power have been investigated through simulated data. Methods are also illustrated on time to specialist visit data for discharged patients presenting to emergency departments for atrial fibrillation and flutter in Alberta during 2010-2011. We found northern regions of Alberta had longer times to specialist visit than other areas. We proposed the spatial scan statistic for the log-Weibull distribution as a new approach for detecting spatial clusters for time to event data. The simulation studies suggest that the test performs well for log-Weibull data.

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.

Spatio-temporal cluster detection of chickenpox in Valencia, Spain in the period 2008-2012.

PubMed

Iftimi, Adina; Martínez-Ruiz, Francisco; Míguez Santiyán, Ana; Montes, Francisco

2015-05-18

Chickenpox is a highly contagious airborne disease caused by Varicella zoster, which affects nearly all non-immune children worldwide with an annual incidence estimated at 80-90 million cases. To analyze the spatiotemporal pattern of the chickenpox incidence in the city of Valencia, Spain two complementary statistical approaches were used. First, we evaluated the existence of clusters and spatio-temporal interaction; secondly, we used this information to find the locations of the spatio-temporal clusters via the space-time permutation model. The first method used detects any aggregation in our data but does not provide the spatial and temporal information. The second method gives the locations, areas and time-frame for the spatio-temporal clusters. An overall decreasing time trend, a pronounced 12-monthly periodicity and two complementary periods were observed. Several areas with high incidence, surrounding the center of the city were identified. The existence of aggregation in time and space was observed, and a number of spatio-temporal clusters were located.

The remarkable geographical pattern of gastric cancer mortality in Ecuador.

PubMed

Montero-Oleas, Nadia; Núñez-González, Solange; Simancas-Racines, Daniel

2017-12-01

This study was aimed to describe the gastric cancer mortality trend, and to analyze the spatial distribution of gastric cancer mortality in Ecuador, between 2004 and 2015. Data were collected from the National Institute of Statistics and Census (INEC) database. Crude gastric cancer mortality rates, standardized mortality ratios (SMRs) and indirect standardized mortality rates (ISMRs) were calculated per 100,000 persons. For time trend analysis, joinpoint regression was used. The annual percentage rate change (APC) and the average annual percent change (AAPC) was computed for each province. Spatial age-adjusted analysis was used to detect high risk clusters of gastric cancer mortality, from 2010 to 2015, using Kulldorff spatial scan statistics. In Ecuador, between 2004 and 2015, gastric cancer caused a total of 19,115 deaths: 10,679 in men and 8436 in women. When crude rates were analyzed, a significant decline was detected (AAPC: -1.8%; p<0.001). ISMR also decreased, but this change was not statistically significant (APC: -0.53%; p=0.36). From 2004 to 2007 and from 2008 to 2011 the province with the highest ISMR was Carchi; and, from 2012 to 2015, was Cotopaxi. The most likely high occurrence cluster included Bolívar, Los Ríos, Chimborazo, Tungurahua, and Cotopaxi provinces, with a relative risk of 1.34 (p<0.001). There is a substantial geographic variation in gastric cancer mortality rates among Ecuadorian provinces. The spatial analysis indicates the presence of high occurrence clusters throughout the Andes Mountains. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Missing Link in Galaxy Evolution: The Mysteries of Dissolving Star Clusters

NASA Astrophysics Data System (ADS)

Pellerin, Anne; Meyer, Martin; Harris, Jason; Calzetti, Daniela

2007-05-01

Star-forming events in starbursts and normal galaxies have a direct impact on the global stellar content of galaxies. These events create numerous compact clusters where stars are produced in great number. These stars eventually end up in the star field background where they are smoothly distributed. However, due to instrumental limitations such as spatial resolution and sensitivity, the processes involved during the transition phase from the compact clusters to the star field background as well as the impact of the environment (spiral waves, bars, starburst) on the lifetime of clusters are still poorly constrained observationally. I will present our latest results on the physical properties of dissolving clusters directly detected in HST/ACS archival images of the three nearby galaxies IC 2574, NGC 1313, and IC 10 (D < 5 Mpc). The ACS has the capability to detect and spatially resolve individual stars in nearby galaxies within a large field-of-view. For all ACS images obtained in three filters (F435W, F555W or F606W, and F814W), we performed PSF stellar photometry in crowded field. Color-magnitude diagrams (CMD) allow us to identify the most massive stars more likely to be part of dissolving clusters (A-type and earlier), and to isolate them from the star field background. We then adapt and use a clustering algorithm on the selected stars to find groups of stars to reveal and quantify the properties of all star clusters (compactness, size, age, mass). With this algorithm, even the less compact clusters are revealed while they are being destroyed. Our sample of three galaxies covers an interesting range in gravitational potential well and explores a variety of galaxy morphological types, which allows us to discuss the dissolving cluster properties as a function of the host galaxy characteristics. The properties of the star field background will also be discussed.

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.

Quantifying clutter: A comparison of four methods and their relationship to bat detection

Treesearch

Joy M. O’Keefe; Susan C. Loeb; Hoke S. Hill Jr.; J. Drew Lanham

2014-01-01

The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these...

Shocks and cold fronts in merging and massive galaxy clusters: new detections with Chandra

NASA Astrophysics Data System (ADS)

Botteon, A.; Gastaldello, F.; Brunetti, G.

2018-06-01

A number of merging galaxy clusters show the presence of shocks and cold fronts, i.e. sharp discontinuities in surface brightness and temperature. The observation of these features requires an X-ray telescope with high spatial resolution like Chandra, and allows to study important aspects concerning the physics of the intracluster medium (ICM), such as its thermal conduction and viscosity, as well as to provide information on the physical conditions leading to the acceleration of cosmic rays and magnetic field amplification in the cluster environment. In this work we search for new discontinuities in 15 merging and massive clusters observed with Chandra by using different imaging and spectral techniques of X-ray observations. Our analysis led to the discovery of 22 edges: six shocks, eight cold fronts, and eight with uncertain origin. All the six shocks detected have M< 2 derived from density and temperature jumps. This work contributed to increase the number of discontinuities detected in clusters and shows the potential of combining diverse approaches aimed to identify edges in the ICM. A radio follow-up of the shocks discovered in this paper will be useful to study the connection between weak shocks and radio relics.

Three-dimensional reconstruction of clustered microcalcifications from two digitized mammograms

NASA Astrophysics Data System (ADS)

Stotzka, Rainer; Mueller, Tim O.; Epper, Wolfgang; Gemmeke, Hartmut

1998-06-01

X-ray mammography is one of the most significant diagnosis methods in early detection of breast cancer. Usually two X- ray images from different angles are taken from each mamma to make even overlapping structures visible. X-ray mammography has a very high spatial resolution and can show microcalcifications of 50 - 200 micron in size. Clusters of microcalcifications are one of the most important and often the only indicator for malignant tumors. These calcifications are in some cases extremely difficult to detect. Computer assisted diagnosis of digitized mammograms may improve detection and interpretation of microcalcifications and cause more reliable diagnostic findings. We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically. New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal. The real problem using only two or three projections for reconstruction is the big loss of volume information. Therefore the arrangement of a cluster is estimated using only the positions of segmented microcalcifications. The arrangement of microcalcifications is visualized to the physician by rotating.

Epidemiological analysis, detection, and comparison of space-time patterns of Beijing hand-foot-mouth disease (2008-2012).

PubMed

Wang, Jiaojiao; Cao, Zhidong; Zeng, Daniel Dajun; Wang, Quanyi; Wang, Xiaoli; Qian, Haikun

2014-01-01

Hand, foot, and mouth disease (HFMD) mostly affects the health of infants and preschool children. Many studies of HFMD in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level HFMD cases in a major city such as Beijing are unknown. The objective of this study was to investigate epidemiological features and identify high relative risk space-time HFMD clusters at a fine spatial scale. Detailed information on age, occupation, pathogen and gender was used to analyze the epidemiological features of HFMD epidemics. Data on individual-level HFMD cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify the spatial autocorrelation of HFMD incidence. Spatial filtering combined with scan statistics methods were used to detect HFMD clusters. A total of 157,707 HFMD cases (60.25% were male, 39.75% were female) reported in Beijing from 2008 to 2012 included 1465 severe cases and 33 fatal cases. The annual average incidence rate was 164.3 per 100,000 (ranged from 104.2 in 2008 to 231.5 in 2010). Male incidence was higher than female incidence for the 0 to 14-year age group, and 93.88% were nursery children or lived at home. Areas at a higher relative risk were mainly located in the urban-rural transition zones (the percentage of the population at risk ranged from 33.89% in 2011 to 39.58% in 2012) showing High-High positive spatial association for HFMD incidence. The most likely space-time cluster was located in the mid-east part of the Fangshan district, southwest of Beijing. The spatial-time patterns of Beijing HFMD (2008-2012) showed relatively steady. The population at risk were mainly distributed in the urban-rural transition zones. Epidemiological features of Beijing HFMD were generally consistent with the previous research. The findings generated computational insights useful for disease surveillance, risk assessment and early warning.

Retrospective space-time cluster analysis of whooping cough, re-emergence in Barcelona, Spain, 2000-2011.

PubMed

Solano, Rubén; Gómez-Barroso, Diana; Simón, Fernando; Lafuente, Sarah; Simón, Pere; Rius, Cristina; Gorrindo, Pilar; Toledo, Diana; Caylà, Joan A

2014-05-01

A retrospective, space-time study of whooping cough cases reported to the Public Health Agency of Barcelona, Spain between the years 2000 and 2011 is presented. It is based on 633 individual whooping cough cases and the 2006 population census from the Spanish National Statistics Institute, stratified by age and sex at the census tract level. Cluster identification was attempted using space-time scan statistic assuming a Poisson distribution and restricting temporal extent to 7 days and spatial distance to 500 m. Statistical calculations were performed with Stata 11 and SatScan and mapping was performed with ArcGis 10.0. Only clusters showing statistical significance (P <0.05) were mapped. The most likely cluster identified included five census tracts located in three neighbourhoods in central Barcelona during the week from 17 to 23 August 2011. This cluster included five cases compared with the expected level of 0.0021 (relative risk = 2436, P <0.001). In addition, 11 secondary significant space-time clusters were detected with secondary clusters occurring at different times and localizations. Spatial statistics is felt to be useful by complementing epidemiological surveillance systems through visualizing excess in the number of cases in space and time and thus increase the possibility of identifying outbreaks not reported by the surveillance system.

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

High quality high spatial resolution functional classification in low dose dynamic CT perfusion using singular value decomposition (SVD) and k-means clustering

NASA Astrophysics Data System (ADS)

Pisana, Francesco; Henzler, Thomas; Schönberg, Stefan; Klotz, Ernst; Schmidt, Bernhard; Kachelrieß, Marc

2017-03-01

Dynamic CT perfusion acquisitions are intrinsically high-dose examinations, due to repeated scanning. To keep radiation dose under control, relatively noisy images are acquired. Noise is then further enhanced during the extraction of functional parameters from the post-processing of the time attenuation curves of the voxels (TACs) and normally some smoothing filter needs to be employed to better visualize any perfusion abnormality, but sacrificing spatial resolution. In this study we propose a new method to detect perfusion abnormalities keeping both high spatial resolution and high CNR. To do this we first perform the singular value decomposition (SVD) of the original noisy spatial temporal data matrix to extract basis functions of the TACs. Then we iteratively cluster the voxels based on a smoothed version of the three most significant singular vectors. Finally, we create high spatial resolution 3D volumes where to each voxel is assigned a distance from the centroid of each cluster, showing how functionally similar each voxel is compared to the others. The method was tested on three noisy clinical datasets: one brain perfusion case with an occlusion in the left internal carotid, one healthy brain perfusion case, and one liver case with an enhancing lesion. Our method successfully detected all perfusion abnormalities with higher spatial precision when compared to the functional maps obtained with a commercially available software. We conclude this method might be employed to have a rapid qualitative indication of functional abnormalities in low dose dynamic CT perfusion datasets. The method seems to be very robust with respect to both spatial and temporal noise and does not require any special a priori assumption. While being more robust respect to noise and with higher spatial resolution and CNR when compared to the functional maps, our method is not quantitative and a potential usage in clinical routine could be as a second reader to assist in the maps evaluation, or to guide a dataset smoothing before the modeling part.

Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics.

PubMed

Thurman, Andrew L; Choi, Jiwoong; Choi, Sanghun; Lin, Ching-Long; Hoffman, Eric A; Lee, Chang Hyun; Chan, Kung-Sik

2017-05-10

Methacholine challenge tests are used to measure changes in pulmonary function that indicate symptoms of asthma. In addition to pulmonary function tests, which measure global changes in pulmonary function, computed tomography images taken at full inspiration before and after administration of methacholine provide local air volume changes (hyper-inflation post methacholine) at individual acinar units, indicating local airway hyperresponsiveness. Some of the acini may have extreme air volume changes relative to the global average, indicating hyperresponsiveness, and those extreme values may occur in clusters. We propose a Gaussian mixture model with a spatial smoothness penalty to improve prediction of hyperresponsive locations that occur in spatial clusters. A simulation study provides evidence that the spatial smoothness penalty improves prediction under different data-generating mechanisms. We apply this method to computed tomography data from Seoul National University Hospital on five healthy and ten asthmatic subjects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Adaptive nest clustering and density-dependent nest survival in dabbling ducks

USGS Publications Warehouse

Ringelman, Kevin M.; Eadie, John M.; Ackerman, Joshua T.

2014-01-01

Density-dependent population regulation is observed in many taxa, and understanding the mechanisms that generate density dependence is especially important for the conservation of heavily-managed species. In one such system, North American waterfowl, density dependence is often observed at continental scales, and nest predation has long been implicated as a key factor driving this pattern. However, despite extensive research on this topic, it remains unclear if and how nest density influences predation rates. Part of this confusion may have arisen because previous studies have studied density-dependent predation at relatively large spatial and temporal scales. Because the spatial distribution of nests changes throughout the season, which potentially influences predator behavior, nest survival may vary through time at relatively small spatial scales. As such, density-dependent nest predation might be more detectable at a spatially- and temporally-refined scale and this may provide new insights into nest site selection and predator foraging behavior. Here, we used three years of data on nest survival of two species of waterfowl, mallards and gadwall, to more fully explore the relationship between local nest clustering and nest survival. Throughout the season, we found that the distribution of nests was consistently clustered at small spatial scales (˜50–400 m), especially for mallard nests, and that this pattern was robust to yearly variation in nest density and the intensity of predation. We demonstrated further that local nest clustering had positive fitness consequences – nests with closer nearest neighbors were more likely to be successful, a result that is counter to the general assumption that nest predation rates increase with nest density.

Geospatial and age-related patterns of Taenia solium taeniasis in the rural health zone of Kimpese, Democratic Republic of Congo.

PubMed

Madinga, Joule; Kanobana, Kirezi; Lukanu, Philippe; Abatih, Emmanuel; Baloji, Sylvain; Linsuke, Sylvie; Praet, Nicolas; Kapinga, Serge; Polman, Katja; Lutumba, Pascal; Speybroeck, Niko; Dorny, Pierre; Harrison, Wendy; Gabriel, Sarah

2017-01-01

Taenia solium infections are mostly endemic in less developed countries where poor hygiene conditions and free-range pig management favor their transmission. Knowledge on patterns of infections in both human and pig is crucial to design effective control strategies. The aim of this study was to assess the prevalence, risk factors and spatial distribution of taeniasis in a rural area of the Democratic Republic of Congo (DRC), in the prospect of upcoming control activities. A cross-sectional study was conducted in 24 villages of the health zone of Kimpese, Bas Congo Province. Individual and household characteristics, including geographical coordinates were recorded. Stool samples were collected from willing participants and analyzed using the copro-antigen enzyme-linked immunosorbent assay (copro-Ag ELISA) for the detection of taeniasis. Blood samples were collected from pigs and analyzed using the B158/B60 monoclonal antibody-based antigen ELISA (sero-Ag ELISA) to detect porcine cysticercosis. Logistic regression and multilevel analysis were applied to identify risk factors. Global clustering and spatial correlation of taeniasis and porcine cysticercosis were assessed using K functions. Local clusters of both infections were identified using the Kulldorff's scan statistic. A total of 4751 participants above 5 years of age (median: 23 years; IQR: 11-41) were included. The overall proportion of taeniasis positivity was 23.4% (95% CI: 22.2-24.6), ranging from 1 to 60% between villages, with a significant between-household variance of 2.43 (SE=0.29, p<0.05). Taeniasis was significantly associated with age (p<0.05) and the highest positivity was found in the 5-10 years age group (27.0% (95% CI: 24.4-29.7)). Overall, 45.6% (95% CI: 40.2-51) of sampled pigs were sero-positive. The K functions revealed a significant overall clustering of human and pig infections but no spatial dependence between them. Two significant clusters of taeniasis (p<0.001; n=276 and n=9) and one cluster of porcine cysticercosis (p<0.001; n=24) were found. This study confirms high endemicity and geographical dispersal of taeniasis in the study area. The role of age in taeniasis patterns and significant spatial clusters of both taeniasis and porcine cysticercosis were evidenced, though no spatial correlation was found between human and pig infections. Urgent control activities are needed for this endemic area. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Monitoring of dispersed smoke-plume layers by determining locations of the data-point clusters

NASA Astrophysics Data System (ADS)

Kovalev, Vladimir; Wold, Cyle; Petkov, Alexander; Min Hao, Wei

2018-04-01

A modified data-processing technique of the signals recorded by zenith-directed lidar, which operates in smoke-polluted atmosphere, is discussed. The technique is based on simple transformations of the lidar backscatter signal and the determination of the spatial location of the data point clusters. The technique allows more reliable detection of the location of dispersed smoke layering. Examples of typical results obtained with lidar in a smokepolluted atmosphere are presented.

XMM-NEWTON OBSERVATION OF THE {alpha} PERSEI CLUSTER

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

Pillitteri, Ignazio; Evans, Nancy Remage; Wolk, Scott J.

We report on the analysis of an archival observation of part of the {alpha} Persei cluster obtained with XMM-Newton. We detected 102 X-ray sources in the band 0.3-8.0 keV, of which 39 of them are associated with the cluster as evidenced by appropriate magnitudes and colors from Two Micron All Sky Survey photometry. We extend the X-ray luminosity distribution (XLD) for M dwarfs, to add to the XLD found for hotter dwarfs from spatially extensive surveys of the whole cluster by ROSAT. Some of the hotter stars are identified as a background, possible slightly older group of stars at amore » distance of approximately 500 pc.« less

Monitoring evolving urban cluster systems using DMSP/OLS nighttime light data: a case study of the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Wang, Zhao; Yang, Shan; Wang, Shuguang; Shen, Yan

2017-10-01

The assessment of the dynamic urban structure has been affected by lack of timely and accurate spatial information for a long period, which has hindered the measurements of structural continuity at the macroscale. Defense meteorological satellite program's operational linescan system (DMSP/OLS) nighttime light (NTL) data provide an ideal source for urban information detection with a long-time span, short-time interval, and wide coverage. In this study, we extracted the physical boundaries of urban clusters from corrected NTL images and quantitatively analyzed the structure of the urban cluster system based on rank-size distribution, spatial metrics, and Mann-Kendall trend test. Two levels of urban cluster systems in the Yangtze River Delta region (YRDR) were examined. We found that (1) in the entire YRDR, the urban cluster system showed a periodic process, with a significant trend of even distribution before 2007 but an unequal growth pattern after 2007, and (2) at the metropolitan level, vast disparities exist in four metropolitan areas for the fluctuations of Pareto's exponent, the speed of cluster expansion, and the dominance of core cluster. The results suggest that the extracted urban cluster information from NTL data effectively reflect the evolving nature of regional urbanization, which in turn can aid in the planning of cities and help achieve more sustainable regional development.

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

A Web-Based Multidrug-Resistant Organisms Surveillance and Outbreak Detection System with Rule-Based Classification and Clustering

PubMed Central

Tseng, Yi-Ju; Wu, Jung-Hsuan; Ping, Xiao-Ou; Lin, Hui-Chi; Chen, Ying-Yu; Shang, Rung-Ji; Chen, Ming-Yuan; Lai, Feipei

2012-01-01

Background The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. Objectives To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. Methods Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system’s performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. Results The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). Conclusion This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates. PMID:23195868

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.

Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China

PubMed Central

Bao, Changjun; Hu, Jianli; Liu, Wendong; Liang, Qi; Wu, Ying; Norris, Jessie; Peng, Zhihang; Yu, Rongbin; Shen, Hongbing; Chen, Feng

2014-01-01

Objective This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission. Methods County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions. Results The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province. Conclusion Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time. PMID:24416167

Anomaly clustering in hyperspectral images

NASA Astrophysics Data System (ADS)

Doster, Timothy J.; Ross, David S.; Messinger, David W.; Basener, William F.

2009-05-01

The topological anomaly detection algorithm (TAD) differs from other anomaly detection algorithms in that it uses a topological/graph-theoretic model for the image background instead of modeling the image with a Gaussian normal distribution. In the construction of the model, TAD produces a hard threshold separating anomalous pixels from background in the image. We build on this feature of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. This is done by identifying, and integrating, clusters of anomalous pixels via a graph theoretical method combining spatial and spectral information. The method is applied to a cluttered HyMap image and combines small groups of pixels containing like materials, such as those corresponding to rooftops and cars, into individual clusters. This improves visualization and interpretation of objects.

The interpoint distance distribution as a descriptor of point patterns, with an application to spatial disease clustering.

PubMed

Bonetti, Marco; Pagano, Marcello

2005-03-15

The topic of this paper is the distribution of the distance between two points distributed independently in space. We illustrate the use of this interpoint distance distribution to describe the characteristics of a set of points within some fixed region. The properties of its sample version, and thus the inference about this function, are discussed both in the discrete and in the continuous setting. We illustrate its use in the detection of spatial clustering by application to a well-known leukaemia data set, and report on the results of a simulation experiment designed to study the power characteristics of the methods within that study region and in an artificial homogenous setting. Copyright (c) 2004 John Wiley & Sons, Ltd.

Detection of a pair of prominent X-ray cavities in Abell 3847

NASA Astrophysics Data System (ADS)

Vagshette, Nilkanth D.; Naik, Sachindra; Patil, Madhav. K.; Sonkamble, Satish S.

2017-04-01

We present the results obtained from a detailed analysis of a deep Chandra observation of the bright FRII radio galaxy 3C 444 in Abell 3847 cluster. A pair of huge X-ray cavities are detected along the north and south directions from the centre of 3C 444. X-ray and radio images of the cluster reveal peculiar positioning of the cavities and radio bubbles. The radio lobes and X-ray cavities are apparently not spatially coincident and exhibit offsets by ˜61 and 77 kpc from each other along the north and south directions, respectively. Radial temperature and density profiles reveal the presence of a cool core in the cluster. Imaging and spectral studies showed the removal of substantial amount of matter from the core of the cluster by the radio jets. A detailed analysis of the temperature and density profiles showed the presence of a rarely detected elliptical shock in the cluster. Detection of inflating cavities at an average distance of ˜55 kpc from the centre implies that the central engine feeds a remarkable amount of radio power (˜6.3 × 1044 erg s-1) into the intra-cluster medium over ˜108 yr, the estimated age of cavity. The cooling luminosity of the cluster was estimated to be ˜8.30 × 1043 erg s-1 , which confirms that the AGN power is sufficient to quench the cooling. Ratios of mass accretion rate to Eddington and Bondi rates were estimated to be ˜0.08 and 3.5 × 104, respectively. This indicates that the black hole in the core of the cluster accretes matter through chaotic cold accretion.

Performance of cancer cluster Q-statistics for case-control residential histories

PubMed Central

Sloan, Chantel D.; Jacquez, Geoffrey M.; Gallagher, Carolyn M.; Ward, Mary H.; Raaschou-Nielsen, Ole; Nordsborg, Rikke Baastrup; Meliker, Jaymie R.

2012-01-01

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Qi, with cases who are constituents of significant local clusters at given times, Qit, yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf’s spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings. PMID:23149326

Spatially explicit population estimates for black bears based on cluster sampling

USGS Publications Warehouse

Humm, J.; McCown, J. Walter; Scheick, B.K.; Clark, Joseph D.

2017-01-01

We estimated abundance and density of the 5 major black bear (Ursus americanus) subpopulations (i.e., Eglin, Apalachicola, Osceola, Ocala-St. Johns, Big Cypress) in Florida, USA with spatially explicit capture-mark-recapture (SCR) by extracting DNA from hair samples collected at barbed-wire hair sampling sites. We employed a clustered sampling configuration with sampling sites arranged in 3 × 3 clusters spaced 2 km apart within each cluster and cluster centers spaced 16 km apart (center to center). We surveyed all 5 subpopulations encompassing 38,960 km2 during 2014 and 2015. Several landscape variables, most associated with forest cover, helped refine density estimates for the 5 subpopulations we sampled. Detection probabilities were affected by site-specific behavioral responses coupled with individual capture heterogeneity associated with sex. Model-averaged bear population estimates ranged from 120 (95% CI = 59–276) bears or a mean 0.025 bears/km2 (95% CI = 0.011–0.44) for the Eglin subpopulation to 1,198 bears (95% CI = 949–1,537) or 0.127 bears/km2 (95% CI = 0.101–0.163) for the Ocala-St. Johns subpopulation. The total population estimate for our 5 study areas was 3,916 bears (95% CI = 2,914–5,451). The clustered sampling method coupled with information on land cover was efficient and allowed us to estimate abundance across extensive areas that would not have been possible otherwise. Clustered sampling combined with spatially explicit capture-recapture methods has the potential to provide rigorous population estimates for a wide array of species that are extensive and heterogeneous in their distribution.

Clustered localization of STAT3 during the cell cycle detected by super-resolution fluorescence microscopy

NASA Astrophysics Data System (ADS)

Gao, Jing; Chen, Junling; Cai, Mingjun; Xu, Haijiao; Jiang, Junguang; Tong, Ti; Wang, Hongda

2017-06-01

Signal transducer and activator of transcription 3 (STAT3) plays a key role in various cellular processes such as cell proliferation, differentiation, apoptosis and immune responses. In particular, STAT3 has emerged as a potential molecular target for cancer therapy. The functional role and standard activation mechanism of STAT3 have been well studied, however, the spatial distribution of STAT3 during the cell cycle is poorly known. Therefore, it is indispensable to study STAT3 spatial arrangement and nuclear-cytoplasimic localization at the different phase of cell cycle in cancer cells. By direct stochastic optical reconstruction microscopy imaging, we find that STAT3 forms various number and size of clusters at the different cell-cycle stage, which could not be clearly observed by conventional fluorescent microscopy. STAT3 clusters get more and larger gradually from G1 to G2 phase, during which time transcription and other related activities goes on consistently. The results suggest that there is an intimate relationship between the clustered characteristic of STAT3 and the cell-cycle behavior. Meanwhile, clustering would facilitate STAT3 rapid response to activating signals due to short distances between molecules. Our data might open a new door to develop an antitumor drug for inhibiting STAT3 signaling pathway by destroying its clusters.

Metal concentration and X-ray cool spectral component in the central region of the Centaurus cluster of galaxies

NASA Technical Reports Server (NTRS)

Fukazawa, Yasushi; Ohashi, Takaya; Fabian, Andrew C.; Canizares, Claude R.; Ikebe, Yasushi; Makishima, Kazuo; Mushotzky, Richard F.; Yamashita, Koujun

1994-01-01

Spatially resolved energy spectra in the energy range 0.5-10 keV have been measured for the Centaurus cluster of galaxies with Advanced Satellite for Cosmology and Astrophysics (ASCA). Within 10 min (200 kpc) from the cluster center, the helium-like iron K emission line exhibits a dramatic increase toward the center rising from an equivalent width approximately 500 eV to approximately 1500 eV corresponding to an abundance change from 0.3 to 1.0 solar. The presence of strong iron L lines indicates an additional cool component (kT approximately 1 keV) within 10 min from the center. The cool component requires absorption in excess of the galactic value and this excess absorption increases towards the central region of the cluster. In the surrounding region with radius greater than 10 min, the spectra are well described by a single temperature thermal model with kT approximately 4 keV and spatially uniform abundances at about 0.3-0.4 times solar. The detection of metal-rich hot and cool gas in the cluster center implies a complex nature of the central cluster gas which is likely to be related to the presence of the central cD galaxy NGC 4696.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS*

PubMed Central

Keller, Joshua P.; Drton, Mathias; Larson, Timothy; Kaufman, Joel D.; Sandler, Dale P.; Szpiro, Adam A.

2017-01-01

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP. PMID:28572869

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.

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 system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

A speeded-up saliency region-based contrast detection method for small targets

NASA Astrophysics Data System (ADS)

Li, Zhengjie; Zhang, Haiying; Bai, Jiaojiao; Zhou, Zhongjun; Zheng, Huihuang

2018-04-01

To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain "integrity" property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of "clustering segmentation", the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

Fascioliasis risk factors and space-time clusters in domestic ruminants in Bangladesh.

PubMed

Rahman, A K M Anisur; Islam, S K Shaheenur; Talukder, Md Hasanuzzaman; Hassan, Md Kumrul; Dhand, Navneet K; Ward, Michael P

2017-05-08

A retrospective observational study was conducted to identify fascioliasis hotspots, clusters, potential risk factors and to map fascioliasis risk in domestic ruminants in Bangladesh. Cases of fascioliasis in cattle, buffalo, sheep and goats from all districts in Bangladesh between 2011 and 2013 were identified via secondary surveillance data from the Department of Livestock Services' Epidemiology Unit. From each case report, date of report, species affected and district data were extracted. The total number of domestic ruminants in each district was used to calculate fascioliasis cases per ten thousand animals at risk per district, and this was used for cluster and hotspot analysis. Clustering was assessed with Moran's spatial autocorrelation statistic, hotspots with the local indicator of spatial association (LISA) statistic and space-time clusters with the scan statistic (Poisson model). The association between district fascioliasis prevalence and climate (temperature, precipitation), elevation, land cover and water bodies was investigated using a spatial regression model. A total of 1,723,971 cases of fascioliasis were reported in the three-year study period in cattle (1,164,560), goats (424,314), buffalo (88,924) and sheep (46,173). A total of nine hotspots were identified; one of these persisted in each of the three years. Only two local clusters were found. Five space-time clusters located within 22 districts were also identified. Annual risk maps of fascioliasis cases correlated with the hotspots and clusters detected. Cultivated and managed (P < 0.001) and artificial surface (P = 0.04) land cover areas, and elevation (P = 0.003) were positively and negatively associated with fascioliasis in Bangladesh, respectively. Results indicate that due to land use characteristics some areas of Bangladesh are at greater risk of fascioliasis. The potential risk factors, hot spots and clusters identified in this study can be used to guide science-based treatment and control decisions for fascioliasis in Bangladesh and in other similar geo-climatic zones throughout the world.

The Geography of Mental Health and General Wellness in Galveston Bay After Hurricane Ike: A Spatial Epidemiologic Study With Longitudinal Data.

PubMed

Gruebner, Oliver; Lowe, Sarah R; Tracy, Melissa; Cerdá, Magdalena; Joshi, Spruha; Norris, Fran H; Galea, Sandro

2016-04-01

To demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness. We used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression. We found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island. Predictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities.

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

Cluster analysis of fasciolosis in dairy cow herds in Munster province of Ireland and detection of major climatic and environmental predictors of the exposure risk.

PubMed

Selemetas, Nikolaos; Phelan, Paul; O'Kiely, Padraig; de Waal, Theo

2015-03-19

Fasciolosis caused by Fasciola hepatica is a widespread parasitic disease in cattle farms. The aim of this study was to detect clusters of fasciolosis in dairy cow herds in Munster Province, Ireland and to identify significant climatic and environmental predictors of the exposure risk. In total, 1,292 dairy herds across Munster was sampled in September 2012 providing a single bulk tank milk (BTM) sample. The analysis of samples by an in-house antibody-detection enzyme-linked immunosorbent assay (ELISA), showed that 65% of the dairy herds (n = 842) had been exposed to F. hepatica. Using the Getis-Ord Gi* statistic, 16 high-risk and 24 low-risk (P <0.01) clusters of fasciolosis were identified. The spatial distribution of high-risk clusters was more dispersed and mainly located in the northern and western regions of Munster compared to the low-risk clusters that were mostly concentrated in the southern and eastern regions. The most significant classes of variables that could reflect the difference between high-risk and low-risk clusters were the total number of wet-days and rain-days, rainfall, the normalized difference vegetation index (NDVI), temperature and soil type. There was a bigger proportion of well-drained soils among the low-risk clusters, whereas poorly drained soils were more common among the high-risk clusters. These results stress the role of precipitation, grazing, temperature and drainage on the life cycle of F. hepatica in the temperate Irish climate. The findings of this study highlight the importance of cluster analysis for identifying significant differences in climatic and environmental variables between high-risk and low-risk clusters of fasciolosis in Irish dairy herds.

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.

The Atacama Cosmology Telescope: High-Resolution Sunyaev-Zel'dovich Array Observations of ACT SZE-Selected Clusters from the Equatorial Strip

NASA Technical Reports Server (NTRS)

Reese, Erik D.; Mroczkowski, Tony; Menanteau, Felipe; Hilton, Matt; Sievers, Jonathan; Aguirre, Paula; Appel, John William; Baker, Andrew J.; Bond, J. Richard; Das, Sudeep;

The Atacama Cosmology Telescope: High-Resolution Sunyaev-Zeldovich Array Observations of ACT SZE-Selected Clusters from the Equatorial Strip

NASA Technical Reports Server (NTRS)

Reese, Erik; Mroczkowski, Tony; Menateau, Felipe; Hilton, Matt; Sievers, Jonathan; Aguirre, Paula; Appel, John William; Baker, Andrew J.; Bond, J. Richard; Das, Sudeep;

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

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.

Tigers on trails: occupancy modeling for cluster sampling.

PubMed

Hines, J E; Nichols, J D; Royle, J A; MacKenzie, D I; Gopalaswamy, A M; Kumar, N Samba; Karanth, K U

2010-07-01

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy estimation in conservation monitoring. More generally, this work represents a contribution to the topic of cluster sampling for situations in which there is a need for specific modeling (e.g., reflecting dependence) for the distribution of the variable(s) of interest among subunits.

First Hard X-Ray Detection of the Non-Thermal Emission Around the Arches Cluster: Morphology and Spectral Studies With NuSTAR

NASA Technical Reports Server (NTRS)

Krivonos, Roman A.; Tomsick, John A.; Bauer, Franz E.; Baganoff, Frederick K.; Barriere, Nicolas M.; Bodaghee, Arash; Boggs, Steven E.; Christensen, Finn E.; Craig, William W.; Grefenstette, Brian W.;

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.

PubMed

Park, Rachel; O'Brien, Thomas F; Huang, Susan S; Baker, Meghan A; Yokoe, Deborah S; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-11-01

While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.

PubMed

Torheim, Turid; Groendahl, Aurora R; Andersen, Erlend K F; Lyng, Heidi; Malinen, Eirik; Kvaal, Knut; Futsaether, Cecilia M

2016-11-01

Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach. Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters K trans and ν e ) and the Brix model (A Brix , k ep and k el ). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster. One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome. We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.

Asymmetric ejecta of cool supergiants and hypergiants in the massive cluster Westerlund 1

NASA Astrophysics Data System (ADS)

Andrews, H.; Fenech, D.; Prinja, R. K.; Clark, J. S.; Hindson, L.

2018-06-01

We report new 5.5 GHz radio observations of the massive star cluster Westerlund 1, taken by the Australia Telescope Compact Array, detecting nine of the ten yellow hypergiants (YHGs) and red supergiants (RSGs) within the cluster. Eight of nine sources are spatially resolved. The nebulae associated with the YHGs Wd1-4a, -12a, and -265 demonstrate a cometary morphology - the first time this phenomenon has been observed for such stars. This structure is also echoed in the ejecta of the RSGs Wd1-20 and -26; in each case the cometary tails are directed away from the cluster core. The nebular emission around the RSG Wd1-237 is less collimated than these systems but once again appears more prominent in the hemisphere facing the cluster. Considered as a whole, the nebular morphologies provide compelling evidence for sculpting via a physical agent associated with Westerlund 1, such as a cluster wind.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).

PubMed

Baker, David M; Valleron, Alain-Jacques

2014-10-30

Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose. Geographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area. We describe how data is projected onto such a grid, as well as grid-based algorithms for spatial epidemiological data-mining. The software program (FGBASE), that we have developed, implements these grid-based methods. The grid based algorithms perform extremely fast. This is particularly the case for cluster searches. When applied to a cohort of French Type 1 Diabetes (T1D) patients, as an example, the grid based algorithms detected potential clusters in a few seconds on a modern laptop. This compares very favorably to an equivalent cluster search using distance calculations instead of a grid, which took over 4 hours on the same computer. In the case study we discovered 4 potential clusters of T1D cases near the cities of Le Havre, Dunkerque, Toulouse and Nantes. One example of environmental analysis with our software was to study whether a significant association could be found between distance to vineyards with heavy pesticide. None was found. In both examples, the software facilitates the rapid testing of hypotheses. Grid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity thus improving the speed of computations. We believe that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.

Correlation analysis of fracture arrangement in space

NASA Astrophysics Data System (ADS)

Marrett, Randall; Gale, Julia F. W.; Gómez, Leonel A.; Laubach, Stephen E.

2018-03-01

We present new techniques that overcome limitations of standard approaches to documenting spatial arrangement. The new techniques directly quantify spatial arrangement by normalizing to expected values for randomly arranged fractures. The techniques differ in terms of computational intensity, robustness of results, ability to detect anti-correlation, and use of fracture size data. Variation of spatial arrangement across a broad range of length scales facilitates distinguishing clustered and periodic arrangements-opposite forms of organization-from random arrangements. Moreover, self-organized arrangements can be distinguished from arrangements due to extrinsic organization. Traditional techniques for analysis of fracture spacing are hamstrung because they account neither for the sequence of fracture spacings nor for possible coordination between fracture size and position, attributes accounted for by our methods. All of the new techniques reveal fractal clustering in a test case of veins, or cement-filled opening-mode fractures, in Pennsylvanian Marble Falls Limestone. The observed arrangement is readily distinguishable from random and periodic arrangements. Comparison of results that account for fracture size with results that ignore fracture size demonstrates that spatial arrangement is dominated by the sequence of fracture spacings, rather than coordination of fracture size with position. Fracture size and position are not completely independent in this example, however, because large fractures are more clustered than small fractures. Both spatial and size organization of veins here probably emerged from fracture interaction during growth. The new approaches described here, along with freely available software to implement the techniques, can be applied with effect to a wide range of structures, or indeed many other phenomena such as drilling response, where spatial heterogeneity is an issue.

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.

Descriptive analysis and spatial epidemiology of porcine reproductive and respiratory syndrome (PRRS) for swine sites participating in area regional control and elimination programs from 3 regions of Ontario

PubMed Central

Arruda, Andreia G.; Poljak, Zvonimir; Friendship, Robert; Carpenter, Jane; Hand, Karen

2015-01-01

The objectives of this study were to describe demographics, basic biosecurity practices, ownership structure, and prevalence of porcine reproductive and respiratory syndrome (PRRS) in swine sites located in 3 regions in Ontario, and investigate the presence of spatial clustering and clusters of PRRS positive sites in the 3 regions. A total of 370 swine sites were enrolled in Area Regional Control and Elimination projects in Niagara, Watford, and Perth from 2010 to 2013. Demographics, biosecurity, and site ownership data were collected using a standardized questionnaire and site locations were obtained from an industry organization. Status was assigned on the basis of available diagnostic tests and/or assessment by site veterinarians. Spatial dependence was investigated using the D-function, the spatial scan statistic test and the spatial relative risk method. Results showed that the use of strict all-in all-out (AIAO) pig flow and shower before entry are uncommon biosecurity practices in swine sites, but a larger proportion of sites reported having a Danish entry. The prevalence of PRRS in the 3 regions ranged from 17% to 48% and localized high and low risk clusters were detected. Sites enrolled in the PRRS control projects were characterized by membership in multiple and overlapping ownership structures and networks, which complicates the way the results of monitoring and disease management measures are communicated to the target population. PMID:26424906

Spatial clustering of Borrelia burgdorferi sensu lato within populations of Allen's chipmunks and dusky-footed woodrats in northwestern California

PubMed Central

Brown, Richard N.; Fedorova, Natalia; Girard, Yvette A.; Higley, Mark; Clueit, Bernadette; Lane, Robert S.

2018-01-01

The ecology of Lyme borreliosis is complex in northwestern California, with several potential reservoir hosts, tick vectors, and genospecies of Borrelia burgdorferi sensu lato. The primary objective of this study was to determine the fine-scale spatial distribution of different genospecies in four rodent species, the California ground squirrel (Otospermophilus beecheyi), northern flying squirrel (Glaucomys sabrinus), dusky-footed woodrat (Neotoma fuscipes), and Allen’s chipmunk (Neotamias senex). Rodents were live-trapped between June 2004 and May 2005 at the Hoopa Valley Tribal Reservation (HVTR) in Humboldt County, California. Ear-punch biopsies obtained from each rodent were tested by polymerase chain reaction (PCR) and sequencing analysis. The programs ArcGIS and SaTScan were used to examine the spatial distribution of genospecies. Multinomial log-linear models were used to model habitat and host-specific characteristics and their effect on the presence of each borrelial genospecies. The Akaike information criterion (AICc) was used to compare models and determine model fit. Borrelia burgdorferi sensu stricto was primarily associated with chipmunks and B. bissettiae largely with woodrats. The top model included the variables “host species”, “month”, and “elevation” (weight = 0.84). Spatial clustering of B. bissettiae was detected in the northwestern section of the HVTR, whereas B. burgdorferi sensu stricto was clustered in the southeastern section. We conclude that the spatial distribution of these borreliae are driven at least in part by host species, time-of-year, and elevation. PMID:29634745

Spatial clustering of Borrelia burgdorferi sensu lato within populations of Allen's chipmunks and dusky-footed woodrats in northwestern California.

PubMed

Hacker, Gregory M; Brown, Richard N; Fedorova, Natalia; Girard, Yvette A; Higley, Mark; Clueit, Bernadette; Lane, Robert S

2018-01-01

The ecology of Lyme borreliosis is complex in northwestern California, with several potential reservoir hosts, tick vectors, and genospecies of Borrelia burgdorferi sensu lato. The primary objective of this study was to determine the fine-scale spatial distribution of different genospecies in four rodent species, the California ground squirrel (Otospermophilus beecheyi), northern flying squirrel (Glaucomys sabrinus), dusky-footed woodrat (Neotoma fuscipes), and Allen's chipmunk (Neotamias senex). Rodents were live-trapped between June 2004 and May 2005 at the Hoopa Valley Tribal Reservation (HVTR) in Humboldt County, California. Ear-punch biopsies obtained from each rodent were tested by polymerase chain reaction (PCR) and sequencing analysis. The programs ArcGIS and SaTScan were used to examine the spatial distribution of genospecies. Multinomial log-linear models were used to model habitat and host-specific characteristics and their effect on the presence of each borrelial genospecies. The Akaike information criterion (AICc) was used to compare models and determine model fit. Borrelia burgdorferi sensu stricto was primarily associated with chipmunks and B. bissettiae largely with woodrats. The top model included the variables "host species", "month", and "elevation" (weight = 0.84). Spatial clustering of B. bissettiae was detected in the northwestern section of the HVTR, whereas B. burgdorferi sensu stricto was clustered in the southeastern section. We conclude that the spatial distribution of these borreliae are driven at least in part by host species, time-of-year, and elevation.

Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003-2012.

PubMed

Khan, Diba; Rossen, Lauren M; Hamilton, Brady E; He, Yulei; Wei, Rong; Dienes, Erin

2017-06-01

Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. Published by Elsevier Ltd.

Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012

PubMed Central

Khan, Diba; Rossen, Lauren M.; Hamilton, Brady E.; He, Yulei; Wei, Rong; Dienes, Erin

2017-01-01

Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003–2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. PMID:28552189

Measuring spatially varying, multispectral, ultraviolet bidirectional reflectance distribution function with an imaging spectrometer

NASA Astrophysics Data System (ADS)

Li, Hongsong; Lyu, Hang; Liao, Ningfang; Wu, Wenmin

2016-12-01

The bidirectional reflectance distribution function (BRDF) data in the ultraviolet (UV) band are valuable for many applications including cultural heritage, material analysis, surface characterization, and trace detection. We present a BRDF measurement instrument working in the near- and middle-UV spectral range. The instrument includes a collimated UV light source, a rotation stage, a UV imaging spectrometer, and a control computer. The data captured by the proposed instrument describe spatial, spectral, and angular variations of the light scattering from a sample surface. Such a multidimensional dataset of an example sample is captured by the proposed instrument and analyzed by a k-mean clustering algorithm to separate surface regions with same material but different surface roughnesses. The clustering results show that the angular dimension of the dataset can be exploited for surface roughness characterization. The two clustered BRDFs are fitted to a theoretical BRDF model. The fitting results show good agreement between the measurement data and the theoretical model.

Spatial correlation analysis of urban traffic state under a perspective of community detection

NASA Astrophysics Data System (ADS)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Application of adaptive cluster sampling to low-density populations of freshwater mussels

USGS Publications Warehouse

Smith, D.R.; Villella, R.F.; Lemarie, D.P.

2003-01-01

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.

Segmentation and clustering as complementary sources of information

NASA Astrophysics Data System (ADS)

Dale, Michael B.; Allison, Lloyd; Dale, Patricia E. R.

2007-03-01

This paper examines the effects of using a segmentation method to identify change-points or edges in vegetation. It identifies coherence (spatial or temporal) in place of unconstrained clustering. The segmentation method involves change-point detection along a sequence of observations so that each cluster formed is composed of adjacent samples; this is a form of constrained clustering. The protocol identifies one or more models, one for each section identified, and the quality of each is assessed using a minimum message length criterion, which provides a rational basis for selecting an appropriate model. Although the segmentation is less efficient than clustering, it does provide other information because it incorporates textural similarity as well as homogeneity. In addition it can be useful in determining various scales of variation that may apply to the data, providing a general method of small-scale pattern analysis.

Alcohol outlets and clusters of violence

PubMed Central

2011-01-01

Background Alcohol related violence continues to be a major public health problem in the United States. In particular, there is substantial evidence of an association between alcohol outlets and assault. However, because the specific geographic relationships between alcohol outlets and the distribution of violence remains obscured, it is important to identify the spatial linkages that may exist, enhancing public health efforts to curb both violence and morbidity. Methods The present study utilizes police-recorded data on simple and aggravated assaults in Cincinnati, Ohio. Addresses of alcohol outlets for Cincinnati, including all bars, alcohol-serving restaurants, and off-premise liquor and convenience stores were obtained from the Ohio Division of Liquor Control and geocoded for analysis. A combination of proximity analysis, spatial cluster detection approaches and a geographic information system were used to identify clusters of alcohol outlets and the distribution of violence around them. Results A brief review of the empirical work relating to alcohol outlet density and violence is provided, noting that the majority of this literature is cross-sectional and ecological in nature, yielding a somewhat haphazard and aggregate view of how outlet type(s) and neighborhood characteristics like social organization and land use are related to assaultive violence. The results of the statistical analysis for Cincinnati suggest that while alcohol outlets are not problematic per se, assaultive violence has a propensity to cluster around agglomerations of alcohol outlets. This spatial relationship varies by distance and is also related to the characteristics of the alcohol outlet agglomeration. Specifically, spatially dense distributions of outlets appear to be more prone to clusters of assaultive violence when compared to agglomerations with a lower density of outlets. Conclusion With a more thorough understanding of the spatial relationships between alcohol outlets and the distribution of assaults, policymakers in urban areas can make more informed regulatory decisions regarding alcohol licenses. Further, this research suggests that public health officials and epidemiologists need to develop a better understanding of what actually occurs in and around alcohol outlets, determining what factors (whether outlet, neighborhood, or spatially related) help fuel their relationship with violence and other alcohol-related harm. PMID:21542932

Understanding the detection of carbon in austenitic high-Mn steel using atom probe tomography.

PubMed

Marceau, R K W; Choi, P; Raabe, D

2013-09-01

A high-Mn TWIP steel having composition Fe-22Mn-0.6C (wt%) is considered in this study, where the need for accurate and quantitative analysis of clustering and short-range ordering by atom probe analysis requires a better understanding of the detection of carbon in this system. Experimental measurements reveal that a high percentage of carbon atoms are detected as molecular ion species and on multiple hit events, which is discussed with respect to issues such as optimal experimental parameters, correlated field evaporation and directional walk/migration of carbon atoms at the surface of the specimen tip during analysis. These phenomena impact the compositional and spatial accuracy of the atom probe measurement and thus require careful consideration for further cluster-finding analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

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.

The Distribution and Ages of Star Clusters in the Small Magellanic Cloud: Constraints on the Interaction History of the Magellanic Clouds

NASA Astrophysics Data System (ADS)

Bitsakis, Theodoros; González-Lópezlira, R. A.; Bonfini, P.; Bruzual, G.; Maravelias, G.; Zaritsky, D.; Charlot, S.; Ramírez-Siordia, V. H.

2018-02-01

We present a new study of the spatial distribution and ages of the star clusters in the Small Magellanic Cloud (SMC). To detect and estimate the ages of the star clusters we rely on the new fully automated method developed by Bitsakis et al. Our code detects 1319 star clusters in the central 18 deg2 of the SMC we surveyed (1108 of which have never been reported before). The age distribution of those clusters suggests enhanced cluster formation around 240 Myr ago. It also implies significant differences in the cluster distribution of the bar with respect to the rest of the galaxy, with the younger clusters being predominantly located in the bar. Having used the same setup, and data from the same surveys as for our previous study of the LMC, we are able to robustly compare the cluster properties between the two galaxies. Our results suggest that the bulk of the clusters in both galaxies were formed approximately 300 Myr ago, probably during a direct collision between the two galaxies. On the other hand, the locations of the young (≤50 Myr) clusters in both Magellanic Clouds, found where their bars join the H I arms, suggest that cluster formation in those regions is a result of internal dynamical processes. Finally, we discuss the potential causes of the apparent outside-in quenching of cluster formation that we observe in the SMC. Our findings are consistent with an evolutionary scheme where the interactions between the Magellanic Clouds constitute the major mechanism driving their overall evolution.

ALMA Pinpoints a Strong Overdensity of U/LIRGs in the Massive Cluster XCS J2215 at z = 1.46

NASA Astrophysics Data System (ADS)

Stach, Stuart M.; Swinbank, A. M.; Smail, Ian; Hilton, Matt; Simpson, J. M.; Cooke, E. A.

2017-11-01

We surveyed the core regions of the z = 1.46 cluster XCS J2215.9-1738 with the Atacama Large Millimeter Array (ALMA) and the MUSE-GALACSI spectrograph on the Very Large Telescope (VLT). We obtained high spatial resolution observations with ALMA of the 1.2 mm dust continuum and molecular gas emission in the central regions of the cluster. These observations detect 14 significant millimeter sources in a region with a projected diameter of just ˜500 kpc (˜1‧). For six of these galaxies, we also obtain 12CO(2-1) and 12CO(5-4) line detections, confirming them as cluster members, and a further five of our millimeter galaxies have archival 12CO(2-1) detections, which also place them in the cluster. An additional two millimeter galaxies have photometric redshifts consistent with cluster membership, although neither show strong line emission in the MUSE spectra. This suggests that the bulk (≥11/14, ˜80%) of the submillimeter sources in the field are in fact luminous infrared galaxies lying within this young cluster. We then use our sensitive new observations to constrain the dust-obscured star formation activity and cold molecular gas within this cluster. We find hints that the cooler dust and gas components within these galaxies may have been influenced by their environment, reducing the gas reservoir available for their subsequent star formation. We also find that these actively star-forming galaxies have dynamical masses and stellar population ages expected for the progenitors of massive, early-type galaxies in local clusters, potentially linking these populations.

Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic, demographic and household factors in northwest Ethiopia.

PubMed

Alene, Kefyalew Addis; Viney, Kerri; McBryde, Emma S; Clements, Archie C A

2017-01-01

Understanding the geographical distribution of multidrug-resistant tuberculosis (MDR-TB) in high TB burden countries such as Ethiopia is crucial for effective control of TB epidemics in these countries, and thus globally. We present the first spatial analysis of multidrug resistant tuberculosis, and its relationship to socio-economic, demographic and household factors in northwest Ethiopia. An ecological study was conducted using data on patients diagnosed with MDR-TB at the University of Gondar Hospital MDR-TB treatment centre, for the period 2010 to 2015. District level population data were extracted from the Ethiopia National and Regional Census Report. Spatial autocorrelation was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate Poisson regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo (MCMC) simulation approach with Gibbs sampling, in WinBUGS. A total of 264 MDR-TB patients were included in the analysis. The overall crude incidence rate of MDR-TB for the six-year period was 3.0 cases per 100,000 population. The highest incidence rate was observed in Metema (21 cases per 100,000 population) and Humera (18 cases per 100,000 population) districts; whereas nine districts had zero cases. Spatial clustering of MDR-TB was observed in districts located in the Ethiopia-Sudan and Ethiopia-Eritrea border regions, where large numbers of seasonal migrants live. Spatial clustering of MDR-TB was positively associated with urbanization (RR: 1.02; 95%CI: 1.01, 1.04) and the percentage of men (RR: 1.58; 95% CI: 1.26, 1.99) in the districts; after accounting for these factors there was no residual spatial clustering. Spatial clustering of MDR-TB, fully explained by demographic factors (urbanization and percent male), was detected in the border regions of northwest Ethiopia, in locations where seasonal migrants live and work. Cross-border initiatives including options for mobile TB treatment and follow up are important for the effective control of MDR-TB in the region.

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

Evolution of the BCG in Disturbed Galaxy Clusters

NASA Astrophysics Data System (ADS)

Ardila, Felipe; Strauss, Michael A.; Lauer, Tod R.; Postman, Marc

2017-01-01

The present paradigm in cosmology tells us that large-scale structures grow hierarchically. This suggests that galaxy clusters grow by accreting mass and merging with other clusters, a process which should be detectable by the presence of substructure within a cluster. Using the Dressler-Shectman (DS) three-dimensional test for dynamical substructure, we determined which clusters showed evidence for disturbance from a set of 227 Abell clusters from Lauer et al. (2014) with at least 50 member galaxies and spectroscopic redshifts, z < 0.08. Our results show that 155 (68.2%) of the clusters showed evidence for substructure at ≥ 95% confidence, while 72 did not. Kolmogorov-Smirnov tests suggest that the two populations of clusters (those with and without detected substructure) are significantly different in their distributions of BCG luminosities (Lm), but not in their BCG stellar velocity dispersions (σ), their BCG spatial offsets from the x-ray centers of the clusters, their BCG velocity offsets from the mean cluster velocity, the logarithmic slopes of their BCG photometric curves of growth (α), their cluster velocity dispersions, or their luminosity differences between the BCG and the second-ranked galaxy in the cluster (M2). Similarly, no significant difference was found in the fitting of the Lm-α-σ metric plane for BCGs of clusters with substructure compared those in which there is not substructure. This is surprising since our hierarchical growth models suggest that some of these BCG/cluster properties would be affected by a disturbance of the cluster, indicating that our understanding of how BCGs evolve with their clusters is incomplete and we should explore other ways to probe the level of disturbance.

Spatial-temporal pattern and risk factor analysis of bacillary dysentery in the Beijing-Tianjin-Tangshan urban region of China.

PubMed

Xiao, Gexin; Xu, Chengdong; Wang, Jinfeng; Yang, Dongyang; Wang, Li

2014-09-25

Bacillary dysentery remains a major public health concern in China. The Beijing-Tianjin-Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region. Bacillary dysentery case data from January 2011 to December 2011 in Beijing-Tianjin-Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors. There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <5 years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence. Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing-Tianjin-Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

NASA Astrophysics Data System (ADS)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

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

Spatio-temporal clustering of wildfires in Portugal

NASA Astrophysics Data System (ADS)

Costa, R.; Pereira, M. G.; Caramelo, L.; Vega Orozco, C.; Kanevski, M.

2012-04-01

Several studies have shown that wildfires in Portugal presenthigh temporal as well as high spatial variability (Pereira et al., 2005, 2011). The identification and characterization of spatio-temporal clusters contributes to a comprehensivecharacterization of the fire regime and to improve the efficiency of fire prevention and combat activities. The main goalsin this studyare: (i) to detect the spatio-temporal clusters of burned area; and, (ii) to characterize these clusters along with the role of human and environmental factors. The data were supplied by the National Forest Authority(AFN, 2011) and comprises: (a)the Portuguese Rural Fire Database, PRFD, (Pereira et al., 2011) for the 1980-2007period; and, (b) the national mapping burned areas between 1990 and 2009. In this work, in order to complement the more common cluster analysis algorithms, an alternative approach based onscan statistics and on the permutation modelwas used. This statistical methodallows the detection of local excess events and to test if such an excess can reasonably have occurred by chance.Results obtained for different simulations performed for different spatial and temporal windows are presented, compared and interpreted.The influence of several fire factors such as (climate, vegetation type, etc.) is also assessed. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005:"Synoptic patterns associated with large summer forest fires in Portugal".Agricultural and Forest Meteorology. 129, 11-25. 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 AFN, 2011: AutoridadeFlorestalNacional (National Forest Authority). Available at http://www.afn.min-agricultura.pt/portal.

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

Use of Spatial Epidemiology and Hot Spot Analysis to Target Women Eligible for Prenatal Women, Infants, and Children Services

PubMed Central

Krawczyk, Christopher; Gradziel, Pat; Geraghty, Estella M.

2014-01-01

Objectives. We used a geographic information system and cluster analyses to determine locations in need of enhanced Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Program services. Methods. We linked documented births in the 2010 California Birth Statistical Master File with the 2010 data from the WIC Integrated Statewide Information System. Analyses focused on the density of pregnant women who were eligible for but not receiving WIC services in California’s 7049 census tracts. We used incremental spatial autocorrelation and hot spot analyses to identify clusters of WIC-eligible nonparticipants. Results. We detected clusters of census tracts with higher-than-expected densities, compared with the state mean density of WIC-eligible nonparticipants, in 21 of 58 (36.2%) California counties (P < .05). In subsequent county-level analyses, we located neighborhood-level clusters of higher-than-expected densities of eligible nonparticipants in Sacramento, San Francisco, Fresno, and Los Angeles Counties (P < .05). Conclusions. Hot spot analyses provided a rigorous and objective approach to determine the locations of statistically significant clusters of WIC-eligible nonparticipants. Results helped inform WIC program and funding decisions, including the opening of new WIC centers, and offered a novel approach for targeting public health services. PMID:24354821

Organ-Level Analysis of Idioblast Patterning in Egeria densa Planch. Leaves

PubMed Central

Hara, Takuya; Kobayashi, Emi; Ohtsubo, Kohei; Kumada, Shogo; Kanazawa, Mikako; Abe, Tomoko; Itoh, Ryuuichi D.; Fujiwara, Makoto T.

2015-01-01

Leaf tissues of plants usually contain several types of idioblasts, defined as specialized cells whose shape and contents differ from the surrounding homogeneous cells. The spatial patterning of idioblasts, particularly of trichomes and guard cells, across the leaf epidermis has received considerable attention as it offers a useful biological model for studying the intercellular regulation of cell fate and patterning. Excretory idioblasts in the leaves of the aquatic monocotyledonous plant Egeria densa produced light blue autofluorescence when irradiated with ultraviolet light. The use of epifluorescence microscopy to detect this autofluorescence provided a simple and convenient method for detecting excretory idioblasts and allowed tracking of those cells across the leaf surfaces, enabling quantitative measurement of the clustering and spacing patterns of idioblasts at the whole leaf level. Occurrence of idioblasts was coordinated along the proximal–distal, medial–lateral, and adaxial–abaxial axes, producing a recognizable consensus spatial pattern of idioblast formation among fully expanded leaves. Idioblast clusters, which comprised up to nine cells aligned along the proximal–distal axis, showed no positional bias or regularity in idioblast-forming areas when compared with singlet idioblasts. Up to 75% of idioblasts existed as clusters on every leaf side examined. The idioblast-forming areas varied between leaves, implying phenotypic plasticity. Furthermore, in young expanding leaves, autofluorescence was occasionally detected in a single giant vesicle or else in one or more small vesicles, which eventually grew to occupy a large portion of the idioblast volume as a central vacuole. Differentiation of vacuoles by accumulating the fluorescence substance might be an integral part of idioblast differentiation. Red autofluorescence from chloroplasts was not detected in idioblasts of young expanding leaves, suggesting idioblast differentiation involves an arrest in chloroplast development at a very early stage, rather than transdifferentiation of chloroplast-containing epidermal cells. PMID:25742311

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Segmentation-assisted detection of dirt impairments in archived film sequences.

PubMed

Ren, Jinchang; Vlachos, Theodore

2007-04-01

In this correspondence, a novel segmentation-assisted method for film-dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood, and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions, which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground-truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal, and multistage median-filtering approaches and provides efficient and robust detection for a wide variety of test materials.

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

PubMed

Nishiyama, Takeshi; Takahashi, Kunihiko; Tango, Toshiro; Pinto, Dalila; Scherer, Stephen W; Takami, Satoshi; Kishino, Hirohisa

2011-05-26

Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance. We propose a flexible statistical framework to circumvent these problems. Inspired by spatial scan statistics for detecting clustering of disease occurrence in the field of epidemiology, we developed a scan statistic to extract disease-associated gene clusters from a whole gene pathway. Extracting one or a few significant gene clusters from a global pathway limits the overall false positive probability, which results in increased statistical power, and facilitates the interpretation of test results. In the present study, we applied our method to genome-wide association data for rare copy-number variations, which have been strongly implicated in common diseases. Application of our method to a simulated dataset demonstrated the high accuracy of this method in detecting disease-associated gene clusters in a whole gene pathway. The scan statistic approach proposed here shows a high level of accuracy in detecting gene clusters in a whole gene pathway. This study has provided a sound statistical framework for analyzing genome-wide rare CNV data by incorporating topological information on the gene pathway.

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

X-Ray Spectroscopy of the Cluster of Galaxies Abell 1795 with XMM-Newton

NASA Technical Reports Server (NTRS)

Tamura, T.; Kaastra, J. S.; Peterson, J. R.; Paerels, F.; Mittaz, J. P. D.; Trudolyubov, S. P.; Stewart, G.; Fabian, A. C.; Mushotzky, R. F.; Lumb, D. H.

2000-01-01

The initial results from XMM-Newton observations of the rich cluster of galaxies Abell 1795 are presented. The spatially-resolved X-ray spectra taken by the European Photon Imaging Cameras (EPIC) show a temperature drop at a radius of - 200 kpc from the cluster center, indicating that the ICM is cooling. Both the EPIC and the Reflection Grating Spectrometers (RGS) spectra extracted from the cluster center can be described by an isothermal model with a temperature of approx. 4 keV. The volume emission measure of any cool component (less than 1 keV) is less than a few % of the hot component at the cluster center. A strong O VIII Lyman alpha line was detected with the RGS from the cluster core. The O abundance of the ICM is 0.2-0.5 times the solar value. The O to Fe ratio at the cluster center is 0.5 - 1.5 times the solar ratio.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Evaluation of single and two-stage adaptive sampling designs for estimation of density and abundance of freshwater mussels in a large river

USGS Publications Warehouse

Smith, D.R.; Rogala, J.T.; Gray, B.R.; Zigler, S.J.; Newton, T.J.

2011-01-01

Reliable estimates of abundance are needed to assess consequences of proposed habitat restoration and enhancement projects on freshwater mussels in the Upper Mississippi River (UMR). Although there is general guidance on sampling techniques for population assessment of freshwater mussels, the actual performance of sampling designs can depend critically on the population density and spatial distribution at the project site. To evaluate various sampling designs, we simulated sampling of populations, which varied in density and degree of spatial clustering. Because of logistics and costs of large river sampling and spatial clustering of freshwater mussels, we focused on adaptive and non-adaptive versions of single and two-stage sampling. The candidate designs performed similarly in terms of precision (CV) and probability of species detection for fixed sample size. Both CV and species detection were determined largely by density, spatial distribution and sample size. However, designs did differ in the rate that occupied quadrats were encountered. Occupied units had a higher probability of selection using adaptive designs than conventional designs. We used two measures of cost: sample size (i.e. number of quadrats) and distance travelled between the quadrats. Adaptive and two-stage designs tended to reduce distance between sampling units, and thus performed better when distance travelled was considered. Based on the comparisons, we provide general recommendations on the sampling designs for the freshwater mussels in the UMR, and presumably other large rivers.

Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event.

PubMed

Baer, Atar; Elbert, Yevgeniy; Burkom, Howard S; Holtry, Rekha; Lombardo, Joseph S; Duchin, Jeffrey S

2011-03-01

We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm. Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event. Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected. Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly. ©2011 American Medical Association. All rights reserved.

NGC 6362: THE LEAST MASSIVE GLOBULAR CLUSTER WITH CHEMICALLY DISTINCT MULTIPLE POPULATIONS

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

Mucciarelli, Alessio; Dalessandro, Emanuele; Ferraro, Francesco R.

2016-06-20

We present the first measure of Fe and Na abundances in NGC 6362, a low-mass globular cluster (GC) where first- and second-generation stars are fully spatially mixed. A total of 160 member stars (along the red giant branch (RGB) and the red horizontal branch (RHB)) were observed with the multi-object spectrograph FLAMES at the Very Large Telescope. We find that the cluster has an iron abundance of [Fe/H] = −1.09 ± 0.01 dex, without evidence of intrinsic dispersion. On the other hand, the [Na/Fe] distribution turns out to be intrinsically broad and bimodal. The Na-poor and Na-rich stars populate, respectively,more » the bluest and the reddest RGBs detected in the color–magnitude diagrams including the U filter. The RGB is composed of a mixture of first- and second-generation stars in a similar proportion, while almost all the RHB stars belong to the first cluster generation. To date, NGC 6362 is the least massive GC where both the photometric and spectroscopic signatures of multiple populations have been detected.« less

A US coordination Facility for the Spectrum-X-Gamma Observatory

NASA Technical Reports Server (NTRS)

Forman, W.; West, Donald (Technical Monitor)

2001-01-01

We have completed our efforts in support of the Spectrum X Gamma mission under a NASA grant. These activities have included direct support to the mission, developing unifying tools applicable to SXG and other X-ray astronomy missions, and X-ray astronomy research to maintain our understanding of the importance and relevance of SXG to the field. SXG provides: 1) Simultaneous Multiwavelength Capability; 2) Large Field of View High Resolution Imaging Spectroscopy; 3) Sensitive Polarimetry with SXRP (Stellar X-Ray Polarimeter). These capabilities will ensure the fulfillment of the following objectives: understanding the accretion dynamics and the importance of reprocessing, upscattering, and disk viscosity around black holes; studying cluster mergers; spatially resolving cluster cooling flows to detect cooling gas; detecting cool gas in cluster outskirts in absorption; mapping gas in filaments around clusters; finding the 'missing' baryons in the Universe; determining the activity history of the black hole in the Galactic Center of our own central black hole; determining pulsar beam geometry; searching for the Lense-Thirring effect in black hole sources; constraining emission mechanisms and accretion geometry in AGN.

Planck intermediate results. XLIII. Spectral energy distribution of dust in clusters of galaxies

NASA Astrophysics Data System (ADS)

Planck Collaboration; Adam, R.; Ade, P. A. R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoit-Lévy, A.; Bersanelli, M.; Bielewicz, P.; Bikmaev, I.; Bonaldi, A.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Burenin, R.; Burigana, C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Chiang, H. C.; Christensen, P. R.; Churazov, E.; Colombo, L. P. L.; Combet, C.; Comis, B.; Couchot, F.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Désert, F.-X.; Diego, J. M.; Dole, H.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Elsner, F.; Enßlin, T. A.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Galeotta, S.; Ganga, K.; Génova-Santos, R. T.; Giard, M.; Giraud-Héraud, Y.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hansen, F. K.; Harrison, D. L.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Hornstrup, A.; Hovest, W.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Jones, W. C.; Keihänen, E.; Keskitalo, R.; Khamitov, I.; Kisner, T. S.; Kneissl, R.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Leonardi, R.; Levrier, F.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; López-Caniego, M.; Macías-Pérez, J. F.; Maffei, B.; Maggio, G.; Mandolesi, N.; Mangilli, A.; Maris, M.; Martin, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Melchiorri, A.; Mennella, A.; Migliaccio, M.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Nørgaard-Nielsen, H. U.; Novikov, D.; Novikov, I.; Oxborrow, C. A.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Piat, M.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Pratt, G. W.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Santos, D.; Savelainen, M.; Savini, G.; Scott, D.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Valenziano, L.; Valiviita, J.; Van Tent, F.; Vielva, P.; Villa, F.; Wade, L. A.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

2016-12-01

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objects from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck's resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. The recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.

Planck intermediate results: XLIII. Spectral energy distribution of dust in clusters of galaxies

DOE PAGES

Adam, R.; Ade, P. A. R.; Aghanim, N.; ...

2016-12-12

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide in this paper new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objectsmore » from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck’s resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. Finally, the recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.« less

Modeling the Movement of Homicide by Type to Inform Public Health Prevention Efforts.

PubMed

Zeoli, April M; Grady, Sue; Pizarro, Jesenia M; Melde, Chris

2015-10-01

We modeled the spatiotemporal movement of hotspot clusters of homicide by motive in Newark, New Jersey, to investigate whether different homicide types have different patterns of clustering and movement. We obtained homicide data from the Newark Police Department Homicide Unit's investigative files from 1997 through 2007 (n = 560). We geocoded the address at which each homicide victim was found and recorded the date of and the motive for the homicide. We used cluster detection software to model the spatiotemporal movement of statistically significant homicide clusters by motive, using census tract and month of occurrence as the spatial and temporal units of analysis. Gang-motivated homicides showed evidence of clustering and diffusion through Newark. Additionally, gang-motivated homicide clusters overlapped to a degree with revenge and drug-motivated homicide clusters. Escalating dispute and nonintimate familial homicides clustered; however, there was no evidence of diffusion. Intimate partner and robbery homicides did not cluster. By tracking how homicide types diffuse through communities and determining which places have ongoing or emerging homicide problems by type, we can better inform the deployment of prevention and intervention efforts.

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.

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

A nonparametric spatial scan statistic for continuous data.

PubMed

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan

PubMed Central

Park, Rachel; O'Brien, Thomas F.; Huang, Susan S.; Baker, Meghan A.; Yokoe, Deborah S.; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-01-01

Objectives While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Methods Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Results Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Conclusion Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures. PMID:27530311

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.

Improving Spectral Image Classification through Band-Ratio Optimization and Pixel Clustering

NASA Astrophysics Data System (ADS)

O'Neill, M.; Burt, C.; McKenna, I.; Kimblin, C.

2017-12-01

The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of DigitalGlobe's WorldView-3 (WV3) to detect and map UNE signatures. WV3 is the current state-of-the-art, commercial, multispectral imaging satellite; however, it has relatively limited spectral and spatial resolutions. These limitations impede image classifiers from detecting targets that are spatially small and lack distinct spectral features. In order to improve classification results, we developed custom algorithms to reduce false positive rates while increasing true positive rates via a band-ratio optimization and pixel clustering front-end. The clusters resulting from these algorithms were processed with standard spectral image classifiers such as Mixture-Tuned Matched Filter (MTMF) and Adaptive Coherence Estimator (ACE). WV3 and AVIRIS data of Cuprite, Nevada, were used as a validation data set. These data were processed with a standard classification approach using MTMF and ACE algorithms. They were also processed using the custom front-end prior to the standard approach. A comparison of the results shows that the custom front-end significantly increases the true positive rate and decreases the false positive rate.This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy. DOE/NV/25946-3283.

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.

GLASS: The Grism Lens-Amplified Survey From Space. HST Grism Spectroscopy of the Frontier Fields.

NASA Astrophysics Data System (ADS)

Borello Schmidt, Kasper

2015-08-01

The Grism Lens-Amplified Survey From Space (GLASS) is a 140 orbit spectroscopic survey of 10 massive galaxy clusters, including the six Hubble Frontier Fields. GLASS has observed the cluster cores in the HST-WFC3 G102 and G141 grisms providing a wide wavelength coverage in the near-infrared from roughly 0.8 - 1.7 μm. The parallel fields were observed through the optical ACS G800L grism. Taking advantage of the lensing magnification of the clusters, GLASS reaches excellent spectroscopic limits of ˜10-18 erg/s/cm2 and improved spatial resolution for lensed sources behind the clusters. These features are particularly useful for the three main science drivers of GLASS, which are: I) Use the hundreds of spectra of galaxies at z>6 to shed light on the epoch of reionization, the role galaxies play in reionizing the universe, and the Lyα escape fraction at the cosmic dawn. II) Study gas accretion, star formation, and outflows by spatially mapping resolved star formation and determine metallicity gradients from emission lines at z˜2. III) Explore the environmental dependence of galaxy evolution using the first comprehensive census of spatially resolved star formation in dense environments, i.e., the cluster cores as well as the cluster infall regions. The former two benefit highly from the improved depth and increased resolution provided by the cluster lensing. Apart from the main science drivers, a slew of ancillary science has been enabled by the survey. One particularly interesting example is the search for supernovae in the more than 40 GLASS visits, which resulted in the detection of the first multiple imaged supernova, SN Refsdal. I will present the survey, give an update on the current science results, in particular on the GLASS galaxies at the epoch of reionization, and provide a status report on the GLASS data releases, which are continuously being made available to the community.

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

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.

Exploring spatial evolution of economic clusters: A case study of Beijing

NASA Astrophysics Data System (ADS)

Yang, Zhenshan; Sliuzas, Richard; Cai, Jianming; Ottens, Henk F. L.

2012-10-01

An identification of economic clusters and analysing their changing spatial patterns is important for understanding urban economic space dynamics. Previous studies, however, suffer from limitations as a consequence of using fixed geographically areas and not combining functional and spatial dynamics. The paper presents an approach, based on local spatial statistics and the case of Beijing to understand the spatial clustering of industries that are functionally interconnected by common or complementary patterns of demand or supply relations. Using register data of business establishments, it identifies economic clusters and analyses their pattern based on postcodes at different time slices during the period 1983-2002. The study shows how the advanced services occupy the urban centre and key sub centres. The Information and Communication Technology (ICT) cluster is mainly concentrated in the north part of the city and circles the urban centre, and the main manufacturing clusters are evolved in the key sub centers. This type of outcomes improves understanding of urban-economic dynamics, which can support spatial and economic planning.

Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2018-01-01

The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study region. The territorial heterogeneity of earthquakes clustering is in good agreement with spatial variability of scaling parameters identified by the USLE. In particular, the fractal dimension is higher to the west (about 1.2-1.4), suggesting a spatially more distributed seismicity, compared to the eastern parte of the investigated territory, where fractal dimension is very low (about 0.8-1.0).

Hot spot detection and spatio-temporal dispersion of dengue fever in Hanoi, Vietnam

PubMed Central

Toan, Do Thi Thanh; Hu, Wenbiao; Thai, Pham Quang; Hoat, Luu Ngoc; Wright, Pamela; Martens, Pim

2013-01-01

Introduction Dengue fever (DF) in Vietnam remains a serious emerging arboviral disease, which generates significant concerns among international health authorities. Incidence rates of DF have increased significantly during the last few years in many provinces and cities, especially Hanoi. The purpose of this study was to detect DF hot spots and identify the disease dynamics dispersion of DF over the period between 2004 and 2009 in Hanoi, Vietnam. Methods Daily data on DF cases and population data for each postcode area of Hanoi between January 1998 and December 2009 were obtained from the Hanoi Center for Preventive Health and the General Statistic Office of Vietnam. Moran's I statistic was used to assess the spatial autocorrelation of reported DF. Spatial scan statistics and logistic regression were used to identify space–time clusters and dispersion of DF. Results The study revealed a clear trend of geographic expansion of DF transmission in Hanoi through the study periods (OR 1.17, 95% CI 1.02–1.34). The spatial scan statistics showed that 6/14 (42.9%) districts in Hanoi had significant cluster patterns, which lasted 29 days and were limited to a radius of 1,000 m. The study also demonstrated that most DF cases occurred between June and November, during which the rainfall and temperatures are highest. Conclusions There is evidence for the existence of statistically significant clusters of DF in Hanoi, and that the geographical distribution of DF has expanded over recent years. This finding provides a foundation for further investigation into the social and environmental factors responsible for changing disease patterns, and provides data to inform program planning for DF control. PMID:23364076

Hot spot detection and spatio-temporal dispersion of dengue fever in Hanoi, Vietnam.

PubMed

Toan, Do Thi Thanh; Hu, Wenbiao; Quang Thai, Pham; Hoat, Luu Ngoc; Wright, Pamela; Martens, Pim

2013-01-24

Dengue fever (DF) in Vietnam remains a serious emerging arboviral disease, which generates significant concerns among international health authorities. Incidence rates of DF have increased significantly during the last few years in many provinces and cities, especially Hanoi. The purpose of this study was to detect DF hot spots and identify the disease dynamics dispersion of DF over the period between 2004 and 2009 in Hanoi, Vietnam. Daily data on DF cases and population data for each postcode area of Hanoi between January 1998 and December 2009 were obtained from the Hanoi Center for Preventive Health and the General Statistic Office of Vietnam. Moran's I statistic was used to assess the spatial autocorrelation of reported DF. Spatial scan statistics and logistic regression were used to identify space-time clusters and dispersion of DF. The study revealed a clear trend of geographic expansion of DF transmission in Hanoi through the study periods (OR 1.17, 95% CI 1.02-1.34). The spatial scan statistics showed that 6/14 (42.9%) districts in Hanoi had significant cluster patterns, which lasted 29 days and were limited to a radius of 1,000 m. The study also demonstrated that most DF cases occurred between June and November, during which the rainfall and temperatures are highest. There is evidence for the existence of statistically significant clusters of DF in Hanoi, and that the geographical distribution of DF has expanded over recent years. This finding provides a foundation for further investigation into the social and environmental factors responsible for changing disease patterns, and provides data to inform program planning for DF control.

Geospatial Distribution and Clustering of Chlamydia trachomatis in Communities Undergoing Mass Azithromycin Treatment

PubMed Central

Yohannan, Jithin; He, Bing; Wang, Jiangxia; Greene, Gregory; Schein, Yvette; Mkocha, Harran; Munoz, Beatriz; Quinn, Thomas C.; Gaydos, Charlotte; West, Sheila K.

2014-01-01

Purpose. We detected spatial clustering of households with Chlamydia trachomatis infection (CI) and active trachoma (AT) in villages undergoing mass treatment with azithromycin (MDA) over time. Methods. We obtained global positioning system (GPS) coordinates for all households in four villages in Kongwa District, Tanzania. Every 6 months for a period of 42 months, our team examined all children under 10 for AT, and tested for CI with ocular swabbing and Amplicor. Villages underwent four rounds of annual MDA. We classified households as having ≥1 child with CI (or AT) or having 0 children with CI (or AT). We calculated the difference in the K function between households with and without CI or AT to detect clustering at each time point. Results. Between 918 and 991 households were included over the 42 months of this analysis. At baseline, 306 households (32.59%) had ≥1 child with CI, which declined to 73 households (7.50%) at 42 months. We observed borderline clustering of households with CI at 12 months after one round of MDA and statistically significant clustering with growing cluster sizes between 18 and 24 months after two rounds of MDA. Clusters diminished in size at 30 months after 3 rounds of MDA. Active trachoma did not cluster at any time point. Conclusions. This study demonstrates that CI clusters after multiple rounds of MDA. Clusters of infection may increase in size if the annual antibiotic pressure is removed. The absence of growth after the three rounds suggests the start of control of transmission. PMID:24906862

Quantifying spatial and temporal trends in beach-dune volumetric changes using spatial statistics

NASA Astrophysics Data System (ADS)

Eamer, Jordan B. R.; Walker, Ian J.

2013-06-01

Spatial statistics are generally underutilized in coastal geomorphology, despite offering great potential for identifying and quantifying spatial-temporal trends in landscape morphodynamics. In particular, local Moran's Ii provides a statistical framework for detecting clusters of significant change in an attribute (e.g., surface erosion or deposition) and quantifying how this changes over space and time. This study analyzes and interprets spatial-temporal patterns in sediment volume changes in a beach-foredune-transgressive dune complex following removal of invasive marram grass (Ammophila spp.). Results are derived by detecting significant changes in post-removal repeat DEMs derived from topographic surveys and airborne LiDAR. The study site was separated into discrete, linked geomorphic units (beach, foredune, transgressive dune complex) to facilitate sub-landscape scale analysis of volumetric change and sediment budget responses. Difference surfaces derived from a pixel-subtraction algorithm between interval DEMs and the LiDAR baseline DEM were filtered using the local Moran's Ii method and two different spatial weights (1.5 and 5 m) to detect statistically significant change. Moran's Ii results were compared with those derived from a more spatially uniform statistical method that uses a simpler student's t distribution threshold for change detection. Morphodynamic patterns and volumetric estimates were similar between the uniform geostatistical method and Moran's Ii at a spatial weight of 5 m while the smaller spatial weight (1.5 m) consistently indicated volumetric changes of less magnitude. The larger 5 m spatial weight was most representative of broader site morphodynamics and spatial patterns while the smaller spatial weight provided volumetric changes consistent with field observations. All methods showed foredune deflation immediately following removal with increased sediment volumes into the spring via deposition at the crest and on lobes in the lee, despite erosion on the stoss slope and dune toe. Generally, the foredune became wider by landward extension and the seaward slope recovered from erosion to a similar height and form to that of pre-restoration despite remaining essentially free of vegetation.

Spatial Analysis of Dengue Seroprevalence and Modeling of Transmission Risk Factors in a Dengue Hyperendemic City of Venezuela.

PubMed

Vincenti-Gonzalez, Maria F; Grillet, María-Eugenia; Velasco-Salas, Zoraida I; Lizarazo, Erley F; Amarista, Manuel A; Sierra, Gloria M; Comach, Guillermo; Tami, Adriana

2017-01-01

Dengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach. From August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela. Local spatial statistics conducted at household- and block-level identified clusters of recent dengue seroprevalence (39 hot spot households and 9 hot spot blocks) in all neighborhoods. However, no clusters were found for past dengue seroprevalence. Clustering of infection was detected at a very small scale (20-110m) suggesting a high disease focal aggregation. Factors associated with living in a hot spot household were occupation (being a domestic worker/housewife (P = 0.002), lower socio-economic status (living in a shack (P<0.001), sharing a household with <7 people (P = 0.004), promoting potential vector breeding sites (storing water in containers (P = 0.024), having litter outdoors (P = 0.002) and mosquito preventive measures (such as using repellent, P = 0.011). Similarly, low socio-economic status (living in crowded conditions, P<0.001), having an occupation of domestic worker/housewife (P = 0.012) and not using certain preventive measures against mosquitoes (P<0.05) were directly associated with living in a hot spot block. Our findings contribute to a better comprehension of the spatial dynamics of dengue by assessing the relationship between disease clusters and their risk factors. These results can inform health authorities in the design of surveillance and control activities. Focalizing dengue control measures during epidemic and inter-epidemic periods to disease high risk zones at household and neighborhood-level may significantly reduce virus transmission in comparison to random interventions.

Spatial Analysis of Dengue Seroprevalence and Modeling of Transmission Risk Factors in a Dengue Hyperendemic City of Venezuela

PubMed Central

Vincenti-Gonzalez, Maria F.; Grillet, María-Eugenia; Velasco-Salas, Zoraida I.; Lizarazo, Erley F.; Amarista, Manuel A.; Sierra, Gloria M.; Comach, Guillermo

2017-01-01

Background Dengue virus (DENV) transmission is spatially heterogeneous. Hence, to stratify dengue prevalence in space may be an efficacious strategy to target surveillance and control efforts in a cost-effective manner particularly in Venezuela where dengue is hyperendemic and public health resources are scarce. Here, we determine hot spots of dengue seroprevalence and the risk factors associated with these clusters using local spatial statistics and a regression modeling approach. Methodology/Principal Findings From August 2010 to January 2011, a community-based cross-sectional study of 2012 individuals in 840 households was performed in high incidence neighborhoods of a dengue hyperendemic city in Venezuela. Local spatial statistics conducted at household- and block-level identified clusters of recent dengue seroprevalence (39 hot spot households and 9 hot spot blocks) in all neighborhoods. However, no clusters were found for past dengue seroprevalence. Clustering of infection was detected at a very small scale (20-110m) suggesting a high disease focal aggregation. Factors associated with living in a hot spot household were occupation (being a domestic worker/housewife (P = 0.002), lower socio-economic status (living in a shack (P<0.001), sharing a household with <7 people (P = 0.004), promoting potential vector breeding sites (storing water in containers (P = 0.024), having litter outdoors (P = 0.002) and mosquito preventive measures (such as using repellent, P = 0.011). Similarly, low socio-economic status (living in crowded conditions, P<0.001), having an occupation of domestic worker/housewife (P = 0.012) and not using certain preventive measures against mosquitoes (P<0.05) were directly associated with living in a hot spot block. Conclusions/Significance Our findings contribute to a better comprehension of the spatial dynamics of dengue by assessing the relationship between disease clusters and their risk factors. These results can inform health authorities in the design of surveillance and control activities. Focalizing dengue control measures during epidemic and inter-epidemic periods to disease high risk zones at household and neighborhood-level may significantly reduce virus transmission in comparison to random interventions. PMID:28114342

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected Via the Sunyaev-Zel'dovich Effect

NASA Technical Reports Server (NTRS)

Sehgal, Neelima; Trac, Hy; Acquaviva, Viviana; Ade, Peter A. R.; Aguirre, Paula; Amiri, Mandana; Appel, John W.; Barrientos, L. Felipe; Battistelli, Elia S.; Bond, J. Richard;

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.

Evaluation of null-point detection methods on simulation data

NASA Astrophysics Data System (ADS)

Olshevsky, Vyacheslav; Fu, Huishan; Vaivads, Andris; Khotyaintsev, Yuri; Lapenta, Giovanni; Markidis, Stefano

2014-05-01

We model the measurements of artificial spacecraft that resemble the configuration of CLUSTER propagating in the particle-in-cell simulation of turbulent magnetic reconnection. The simulation domain contains multiple isolated X-type null-points, but the majority are O-type null-points. Simulations show that current pinches surrounded by twisted fields, analogous to laboratory pinches, are formed along the sequences of O-type nulls. In the simulation, the magnetic reconnection is mainly driven by the kinking of the pinches, at spatial scales of several ion inertial lentghs. We compute the locations of magnetic null-points and detect their type. When the satellites are separated by the fractions of ion inertial length, as it is for CLUSTER, they are able to locate both the isolated null-points, and the pinches. We apply the method to the real CLUSTER data and speculate how common are pinches in the magnetosphere, and whether they play a dominant role in the dissipation of magnetic energy.

Detecting Spatial Patterns of Natural Hazards from the Wikipedia Knowledge Base

NASA Astrophysics Data System (ADS)

Fan, J.; Stewart, K.

2015-07-01

The Wikipedia database is a data source of immense richness and variety. Included in this database are thousands of geotagged articles, including, for example, almost real-time updates on current and historic natural hazards. This includes usercontributed information about the location of natural hazards, the extent of the disasters, and many details relating to response, impact, and recovery. In this research, a computational framework is proposed to detect spatial patterns of natural hazards from the Wikipedia database by combining topic modeling methods with spatial analysis techniques. The computation is performed on the Neon Cluster, a high performance-computing cluster at the University of Iowa. This work uses wildfires as the exemplar hazard, but this framework is easily generalizable to other types of hazards, such as hurricanes or flooding. Latent Dirichlet Allocation (LDA) modeling is first employed to train the entire English Wikipedia dump, transforming the database dump into a 500-dimension topic model. Over 230,000 geo-tagged articles are then extracted from the Wikipedia database, spatially covering the contiguous United States. The geo-tagged articles are converted into an LDA topic space based on the topic model, with each article being represented as a weighted multidimension topic vector. By treating each article's topic vector as an observed point in geographic space, a probability surface is calculated for each of the topics. In this work, Wikipedia articles about wildfires are extracted from the Wikipedia database, forming a wildfire corpus and creating a basis for the topic vector analysis. The spatial distribution of wildfire outbreaks in the US is estimated by calculating the weighted sum of the topic probability surfaces using a map algebra approach, and mapped using GIS. To provide an evaluation of the approach, the estimation is compared to wildfire hazard potential maps created by the USDA Forest service.

Quantile regression and Bayesian cluster detection to identify radon prone areas.

PubMed

Sarra, Annalina; Fontanella, Lara; Valentini, Pasquale; Palermi, Sergio

2016-11-01

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatio-Temporal Variation of Longevity Clusters and the Influence of Social Development Level on Lifespan in a Chinese Longevous Area (1982–2010)

PubMed Central

Qin, Jian; Xia, Tianlong; Li, You; Liang, Xue; Wei, Peng; Long, Bingshuang; Lei, Mingzhi; Wei, Xiao; Tang, Xianyan; Zhang, Zhiyong

2017-01-01

The study aims to determine the spatial and temporal variation of a longevous region and explore the correlation between longevity and socioeconomic development. Population data at the township level were obtained from the last four population censuses (1982–2010). Five main lifespan indicators and the Human Development Index (HDI) were calculated. Getis-Ord G*, Gravity modeling, and Pearson’s r between lifespan indicators and HDI were applied. In this study, a stable longevous gathering area was discovered in Hechi during different periods. Under the influence of social and economic development, more longevous areas appeared. However, the effects of genetic and natural environmental factors on longevity were always dominant in this remote and mountainous city. Furthermore, longevity indicators lacked any significant correlation with life expectancy. No significant positive correlation was detected between lifespan indicators and HDI. Thus, we conclude that lifespan indicators can determine the spatial distribution and variation pattern of longevity from multiple dimensions. The geographical scope of longevity in Hechi City is gradually expanding, and significant spatial clustering was detected in southwestern, southern, and eastern parts of Hechi. This study also found that social economic development is likely to have a certain impact on new longevous areas, but their role on extreme longevity is not significant. PMID:28753971

Geographic information systems and pharmacoepidemiology: using spatial cluster detection to monitor local patterns of prescription opioid abuse.

PubMed

Brownstein, John S; Green, Traci C; Cassidy, Theresa A; Butler, Stephen F

2010-06-01

Understanding the spatial distribution of opioid abuse at the local level may facilitate public health interventions. Using patient-level data from addiction treatment facilities in New Mexico from ASI-MV Connect, we applied geographic information system (GIS) in combination with a spatial scan statistic to generate risk maps of prescription opioid abuse and identify clusters of product- and compound-specific abuse. Prescribed opioid volume data was used to determine whether identified clusters are beyond geographic differences in availability. Data on 24 452 patients residing in New Mexico were collected. Among those patients, 1779 (7.3%) reported abusing any prescription opioid (past 30 days). According to opioid type, 979 patients (4.0%) reported abuse of any hydrocodone, 1007 (4.1%) for any oxycodone, 108 (0.4%) for morphine, 507 (2.1%) for Vicodin or generic equivalent, 390 (1.6%) for OxyContin, and 63 (0.2%) for MS Contin or generic equivalent. Highest rates of abuse were found in the area surrounding Albuquerque with 8.6 patients indicating abuse per 100 interviewed patients. We found clustering of abuse around Albuquerque (P = 0.001; Relative Risk = 1.35, and a radius of 146 km). At the compound level, we found that drug availability was partly responsible for clustering of prescription opioid abuse. After accounting for drug availability, we identified a second foci of Vicodin abuse in the southern rural portion of the state near Las Cruces, NM and El Paso, Texas and bordering Mexico (RR = 2.1; P = 0.001). A better understanding of local risk distribution may have implications for response strategies to future introductions of prescription opioids.

Geographic Informations Systems and Pharmacoepidemiology: Using spatial cluster detection to monitor local patterns of prescription opioid abuse

PubMed Central

Brownstein, John S.; Green, Traci C.; Cassidy, Theresa A.; Butler, Stephen F.

2010-01-01

Purpose Understanding the spatial distribution of opioid abuse at the local level may facilitate public health interventions. Methods Using patient-level data from addiction treatment facilities in New Mexico from ASI-MV® Connect, we applied geographic information system in combination with a spatial scan statistics to generate risk maps of prescription opioid abuse and identify clusters of product- and compound-specific abuse. Prescribed opioid volume data was used to determine whether identified clusters are beyond geographic differences in availability. Results Data on 24,452 patients residing in New Mexico was collected. Among those patients, 1779 (7.3%) reported abusing any prescription opioid (past 30 days). According to opioid type, 979 patients (4.0%) reported abuse of any hydrocodone, 1007 (4.1%) for any oxycodone, 108 (0.4%) for morphine, 507 (2.1%) for Vicodin® or generic equivalent, 390 (1.6%) for OxyContin®, and 63 (0.2%) for MS Contin® or generic equivalent. Highest rates of abuse were found in the area surrounding Albuquerque with 8.6 patients indicating abuse per 100 interviewed patients. We found clustering of abuse around Albuquerque (P=0.001; Relative Risk=1.35 and a radius of 146 km). At the compound level, we found that drug availability was partly responsible for clustering of prescription opioid abuse. After accounting for drug availability, we identified a second foci of Vicodin® abuse in the southern rural portion of the state near Las Cruces, NM and El Paso, Texas and bordering Mexico (RR=2.1; P=0.001). Conclusions A better understanding of local risk distribution may have implications for response strategies to future introductions of prescription opioids. PMID:20535759

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.

Extended halos and intracluster light using Planetary Nebulae as tracers in nearby clusters

NASA Astrophysics Data System (ADS)

Arnaboldi, Magda

Since the first detection of intracluster planetary nebulae in 1996, imaging and spectroscopic surveys identified such stars to trace the radial extent and the kinematics of diffuse light in clusters. This topic of research is tightly linked with the studies of galaxy formation and evolution in dense environment, as the spatial distribution and kinematics of planetary nebulae in the outermost regions of galaxies and in the cluster cores is relevant for setting constraints on cosmological simulations. In this sense, extragalactic planetary nebulae play a very important role in the near-field cosmology, in order to measure the integrated mass as function of radius and the orbital distribution of stars in structures placed in the densest regions of the nearby universe.

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

Adam, R.; Ade, P. A. R.; Aghanim, N.

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide in this paper new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objectsmore » from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck’s resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. Finally, the recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.« less

Detection of extended galactic sources with an underwater neutrino telescope

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

Leisos, A.; Tsirigotis, A. G.; Tzamarias, S. E.

2014-11-18

In this study we investigate the discovery capability of a Very Large Volume Neutrino Telescope to Galactic extended sources. We focus on the brightest HESS gamma rays sources which are considered also as very high energy neutrino emitters. We use the unbinned method taking into account both the spatial and the energy distribution of high energy neutrinos and we investigate parts of the Galactic plane where nearby potential neutrino emitters form neutrino source clusters. Neutrino source clusters as well as isolated neutrino sources are combined to estimate the observation period for 5 sigma discovery of neutrino signals from these objects.

The Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII): Towards the First Flight

NASA Technical Reports Server (NTRS)

Rizzo, Maxime J.; Rinehart, S. A.; Dhabal, A.; Ade, P.; Benford, D. J.; Fixsen, D. J.; Griffin, M.; Juanola Parramon, R.; Leisawitz, D. T.; Maher, S. F.;

A novel surveillance approach for disaster mental health

PubMed Central

Shankardass, Ketan; Subramanian, S. V.; Galea, Sandro

2017-01-01

Background Disasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions. Methods We applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions. Results Sadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster. Conclusions We proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters. PMID:28723959

A novel surveillance approach for disaster mental health.

PubMed

Gruebner, Oliver; Lowe, Sarah R; Sykora, Martin; Shankardass, Ketan; Subramanian, S V; Galea, Sandro

2017-01-01

Disasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions. We applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions. Sadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster. We proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters.

Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.

2016-12-01

Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.

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.

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.

Infrared spectroscopy reveals both qualitative and quantitative differences in equine subchondral bone during maturation

NASA Astrophysics Data System (ADS)

Kobrina, Yevgeniya; Isaksson, Hanna; Sinisaari, Miikka; Rieppo, Lassi; Brama, Pieter A.; van Weeren, René; Helminen, Heikki J.; Jurvelin, Jukka S.; Saarakkala, Simo

2010-11-01

The collagen phase in bone is known to undergo major changes during growth and maturation. The objective of this study is to clarify whether Fourier transform infrared (FTIR) microspectroscopy, coupled with cluster analysis, can detect quantitative and qualitative changes in the collagen matrix of subchondral bone in horses during maturation and growth. Equine subchondral bone samples (n = 29) from the proximal joint surface of the first phalanx are prepared from two sites subjected to different loading conditions. Three age groups are studied: newborn (0 days old), immature (5 to 11 months old), and adult (6 to 10 years old) horses. Spatial collagen content and collagen cross-link ratio are quantified from the spectra. Additionally, normalized second derivative spectra of samples are clustered using the k-means clustering algorithm. In quantitative analysis, collagen content in the subchondral bone increases rapidly between the newborn and immature horses. The collagen cross-link ratio increases significantly with age. In qualitative analysis, clustering is able to separate newborn and adult samples into two different groups. The immature samples display some nonhomogeneity. In conclusion, this is the first study showing that FTIR spectral imaging combined with clustering techniques can detect quantitative and qualitative changes in the collagen matrix of subchondral bone during growth and maturation.

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

NASA Astrophysics Data System (ADS)

Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

2014-04-01

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

Outlier detection for particle image velocimetry data using a locally estimated noise variance

NASA Astrophysics Data System (ADS)

Lee, Yong; Yang, Hua; Yin, ZhouPing

2017-03-01

This work describes an adaptive spatial variable threshold outlier detection algorithm for raw gridded particle image velocimetry data using a locally estimated noise variance. This method is an iterative procedure, and each iteration is composed of a reference vector field reconstruction step and an outlier detection step. We construct the reference vector field using a weighted adaptive smoothing method (Garcia 2010 Comput. Stat. Data Anal. 54 1167-78), and the weights are determined in the outlier detection step using a modified outlier detector (Ma et al 2014 IEEE Trans. Image Process. 23 1706-21). A hard decision on the final weights of the iteration can produce outlier labels of the field. The technical contribution is that the spatial variable threshold motivation is embedded in the modified outlier detector with a locally estimated noise variance in an iterative framework for the first time. It turns out that a spatial variable threshold is preferable to a single spatial constant threshold in complicated flows such as vortex flows or turbulent flows. Synthetic cellular vortical flows with simulated scattered or clustered outliers are adopted to evaluate the performance of our proposed method in comparison with popular validation approaches. This method also turns out to be beneficial in a real PIV measurement of turbulent flow. The experimental results demonstrated that the proposed method yields the competitive performance in terms of outlier under-detection count and over-detection count. In addition, the outlier detection method is computational efficient and adaptive, requires no user-defined parameters, and corresponding implementations are also provided in supplementary materials.

Spatial modelling and mapping of female genital mutilation in Kenya.

PubMed

Achia, Thomas N O

2014-03-25

Female genital mutilation/cutting (FGM/C) is still prevalent in several communities in Kenya and other areas in Africa, as well as being practiced by some migrants from African countries living in other parts of the world. This study aimed at detecting clustering of FGM/C in Kenya, and identifying those areas within the country where women still intend to continue the practice. A broader goal of the study was to identify geographical areas where the practice continues unabated and where broad intervention strategies need to be introduced. The prevalence of FGM/C was investigated using the 2008 Kenya Demographic and Health Survey (KDHS) data. The 2008 KDHS used a multistage stratified random sampling plan to select women of reproductive age (15-49 years) and asked questions concerning their FGM/C status and their support for the continuation of FGM/C. A spatial scan statistical analysis was carried out using SaTScan™ to test for statistically significant clustering of the practice of FGM/C in the country. The risk of FGM/C was also modelled and mapped using a hierarchical spatial model under the Integrated Nested Laplace approximation approach using the INLA library in R. The prevalence of FGM/C stood at 28.2% and an estimated 10.3% of the women interviewed indicated that they supported the continuation of FGM. On the basis of the Deviance Information Criterion (DIC), hierarchical spatial models with spatially structured random effects were found to best fit the data for both response variables considered. Age, region, rural-urban classification, education, marital status, religion, socioeconomic status and media exposure were found to be significantly associated with FGM/C. The current FGM/C status of a woman was also a significant predictor of support for the continuation of FGM/C. Spatial scan statistics confirm FGM clusters in the North-Eastern and South-Western regions of Kenya (p<0.001). This suggests that the fight against FGM/C in Kenya is not yet over. There are still deep cultural and religious beliefs to be addressed in a bid to eradicate the practice. Interventions by government and other stakeholders must address these challenges and target the identified clusters.

Optimizing surveillance for livestock disease spreading through animal movements

PubMed Central

Bajardi, Paolo; Barrat, Alain; Savini, Lara; Colizza, Vittoria

2012-01-01

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems. PMID:22728387

Spatial correlations of Diceroprocta apache and its host plants: Evidence for a negative impact from Tamarix invasion

USGS Publications Warehouse

Ellingson, A.R.; Andersen, D.C.

2002-01-01

1. The hypothesis that the habitat-scale spatial distribution of the, Apache cicada Diceroprocta apache Davis is unaffected by the presence of the invasive exotic saltcedar Tamarix ramosissima was tested using data from 205 1-m2 quadrats placed within the flood-plain of the Bill Williams River, Arizona, U.S.A. Spatial dependencies within and between cicada density and habitat variables were estimated using Moran's I and its bivariate analogue to discern patterns and associations at spatial scales from 1 to 30 m. 2. Apache cicadas were spatially aggregated in high-density clusters averaging 3m in diameter. A positive association between cicada density, estimated by exuvial density, and the per cent canopy cover of a native tree, Goodding's willow Salix gooddingii, was detected in a non-spatial correlation analysis. No non-spatial association between cicada density and saltcedar canopy cover was detected. 3. Tests for spatial cross-correlation using the bivariate IYZ indicated the presence of a broad-scale negative association between cicada density and saltcedar canopy cover. This result suggests that large continuous stands of saltcedar are associated with reduced cicada density. In contrast, positive associations detected at spatial scales larger than individual quadrats suggested a spill-over of high cicada density from areas featuring Goodding's willow canopy into surrounding saltcedar monoculture. 4. Taken together and considered in light of the Apache cicada's polyphagous habits, the observed spatial patterns suggest that broad-scale factors such as canopy heterogeneity affect cicada habitat use more than host plant selection. This has implications for management of lower Colorado River riparian woodlands to promote cicada presence and density through maintenance or creation of stands of native trees as well as manipulation of the characteristically dense and homogeneous saltcedar canopies.

Spatial correlations of Diceroprocta apache and its host plants: Evidence for a negative impact from Tamarix invasion

USGS Publications Warehouse

Ellingson, A.R.; Andersen, D.C.

2002-01-01

1. The hypothesis that the habitat-scale spatial distribution of the Apache cicada Diceroprocta apache Davis is unaffected by the presence of the invasive exotic saltcedar Tamarix ramosissima was tested using data from 205 1-m2 quadrats placed within the flood-plain of the Bill Williams River, Arizona, U.S.A. Spatial dependencies within and between cicada density and habitat variables were estimated using Moran's I and its bivariate analogue to discern patterns and associations at spatial scales from 1 to 30 m.2. Apache cicadas were spatially aggregated in high-density clusters averaging 3 m in diameter. A positive association between cicada density, estimated by exuvial density, and the per cent canopy cover of a native tree, Goodding's willow Salix gooddingii, was detected in a non-spatial correlation analysis. No non-spatial association between cicada density and saltcedar canopy cover was detected.3. Tests for spatial cross-correlation using the bivariate IYZ indicated the presence of a broad-scale negative association between cicada density and saltcedar canopy cover. This result suggests that large continuous stands of saltcedar are associated with reduced cicada density. In contrast, positive associations detected at spatial scales larger than individual quadrats suggested a spill-over of high cicada density from areas featuring Goodding's willow canopy into surrounding saltcedar monoculture.4. Taken together and considered in light of the Apache cicada's polyphagous habits, the observed spatial patterns suggest that broad-scale factors such as canopy heterogeneity affect cicada habitat use more than host plant selection. This has implications for management of lower Colorado River riparian woodlands to promote cicada presence and density through maintenance or creation of stands of native trees as well as manipulation of the characteristically dense and homogeneous saltcedar canopies.

Modeling the Movement of Homicide by Type to Inform Public Health Prevention Efforts

PubMed Central

Grady, Sue; Pizarro, Jesenia M.; Melde, Chris

2015-01-01

Objectives. We modeled the spatiotemporal movement of hotspot clusters of homicide by motive in Newark, New Jersey, to investigate whether different homicide types have different patterns of clustering and movement. Methods. We obtained homicide data from the Newark Police Department Homicide Unit’s investigative files from 1997 through 2007 (n = 560). We geocoded the address at which each homicide victim was found and recorded the date of and the motive for the homicide. We used cluster detection software to model the spatiotemporal movement of statistically significant homicide clusters by motive, using census tract and month of occurrence as the spatial and temporal units of analysis. Results. Gang-motivated homicides showed evidence of clustering and diffusion through Newark. Additionally, gang-motivated homicide clusters overlapped to a degree with revenge and drug-motivated homicide clusters. Escalating dispute and nonintimate familial homicides clustered; however, there was no evidence of diffusion. Intimate partner and robbery homicides did not cluster. Conclusions. By tracking how homicide types diffuse through communities and determining which places have ongoing or emerging homicide problems by type, we can better inform the deployment of prevention and intervention efforts. PMID:26270315

Localized Hotspots Drive Continental Geography of Abnormal Amphibians on U.S. Wildlife Refuges

PubMed Central

Reeves, Mari K.; Medley, Kimberly A.; Pinkney, Alfred E.; Holyoak, Marcel; Johnson, Pieter T. J.; Lannoo, Michael J.

2013-01-01

Amphibians with missing, misshapen, and extra limbs have garnered public and scientific attention for two decades, yet the extent of the phenomenon remains poorly understood. Despite progress in identifying the causes of abnormalities in some regions, a lack of knowledge about their broader spatial distribution and temporal dynamics has hindered efforts to understand their implications for amphibian population declines and environmental quality. To address this data gap, we conducted a nationwide, 10-year assessment of 62,947 amphibians on U.S. National Wildlife Refuges. Analysis of a core dataset of 48,081 individuals revealed that consistent with expected background frequencies, an average of 2% were abnormal, but abnormalities exhibited marked spatial variation with a maximum prevalence of 40%. Variance partitioning analysis demonstrated that factors associated with space (rather than species or year sampled) captured 97% of the variation in abnormalities, and the amount of partitioned variance decreased with increasing spatial scale (from site to refuge to region). Consistent with this, abnormalities occurred in local to regional hotspots, clustering at scales of tens to hundreds of kilometers. We detected such hotspot clusters of high-abnormality sites in the Mississippi River Valley, California, and Alaska. Abnormality frequency was more variable within than outside of hotspot clusters. This is consistent with dynamic phenomena such as disturbance or natural enemies (pathogens or predators), whereas similarity of abnormality frequencies at scales of tens to hundreds of kilometers suggests involvement of factors that are spatially consistent at a regional scale. Our characterization of the spatial and temporal variation inherent in continent-wide amphibian abnormalities demonstrates the disproportionate contribution of local factors in predicting hotspots, and the episodic nature of their occurrence. PMID:24260103

The CAnadian NIRISS Unbiased Cluster Survey (CANUCS)

NASA Astrophysics Data System (ADS)

Ravindranath, Swara; NIRISS GTO Team

2017-06-01

CANUCS GTO program is a JWST spectroscopy and imaging survey of five massive galaxy clusters and ten parallel fields using the NIRISS low-resolution grisms, NIRCam imaging and NIRSpec multi-object spectroscopy. The primary goal is to understand the evolution of low mass galaxies across cosmic time. The resolved emission line maps and line ratios for many galaxies, with some at resolution of 100pc via the magnification by gravitational lensing will enable determining the spatial distribution of star formation, dust and metals. Other science goals include the detection and characterization of galaxies within the reionization epoch, using multiply-imaged lensed galaxies to constrain cluster mass distributions and dark matter substructure, and understanding star-formation suppression in the most massive galaxy clusters. In this talk I will describe the science goals of the CANUCS program. The proposed prime and parallel observations will be presented with details of the implementation of the observation strategy using JWST proposal planning tools.

Constraints on the dark matter neutralinos from the radio emissions of galaxy clusters

NASA Astrophysics Data System (ADS)

Kiew, Ching-Yee; Hwang, Chorng-Yuan; Zainal Abibin, Zamri

2017-05-01

By assuming the dark matter to be composed of neutralinos, we used the detection of upper limit on diffuse radio emission in a sample of galaxy clusters to put constraint on the properties of neutralinos. We showed the upper limit constraint on <σv>-mχ space with neutralino annihilation through b\\bar{b} and μ+μ- channels. The best constraint is from the galaxy clusters A2199 and A1367. We showed the uncertainty due to the density profile and cluster magnetic field. The largest uncertainty comes from the uncertainty in dark matter spatial distribution. We also investigated the constraints on minimal Supergravity (mSUGRA) and minimal supersymmetric standard model (MSSM) parameter space by scanning the parameters using the darksusy package. By using the current radio observation, we managed to exclude 40 combinations of mSUGRA parameters. On the other hand, 573 combinations of MSSM parameters can be excluded by current observation.

Dense CO in Mrk 71-A: Superwind Suppressed in a Young Super Star Cluster

NASA Astrophysics Data System (ADS)

Oey, M. S.; Herrera, C. N.; Silich, Sergiy; Reiter, Megan; James, Bethan L.; Jaskot, A. E.; Micheva, Genoveva

2017-11-01

We report the detection of CO(J=2-1) coincident with the super star cluster (SSC) Mrk 71-A in the nearby Green Pea analog galaxy, NGC 2366. Our observations with the Northern Extended Millimeter Array reveal a compact, ˜7 pc, molecular cloud whose mass ({10}5 {M}⊙ ) is similar to that of the SSC, consistent with a high star formation efficiency, on the order of 0.5. There are two spatially distinct components separated by 11 {km} {{{s}}}-1. If expanding, these could be due to momentum-driven stellar wind feedback. Alternatively, we may be seeing remnants of the infalling, colliding clouds responsible for triggering the SSC formation. The kinematics are also consistent with a virialized system. These extreme, high-density, star-forming conditions inhibit energy-driven feedback; the co-spatial existence of a massive, molecular cloud with the SSC supports this scenario, and we quantitatively confirm that any wind-driven feedback in Mrk 71-A is momentum-driven, rather than energy-driven. Since Mrk 71-A is a candidate Lyman continuum emitter, this implies that energy-driven superwinds may not be a necessary condition for the escape of ionizing radiation. In addition, the detection of nebular continuum emission yields an accurate astrometric position for the Mrk 71-A. We also detect four other massive molecular clouds in this giant star-forming complex.

Origin, distribution, and potential risk factors associated with influenza A virus in swine in two production systems in Guatemala.

PubMed

Gonzalez-Reiche, Ana S; Ramírez, Ana L; Müller, María L; Orellana, David; Sosa, Silvia M; Ola, Pablo; Paniagua, Jorge; Ortíz, Lucía; Hernandez, Jorge; Cordón-Rosales, Celia; Perez, Daniel R

2017-03-01

Guatemala is the country with the largest swine production in Central America; however, evidence of influenza A virus (IAV) in pigs has not been clearly delineated. In this study, we analyzed the presence and spatial distribution of IAV in commercial and backyard swine populations. Samples from two nationwide surveys conducted in 2010 and 2011 were tested using virological (rRT-PCR and virus isolation) and serological (ELISA and hemagglutination inhibition) assays to detect IAV. Influenza A virus was detected in 15.7% of the sampled pigs (30.6% of herds) in 2010 and in 11.7% (24.2% of herds) in 2011. The percentage of seropositive pigs was 10.6% (16.1% of herds) and 1.4% (3.1% of herds) for each year, respectively. Three pandemic H1N1 and one seasonal human-like H3N2 viruses were isolated. Antibodies against viruses from different genetic clusters were detected. No reassortant strains with swine viruses were detected. The H3N2 virus was closely related to human viruses that circulated in Central America in 2010, distinct to the most recent human seasonal vaccine lineages. Spatial clusters of rRT-PCR positive herds were detected each year by scan statistics. Our results demonstrate circulation of IAV throughout Guatemala and identify commercial farms, animal health status, and age as potential risk factors associated with IAV infection and exposure. Detection of human-origin viruses in pigs suggests a role for humans in the molecular epidemiology of IAV in swine in Guatemala and evidences gaps in local animal and human surveillance. © 2016 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

An analysis of spatial and socio-economic determinants of tuberculosis in Hermosillo, Mexico, 2000-2006.

PubMed

Alvarez-Hernández, G; Lara-Valencia, F; Reyes-Castro, P A; Rascón-Pacheco, R A

2010-06-01

The city of Hermosillo, in Northwest Mexico, has a higher incidence of tuberculosis (TB) than the national average. However, the intra-urban TB distribution, which could limit the effectiveness of preventive strategies and control, is unknown. Using geographic information systems (GIS) and spatial analysis, we characterized the geographical distribution of TB by basic geostatistical area (BGA), and compared it with a social deprivation index. Univariate and bivariate techniques were used to detect risk areas. Globally, TB in the city of Hermosillo is not spatially auto-correlated, but local clusters with high incidence and mortality rates were identified in the northwest, central-east and southwest sections of the city. BGAs with high social deprivation had an excess risk of TB. GIS and spatial analysis are useful tools to detect high TB risk areas in the city of Hermosillo. Such areas may be vulnerable due to low socio-economic status. The study of small geographical areas in urban settings similar to Hermosillo could indicate the best course of action to be taken for TB prevention and control.

Spatial distribution of trachoma cases in the City of Bauru, State of São Paulo, Brazil, detected in 2006: defining key areas for improvement of health resources.

PubMed

Macharelli, Carlos Alberto; Schellini, Silvana Artioli; Opromolla, Paula Araujo; Dalben, Ivete

2013-01-01

The objective of this study was to analyze the spatial behavior of the occurrence of trachoma cases detected in the City of Bauru, State of São Paulo, Brazil, in 2006 in order to use the information collected to set priority areas for optimization of health resources. the trachoma cases identified in 2006 were georeferenced. The data evaluated were: schools where the trachoma cases studied, data from the 2000 Census, census tract, type of housing, water supply conditions, distribution of income and levels of education of household heads. In the Google Earth® software and TerraView® were made descriptive spatial analysis and estimates of the Kernel. Each area was studied by interpolation of the density surfaces exposing events to facilitate to recognize the clusters. Of the 66 cases detected, only one (1.5%) was not a resident of the city's outskirts. A positive association was detected of trachoma cases and the percentage of heads of household with income below three minimum wages and schooling under eight years of education. The recognition of the spatial distribution of trachoma cases coincided with the areas of greatest social inequality in Bauru city. The micro-areas identified are those that should be prioritized in the rationalization of health resources. There is the possibility of using the trachoma cases detected as an indicator of performance of micro priority health programs.

A spatial scan statistic for survival data based on Weibull distribution.

PubMed

Bhatt, Vijaya; Tiwari, Neeraj

2014-05-20

The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

PubMed Central

Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables. PMID:26808311

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

PubMed

Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across provinces. Future research should explore the risk factors that cause spatial correlated structure or high variation of HFMD incidence which can be explained by temperature. When analyzing association between HFMD incidence and climatic variables, spatial heterogeneity among provinces should be evaluated. Moreover, the extra-Poisson multilevel model was capable of modeling the association between overdispersion of HFMD incidence and climatic variables.

Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis.

PubMed

Selemetas, Nikolaos; de Waal, Theo

2015-04-30

Fasciolosis caused by Fasciola hepatica (liver fluke) can cause significant economic and production losses in dairy cow farms. The aim of the current study was to identify important weather and environmental predictors of the exposure risk to liver fluke by detecting clusters of fasciolosis in Ireland. During autumn 2012, bulk-tank milk samples from 4365 dairy farms were collected throughout Ireland. Using an in-house antibody-detection ELISA, the analysis of BTM samples showed that 83% (n=3602) of dairy farms had been exposed to liver fluke. The Getis-Ord Gi* statistic identified 74 high-risk and 130 low-risk significant (P<0.01) clusters of fasciolosis. The low-risk clusters were mostly located in the southern regions of Ireland, whereas the high-risk clusters were mainly situated in the western part. Several climatic variables (monthly and seasonal mean rainfall and temperatures, total wet days and rain days) and environmental datasets (soil types, enhanced vegetation index and normalised difference vegetation index) were used to investigate dissimilarities in the exposure to liver fluke between clusters. Rainfall, total wet days and rain days, and soil type were the significant classes of climatic and environmental variables explaining the differences between significant clusters. A discriminant function analysis was used to predict the exposure risk to liver fluke using 80% of data for modelling and the remaining subset of 20% for post hoc model validation. The most significant predictors of the model risk function were total rainfall in August and September and total wet days. The risk model presented 100% sensitivity and 91% specificity and an accuracy of 95% correctly classified cases. A risk map of exposure to liver fluke was constructed with higher probability of exposure in western and north-western regions. The results of this study identified differences between clusters of fasciolosis in Ireland regarding climatic and environmental variables and detected significant predictors of the exposure risk to liver fluke. Copyright © 2015 Elsevier B.V. All rights reserved.

Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey

PubMed Central

Rosas-Aguirre, Angel; Speybroeck, Niko; Llanos-Cuentas, Alejandro; Rosanas-Urgell, Anna; Carrasco-Escobar, Gabriel; Rodriguez, Hugo; Gamboa, Dionicia; Contreras-Mancilla, Juan; Alava, Freddy; Soares, Irene S.; Remarque, Edmond; D´Alessandro, Umberto; Erhart, Annette

2015-01-01

Background With low and markedly seasonal malaria transmission, increasingly sensitive tools for better stratifying the risk of infection and targeting control interventions are needed. A cross-sectional survey to characterize the current malaria transmission patterns, identify hotspots, and detect recent changes using parasitological and serological measures was conducted in three sites of the Peruvian Amazon. Material and Methods After full census of the study population, 651 participants were interviewed, clinically examined and had a blood sample taken for the detection of malaria parasites (microscopy and PCR) and antibodies against P. vivax (PvMSP119, PvAMA1) and P. falciparum (PfGLURP, PfAMA1) antigens by ELISA. Risk factors for malaria infection (positive PCR) and malaria exposure (seropositivity) were assessed by multivariate survey logistic regression models. Age-specific seroprevalence was analyzed using a reversible catalytic conversion model based on maximum likelihood for generating seroconversion rates (SCR, λ). SaTScan was used to detect spatial clusters of serology-positive individuals within each site. Results The overall parasite prevalence by PCR was low, i.e. 3.9% for P. vivax and 6.7% for P. falciparum, while the seroprevalence was substantially higher, 33.6% for P. vivax and 22.0% for P. falciparum, with major differences between study sites. Age and location (site) were significantly associated with P. vivax exposure; while location, age and outdoor occupation were associated with P. falciparum exposure. P. falciparum seroprevalence curves showed a stable transmission throughout time, while for P. vivax transmission was better described by a model with two SCRs. The spatial analysis identified well-defined clusters of P. falciparum seropositive individuals in two sites, while it detected only a very small cluster of P. vivax exposure. Conclusion The use of a single parasitological and serological malaria survey has proven to be an efficient and accurate method to characterize the species specific heterogeneity in malaria transmission at micro-geographical level as well as to identify recent changes in transmission. PMID:26356311

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600

PubMed Central

Howey, Meghan C. L.; Sullivan, Franklin B.; Tallant, Jason; Kopple, Robert Vande; Palace, Michael W.

2016-01-01

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000–1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications. PMID:27584031

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600.

PubMed

Howey, Meghan C L; Sullivan, Franklin B; Tallant, Jason; Kopple, Robert Vande; Palace, Michael W

2016-01-01

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000-1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications.

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

Environment-based selection effects of Planck clusters

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

Kosyra, R.; Gruen, D.; Seitz, S.

2015-07-24

We investigate whether the large-scale structure environment of galaxy clusters imprints a selection bias on Sunyaev–Zel'dovich (SZ) catalogues. Such a selection effect might be caused by line of sight (LoS) structures that add to the SZ signal or contain point sources that disturb the signal extraction in the SZ survey. We use the Planck PSZ1 union catalogue in the Sloan Digital Sky Survey (SDSS) region as our sample of SZ-selected clusters. We calculate the angular two-point correlation function (2pcf) for physically correlated, foreground and background structure in the RedMaPPer SDSS DR8 catalogue with respect to each cluster. We compare ourmore » results with an optically selected comparison cluster sample and with theoretical predictions. In contrast to the hypothesis of no environment-based selection, we find a mean 2pcf for background structures of -0.049 on scales of ≲40 arcmin, significantly non-zero at ~4σ, which means that Planck clusters are more likely to be detected in regions of low background density. We hypothesize this effect arises either from background estimation in the SZ survey or from radio sources in the background. We estimate the defect in SZ signal caused by this effect to be negligibly small, of the order of ~10 -4 of the signal of a typical Planck detection. Analogously, there are no implications on X-ray mass measurements. However, the environmental dependence has important consequences for weak lensing follow up of Planck galaxy clusters: we predict that projection effects account for half of the mass contained within a 15 arcmin radius of Planck galaxy clusters. We did not detect a background underdensity of CMASS LRGs, which also leaves a spatially varying redshift dependence of the Planck SZ selection function as a possible cause for our findings.« less

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage

USGS Publications Warehouse

Mattsson, Brady J.; Zipkin, Elise F.; Gardner, Beth; Blank, Peter J.; Sauer, John R.; Royle, J. Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage.

PubMed

Mattsson, Brady J; Zipkin, Elise F; Gardner, Beth; Blank, Peter J; Sauer, John R; Royle, J Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Explaining Local-Scale Species Distributions: Relative Contributions of Spatial Autocorrelation and Landscape Heterogeneity for an Avian Assemblage

PubMed Central

Mattsson, Brady J.; Zipkin, Elise F.; Gardner, Beth; Blank, Peter J.; Sauer, John R.; Royle, J. Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition. PMID:23393564

Finding text in color images

NASA Astrophysics Data System (ADS)

Zhou, Jiangying; Lopresti, Daniel P.; Tasdizen, Tolga

1998-04-01

In this paper, we consider the problem of locating and extracting text from WWW images. A previous algorithm based on color clustering and connected components analysis works well as long as the color of each character is relatively uniform and the typography is fairly simple. It breaks down quickly, however, when these assumptions are violated. In this paper, we describe more robust techniques for dealing with this challenging problem. We present an improved color clustering algorithm that measures similarity based on both RGB and spatial proximity. Layout analysis is also incorporated to handle more complex typography. THese changes significantly enhance the performance of our text detection procedure.

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.

Automated thematic mapping and change detection of ERTS-A images. [farmlands, cities, and mountain identification in Utah, Washington, Arizona, and California

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1974-01-01

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Identifying Hot-Spots of Metal Contamination in Campus Dust of Xi’an, China

PubMed Central

Chen, Hao; Lu, Xinwei; Gao, Tianning; Chang, Yuyu

2016-01-01

The concentrations of heavy metals (As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn) in campus dust from kindergartens, elementary schools, middle schools, and universities in the city of Xi’an, China, were determined by X-ray fluorescence spectrometry. The pollution levels and hotspots of metals were analyzed using a geoaccumulation index and Local Moran’s I, an indicator of spatial association, respectively. The dust samples from the campuses had metal concentrations higher than background levels, especially for Pb, Zn, Co, Cu, Cr, and Ba. The pollution assessment indicated that the campus dusts were not contaminated with As, Mn, Ni, or V, were moderately or not contaminated with Ba and Cr and were moderately to strongly contaminated with Co, Cu, Pb, and Zn. Local Moran’s I analysis detected the locations of spatial clusters and outliers and indicated that the pollution with these 10 metals occurred in significant high-high spatial clusters, low-high, or even high-low spatial outliers. As, Cu, Mn, Ni, Pb, V, and Zn had important high-high patterns in the center of Xi’an. The western and southwestern regions of the study area, i.e., areas of old and high-tech industries, have strongly contributed to the Co content in the campus dust. PMID:27271645

Towards a comprehensive knowledge of the star cluster population in the Small Magellanic Cloud

NASA Astrophysics Data System (ADS)

Piatti, A. E.

2018-07-01

The Small Magellanic Cloud (SMC) has recently been found to harbour an increase of more than 200 per cent in its known cluster population. Here, we provide solid evidence that this unprecedented number of clusters could be greatly overestimated. On the one hand, the fully automatic procedure used to identify such an enormous cluster candidate sample did not recover ˜50 per cent, on average, of the known relatively bright clusters located in the SMC main body. On the other hand, the number of new cluster candidates per time unit as a function of time is noticeably different from the intrinsic SMC cluster frequency (CF), which should not be the case if these new detections were genuine physical systems. We found additionally that the SMC CF varies spatially, in such a way that it resembles an outside-in process coupled with the effects of a relatively recent interaction with the Large Magellanic Cloud. By assuming that clusters and field stars share the same formation history, we showed for the first time that the cluster dissolution rate also depends on position in the galaxy. The cluster dissolution becomes higher as the concentration of galaxy mass increases or if external tidal forces are present.

Machine-Learning Inspired Seismic Phase Detection for Aftershocks of the 2008 MW7.9 Wenchuan Earthquake

NASA Astrophysics Data System (ADS)

Zhu, L.; Li, Z.; Li, C.; Wang, B.; Chen, Z.; McClellan, J. H.; Peng, Z.

2017-12-01

Spatial-temporal evolution of aftershocks is important for illumination of earthquake physics and for rapid response of devastative earthquakes. To improve aftershock catalogs of the 2008 MW7.9 Wenchuan earthquake in Sichuan, China, Alibaba cloud and China Earthquake Administration jointly launched a seismological contest in May 2017 [Fang et al., 2017]. This abstract describes how we handle this problem in this competition. We first used Short-Term Average/Long-Term Average (STA/LTA) and Kurtosis function to obtain over 55000 candidate phase picks (P or S). Based on Signal to Noise Ratio (SNR), about 40000 phases (P or S) are selected. So far, these 40000 phases have a hit rate of 40% among the manually picks. The causes include that 1) there exist false picks (neither P nor S); 2) some P and S arrivals are mis-labeled. To improve our results, we correlate the 40000 phases over continuous waveforms to obtain the phases missed by during the first pass. This results in 120,000 events. After constructing an affinity matrix based on the cross-correlation for newly detected phases, subspace clustering methods [Vidal 2011] are applied to group those phases into separated subspaces. Initial results show good agreement between empirical and clustered labels of P phases. Half of the empirical S phases are clustered into the P phase cluster. This may be a combined effect of 1) mislabeling isolated P phases to S phases and 2) clustering errors due to a small incomplete sample pool. Phases that were falsely detected in the initial results can be also teased out. To better characterize P and S phases, our next step is to apply subspace clustering methods directly to the waveforms, instead of using the cross-correlation coefficients of detected phases. After that, supervised learning, e.g., a convolutional neural network, can be employed to improve the pick accuracy. Updated results will be presented at the meeting.

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.

Spatial variations and determinants of infant and under-five mortality in Bangladesh.

PubMed

Gruebner, Oliver; Khan, Mmh; Burkart, Katrin; Lautenbach, Sven; Lakes, Tobia; Krämer, Alexander; Subramanian, S V; Galea, Sandro

2017-09-01

Reducing child mortality is a Sustainable Development Goal yet to be achieved by many low-income countries. We applied a subnational and spatial approach based on publicly available datasets and identified permanent insolvency, urbanicity, and malaria endemicity as factors associated with child mortality. We further detected spatial clusters in the east of Bangladesh and noted Sylhet and Jamalpur as those districts that need immediate attention to reduce child mortality. Our approach is transferable to other regions in comparable settings worldwide and may guide future studies to identify subnational regions in need for public health attention. Our study adds to our understanding where we may intervene to more effectively improve health, particularly among disadvantaged populations. Copyright © 2017 Elsevier Ltd. 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.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

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

Tjong, Harianto; Li, Wenyuan; Kalhor, Reza

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Here, our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm themore » presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.« less

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

DOE PAGES

Tjong, Harianto; Li, Wenyuan; Kalhor, Reza; ...

2016-03-07

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Here, our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm themore » presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.« less

Baryonic dark clusters in galactic halos and their observable consequences

NASA Technical Reports Server (NTRS)

Wasserman, Ira; Salpeter, Edwin E.

1994-01-01

We consider the possibility that approximately 10% of the mass of a typical galaxy halo is in the form of massive (approximately 10(exp 7) solar masses), compact (escape speeds approximately 100 km/s) baryonic clusters made of neutron stars (approximately 10% by mass), black holes (less than or approximately equal to 1%) and brown dwarfs, asteroids, and other low-mass debris (approximately 90%). These general properties are consistent with several different observational and phenomenological constraints on cluster properties subject to the condition that neutron stars comprise approximately 1% of the total halo mass. Such compact, dark clusters could be the sites of a variety of collisional phenomena involving neutron stars. We find that integrated out to the Hubble distance approximately one neutron star-neutron star or neutron star-black hole collision occurs daily. Of order 0.1-1 asteroid-neutron star collisions may also happen daily in the halo of the Milky Way if there is roughly equal cluster mass per logarithmic particle mass interval between asteroids and brown dwarfs. These event rates are comparable to the frequency of gamma-ray burst detections by the Burst and Transient Source Experiment (BATSE) on the Compton Observatory, implying that if dark halo clusters are the sites of most gamma-ray bursts, perhaps approximately 90% of all bursts are extragalactic, but approximately 10% are galactic. It is possible that dark clusters of the kind discussed here could be detected directly by the Infrared Space Observatory (ISO) or Space Infrared Telescope Facility (SIRTF). If the clusters considered in this paper exist, they should produce spatially correlated gravitational microlensing of stars in the Large Magellanic Cloud (LMC). If 10% of the halo is in the form of dark baryonic clusters, and the remaining 90% is in brown dwarfs and other dark objects which are either unclustered or collected into low-mass clusters, then we expect that two events within approximately 1 min of one another are likely to be seen after a total of order 20-30 microlenses have been detected.

Analysis of the spatial and temporal distribution of malaria in an area of Northern Guatemala with seasonal malaria transmission.

PubMed

Malvisi, Lucio; Troisi, Catherine L; Selwyn, Beatrice J

2018-06-23

The risk of malaria infection displays spatial and temporal variability that is likely due to interaction between the physical environment and the human population. In this study, we performed a spatial analysis at three different time points, corresponding to three cross-sectional surveys conducted as part of an insecticide-treated bed nets efficacy study, to reveal patterns of malaria incidence distribution in an area of Northern Guatemala characterized by low malaria endemicity. A thorough understanding of the spatial and temporal patterns of malaria distribution is essential for targeted malaria control programs. Two methods, the local Moran's I and the Getis-Ord G * (d), were used for the analysis, providing two different statistical approaches and allowing for a comparison of results. A distance band of 3.5 km was considered to be the most appropriate distance for the analysis of data based on epidemiological and entomological factors. Incidence rates were higher at the first cross-sectional survey conducted prior to the intervention compared to the following two surveys. Clusters or hot spots of malaria incidence exhibited high spatial and temporal variations. Findings from the two statistics were similar, though the G * (d) detected cold spots using a higher distance band (5.5 km). The high spatial and temporal variability in the distribution of clusters of high malaria incidence seems to be consistent with an area of unstable malaria transmission. In such a context, a strong surveillance system and the use of spatial analysis may be crucial for targeted malaria control activities.

HIGH-RESOLUTION IMAGES OF ORBITAL MOTION IN THE ORION TRAPEZIUM CLUSTER WITH THE LBT AO SYSTEM

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

Close, L. M.; Males, J. R.; Skemer, A.

2012-04-20

The new 8.4 m LBT adaptive secondary AO system, with its novel pyramid wavefront sensor, was used to produce very high Strehl ({approx}> 75% at 2.16 {mu}m) near-infrared narrowband (Br{gamma}: 2.16 {mu}m and [Fe II]: 1.64 {mu}m) images of 47 young ({approx}1 Myr) Orion Trapezium {theta}{sup 1} Ori cluster members. The inner {approx}41 Multiplication-Sign 53'' of the cluster was imaged at spatial resolutions of {approx}0.''050 (at 1.64 {mu}m). A combination of high spatial resolution and high S/N yielded relative binary positions to {approx}0.5 mas accuracies. Including previous speckle data, we analyze a 15 year baseline of high-resolution observations of thismore » cluster. We are now sensitive to relative proper motions of just {approx}0.3 mas yr{sup -1} (0.6 km s{sup -1} at 450 pc); this is a {approx}7 Multiplication-Sign improvement in orbital velocity accuracy compared to previous efforts. We now detect clear orbital motions in the {theta}{sup 1} Ori B{sub 2} B{sub 3} system of 4.9 {+-} 0.3 km s{sup -1} and 7.2 {+-} 0.8 km s{sup -1} in the {theta}{sup 1} Ori A{sub 1} A{sub 2} system (with correlations of P.A. versus time at >99% confidence). All five members of the {theta}{sup 1} Ori B system appear likely a gravitationally bound 'mini-cluster'. The very lowest mass member of the {theta}{sup 1} Ori B system (B{sub 4}; mass {approx}0.2 M{sub Sun }) has, for the first time, a clearly detected motion (at 4.3 {+-} 2.0 km s{sup -1}; correlation = 99.7%) w.r.t. B{sub 1}. However, B{sub 4} is most likely in a long-term unstable (non-hierarchical) orbit and may 'soon' be ejected from this 'mini-cluster'. This 'ejection' process could play a major role in the formation of low-mass stars and brown dwarfs.« less

Herschel-ATLAS/GAMA: SDSS cross-correlation induced by weak lensing

NASA Astrophysics Data System (ADS)

González-Nuevo, J.; Lapi, A.; Negrello, M.; Danese, L.; De Zotti, G.; Amber, S.; Baes, M.; Bland-Hawthorn, J.; Bourne, N.; Brough, S.; Bussmann, R. S.; Cai, Z.-Y.; Cooray, A.; Driver, S. P.; Dunne, L.; Dye, S.; Eales, S.; Ibar, E.; Ivison, R.; Liske, J.; Loveday, J.; Maddox, S.; Michałowski, M. J.; Robotham, A. S. G.; Scott, D.; Smith, M. W. L.; Valiante, E.; Xia, J.-Q.

2014-08-01

We report a highly significant (>10σ) spatial correlation between galaxies with S350 μm ≥ 30 mJy detected in the equatorial fields of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) with estimated redshifts ≳ 1.5, and Sloan Digital Sky Survey (SDSS) or Galaxy And Mass Assembly (GAMA) galaxies at 0.2 ≤ z ≤ 0.6. The significance of the cross-correlation is much higher than those reported so far for samples with non-overlapping redshift distributions selected in other wavebands. Extensive, realistic simulations of clustered sub-mm galaxies amplified by foreground structures confirm that the cross-correlation can be explained by weak gravitational lensing (μ < 2). The simulations also show that the measured amplitude and range of angular scales of the signal are larger than can be accounted for by galaxy-galaxy weak lensing. However, for scales ≲ 2 arcmin, the signal can be reproduced if SDSS/GAMA galaxies act as signposts of galaxy groups/clusters with halo masses in the range 1013.2-1014.5 M⊙. The signal detected on larger scales appears to reflect the clustering of such haloes.

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.

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.

Visceral leishmaniasis in the state of Sao Paulo, Brazil: spatial and space-time analysis

PubMed Central

Cardim, Marisa Furtado Mozini; Guirado, Marluci Monteiro; Dibo, Margareth Regina; Chiaravalloti, Francisco

2016-01-01

ABSTRACT OBJECTIVE To perform both space and space-time evaluations of visceral leishmaniasis in humans in the state of Sao Paulo, Brazil. METHODS The population considered in the study comprised autochthonous cases of visceral leishmaniasis and deaths resulting from it in Sao Paulo, between 1999 and 2013. The analysis considered the western region of the state as its studied area. Thematic maps were created to show visceral leishmaniasis dissemination in humans in the municipality. Spatial analysis tools Kernel and Kernel ratio were used to respectively obtain the distribution of cases and deaths and the distribution of incidence and mortality. Scan statistics were used in order to identify spatial and space-time clusters of cases and deaths. RESULTS The visceral leishmaniasis cases in humans, during the studied period, were observed to occur in the western portion of Sao Paulo, and their territorial extension mainly followed the eastbound course of the Marechal Rondon highway. The incidences were characterized as two sequences of concentric ellipses of decreasing intensities. The first and more intense one was found to have its epicenter in the municipality of Castilho (where the Marechal Rondon highway crosses the border of the state of Mato Grosso do Sul) and the second one in Bauru. Mortality was found to have a similar behavior to incidence. The spatial and space-time clusters of cases were observed to coincide with the two areas of highest incidence. Both the space-time clusters identified, even without coinciding in time, were started three years after the human cases were detected and had the same duration, that is, six years. CONCLUSIONS The expansion of visceral leishmaniasis in Sao Paulo has been taking place in an eastbound direction, focusing on the role of highways, especially Marechal Rondon, in this process. The space-time analysis detected the disease occurred in cycles, in different spaces and time periods. These meetings, if considered, may contribute to the adoption of actions that aim to prevent the disease from spreading throughout the whole territory of São Paulo or to at least reducing its expansion speed. PMID:27533364

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.

Meteor localization via statistical analysis of spatially temporal fluctuations in image sequences

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Šihlík, Jan; Fliegel, Karel

2015-09-01

Meteor detection is one of the most important procedures in astronomical imaging. Meteor path in Earth's atmosphere is traditionally reconstructed from double station video observation system generating 2D image sequences. However, the atmospheric turbulence and other factors cause spatially-temporal fluctuations of image background, which makes the localization of meteor path more difficult. Our approach is based on nonlinear preprocessing of image intensity using Box-Cox and logarithmic transform as its particular case. The transformed image sequences are then differentiated along discrete coordinates to obtain statistical description of sky background fluctuations, which can be modeled by multivariate normal distribution. After verification and hypothesis testing, we use the statistical model for outlier detection. Meanwhile the isolated outlier points are ignored, the compact cluster of outliers indicates the presence of meteoroids after ignition.

Bovine respiratory syncytial virus and bovine coronavirus antibodies in bulk tank milk - risk factors and spatial analysis.

PubMed

Toftaker, Ingrid; Sanchez, Javier; Stokstad, Maria; Nødtvedt, Ane

2016-10-01

Bovine respiratory syncytial virus (BRSV) and bovine coronavirus (BCoV) are considered widespread among cattle in Norway and worldwide. This cross-sectional study was conducted based on antibody-ELISA of bulk tank milk (BTM) from 1347 herds in two neighboring counties in western Norway. The study aims were to determine the seroprevalence at herd level, to evaluate risk factors for BRSV and BCoV seropositivity, and to assess how these factors were associated with the spatial distribution of positive herds. The overall prevalence of BRSV and BCoV positive herds in the region was 46.2% and 72.2%, respectively. Isopleth maps of the prevalence risk distribution showed large differences in prevalence risk across the study area, with the highest prevalence in the northern region. Common risk factors of importance for both viruses were herd size, geographic location, and proximity to neighbors. Seropositivity for one virus was associated with increased odds of seropositivity for the other virus. Purchase of livestock was an additional risk factor for BCoV seropositivity, included in the model as in-degree, which was defined as the number of incoming movements from individual herds, through animal purchase, over a period of five years. Local dependence and the contribution of risk factors to this effect were assessed using the residuals from two logistic regression models for each virus. One model contained only the x- and y- coordinates as predictors, the other had all significant predictors included. Spatial clusters of high values of residuals were detected using the normal model of the spatial scan statistic and visualized on maps. Adjusting for the risk factors in the final models had different impact on the spatial clusters for the two viruses: For BRSV the number of clusters was reduced from six to four, for BCoV the number of clusters remained the same, however the log-likelihood ratios changed notably. This indicates that geographical differences in proximity to neighbors, herd size and animal movements explain some of the spatial clusters of BRSV- and BCoV seropositivity, but far from all. The remaining local dependence in the residuals show that the antibody status of one herd is influenced by the antibody status of its neighbors, indicating the importance of indirect transmission and that increased biosecurity routines might be an important preventive strategy. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach.

PubMed

Stopka, Thomas J; Goulart, Michael A; Meyers, David J; Hutcheson, Marga; Barton, Kerri; Onofrey, Shauna; Church, Daniel; Donahue, Ashley; Chui, Kenneth K H

2017-04-20

Hepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns. We used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots. We identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the "other" race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91). We identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

High-resolution observations of the globular cluster NGC 7099

NASA Astrophysics Data System (ADS)

Sams, Bruce Jones, III

The globular cluster NGC 7099 is a prototypical collapsed core cluster. Through a series of instrumental, observational, and theoretical observations, I have resolved its core structure using a ground based telescope. The core has a radius of 2.15 arcsec when imaged with a V band spatial resolution of 0.35 arcsec. Initial attempts at speckle imaging produced images of inadequate signal to noise and resolution. To explain these results, a new, fully general signal-to-noise model has been developed. It properly accounts for all sources of noise in a speckle observation, including aliasing of high spatial frequencies by inadequate sampling of the image plane. The model, called Full Speckle Noise (FSN), can be used to predict the outcome of any speckle imaging experiment. A new high resolution imaging technique called ACT (Atmospheric Correlation with a Template) was developed to create sharper astronomical images. ACT compensates for image motion due to atmospheric turbulence. ACT is similar to the Shift and Add algorithm, but uses apriori spatial knowledge about the image to further constrain the shifts. In this instance, the final images of NGC 7099 have resolutions of 0.35 arcsec from data taken in 1 arcsec seeing. The PAPA (Precision Analog Photon Address) camera was used to record data. It is subject to errors when imaging cluster cores in a large field of view. The origin of these errors is explained, and several ways to avoid them proposed. New software was created for the PAPA camera to properly take flat field images taken in a large field of view. Absolute photometry measurements of NGC 7099 made with the PAPA camera are accurate to 0.1 magnitude. Luminosity sampling errors dominate surface brightness profiles of the central few arcsec in a collapsed core cluster. These errors set limits on the ultimate spatial accuracy of surface brightness profiles.

Hydraulic fracturing and the Crooked Lake Sequences: Insights gleaned from regional seismic networks

NASA Astrophysics Data System (ADS)

Schultz, Ryan; Stern, Virginia; Novakovic, Mark; Atkinson, Gail; Gu, Yu Jeffrey

2015-04-01

Within central Alberta, Canada, a new sequence of earthquakes has been recognized as of 1 December 2013 in a region of previous seismic quiescence near Crooked Lake, ~30 km west of the town of Fox Creek. We utilize a cross-correlation detection algorithm to detect more than 160 events to the end of 2014, which is temporally distinguished into five subsequences. This observation is corroborated by the uniqueness of waveforms clustered by subsequence. The Crooked Lake Sequences have come under scrutiny due to its strong temporal correlation (>99.99%) to the timing of hydraulic fracturing operations in the Duvernay Formation. We assert that individual subsequences are related to fracturing stimulation and, despite adverse initial station geometry, double-difference techniques allow us to spatially relate each cluster back to a unique horizontal well. Overall, we find that seismicity in the Crooked Lake Sequences is consistent with first-order observations of hydraulic fracturing induced seismicity.

Supra-galactic colour patterns in globular cluster systems

NASA Astrophysics Data System (ADS)

Forte, Juan C.

2017-07-01

An analysis of globular cluster systems associated with galaxies included in the Virgo and Fornax Hubble Space Telescope-Advanced Camera Surveys reveals distinct (g - z) colour modulation patterns. These features appear on composite samples of globular clusters and, most evidently, in galaxies with absolute magnitudes Mg in the range from -20.2 to -19.2. These colour modulations are also detectable on some samples of globular clusters in the central galaxies NGC 1399 and NGC 4486 (and confirmed on data sets obtained with different instruments and photometric systems), as well as in other bright galaxies in these clusters. After discarding field contamination, photometric errors and statistical effects, we conclude that these supra-galactic colour patterns are real and reflect some previously unknown characteristic. These features suggest that the globular cluster formation process was not entirely stochastic but included a fraction of clusters that formed in a rather synchronized fashion over large spatial scales, and in a tentative time lapse of about 1.5 Gy at redshifts z between 2 and 4. We speculate that the putative mechanism leading to that synchronism may be associated with large scale feedback effects connected with violent star-forming events and/or with supermassive black holes.

A Chandra ACIS Study of 30 Doradus. II. X-Ray Point Sources in the Massive Star Cluster R136 and Beyond

NASA Astrophysics Data System (ADS)

Townsley, Leisa K.; Broos, Patrick S.; Feigelson, Eric D.; Garmire, Gordon P.; Getman, Konstantin V.

2006-04-01

We have studied the X-ray point-source population of the 30 Doradus (30 Dor) star-forming complex in the Large Magellanic Cloud using high spatial resolution X-ray images and spatially resolved spectra obtained with the Advanced CCD Imaging Spectrometer (ACIS) on board the Chandra X-Ray Observatory. Here we describe the X-ray sources in a 17'×17' field centered on R136, the massive star cluster at the center of the main 30 Dor nebula. We detect 20 of the 32 Wolf-Rayet stars in the ACIS field. The cluster R136 is resolved at the subarcsecond level into almost 100 X-ray sources, including many typical O3-O5 stars, as well as a few bright X-ray sources previously reported. Over 2 orders of magnitude of scatter in LX is seen among R136 O stars, suggesting that X-ray emission in the most massive stars depends critically on the details of wind properties and the binarity of each system, rather than reflecting the widely reported characteristic value LX/Lbol~=10-7. Such a canonical ratio may exist for single massive stars in R136, but our data are too shallow to confirm this relationship. Through this and future X-ray studies of 30 Dor, the complete life cycle of a massive stellar cluster can be revealed.

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.

A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

2008-03-01

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.

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

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.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

PubMed Central

2011-01-01

Background The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). Methods We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. Conclusions Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies. PMID:21599932

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.

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.

Cross-visit tumor sub-segmentation and registration with outlier rejection for dynamic contrast-enhanced MRI time series data.

PubMed

Buonaccorsi, G A; Rose, C J; O'Connor, J P B; Roberts, C; Watson, Y; Jackson, A; Jayson, G C; Parker, G J M

2010-01-01

Clinical trials of anti-angiogenic and vascular-disrupting agents often use biomarkers derived from DCE-MRI, typically reporting whole-tumor summary statistics and so overlooking spatial parameter variations caused by tissue heterogeneity. We present a data-driven segmentation method comprising tracer-kinetic model-driven registration for motion correction, conversion from MR signal intensity to contrast agent concentration for cross-visit normalization, iterative principal components analysis for imputation of missing data and dimensionality reduction, and statistical outlier detection using the minimum covariance determinant to obtain a robust Mahalanobis distance. After applying these techniques we cluster in the principal components space using k-means. We present results from a clinical trial of a VEGF inhibitor, using time-series data selected because of problems due to motion and outlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.

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.

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

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

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.

Spatial spread of dengue in a non-endemic tropical city in northern Argentina.

PubMed

Gil, José F; Palacios, Maximiliano; Krolewiecki, Alejandro J; Cortada, Pedro; Flores, Rosana; Jaime, Cesar; Arias, Luis; Villalpando, Carlos; Alberti DÁmato, Anahí M; Nasser, Julio R; Aparicio, Juan P

2016-06-01

After more than eighty years dengue reemerged in Argentina in 1997. Since then, the largest epidemic in terms of geographical extent, magnitude and mortality, was recorded in 2009. In this report we analyzed the DEN-1 epidemic spread in Orán, a mid-size city in a non-endemic tropical area in Northern Argentina, and its correlation with demographic and socioeconomic factors. Cases were diagnosed by ELISA between January and June 2009. We applied a space-time and spatial scan statistic under a Poisson model. Possible association between dengue incidence and socio-economic variables was studied with the Spearman correlation test. The epidemic started from an imported case from Bolivia and space-time analysis detected two clusters: one on February and other in April (in the south and the northeast of the city respectively) with risk ratios of 25.24 and 4.07 (p<0.01). Subsequent cases spread widely around the city without significant space-temporal clustering. Maximum values of the entomological indices were observed in January, at the beginning of the epidemic (B=21.96; LH=8.39). No statistically significant association between socioeconomic variables and dengue incidence was found but positive correlation between population size and the number of cases (p<0.05) was detected. Two mechanisms may explain the observed pattern of epidemic spread in this non-endemic tropical city: a) Short range dispersal of mosquitoes and people generates clusters of cases and b) long-distance (within the city) human movement contributes to a quasi-random distribution of cases. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Characterization of the temporal and spatial distribution and reproductive ratio of vesicular stomatitis outbreaks in Mexico in 2008.

PubMed

Arroyo, Montserrat; Perez, Andres M; Rodriguez, Luis L

2011-02-01

To characterize the temporal and spatial distribution and reproductive ratio of vesicular stomatitis (VS) outbreaks reported in Mexico in 2008. Bovine herds in Mexico in which VS outbreaks were officially reported and confirmed from January 1 through December 31, 2008. The Poisson model of the space-time scan statistic was used to identify periods and geographical locations at highest risk for VS in Mexico in 2008. The herd reproductive ratio (R(h)) of the epidemic was computed by use of the doubling-time method. 1 significant space-time cluster of VS was detected in the state of Michoacan from September 4 through December 10, 2008. The temporal extent of the VS outbreaks and the value and pattern of decrease of the R(h) were different in the endemic zone of Tabasco and Chiapas, compared with findings in the region included in the space-time cluster. The large number of VS outbreaks reported in Mexico in 2008 was associated with the spread of the disease from the endemic zone in southern Mexico to areas sporadically affected by the disease. Results suggested that implementation of a surveillance system in the endemic zone of Mexico aimed at early detection of changes in the value of R(h) and space-time clustering of the disease could help predict occurrence of future VS outbreaks originating from this endemic zone. This information will help prevent VS spread into regions of Mexico and neighboring countries that are only sporadically affected by the disease.

Probing Reionization at z >~ 7 with HST's Near-Infrared Grisms

NASA Astrophysics Data System (ADS)

Schmidt, Kasper B.

The epoch of reionization, i.e. the phase transition of the inter-galactic medium from neutral to fully ionized, is essential for our understanding of the evolution of the Universe and the formation of the first stars and galaxies. The Grism Lens-Amplified Survey from Space (GLASS) has obtained spectra of ten thousands of objects in and behind 10 massive galaxy clusters, including the six Hubble Frontier Fields. The grism spectroscopy from GLASS results in hundreds of spectra of z >~ 7 galaxy candidates. Taking advantage of the lensing magnification from the foreground clusters, the GLASS spectra reaches unprecedented depths in the near-infrared with observed flux limits of ~ 5 × 10-18erg/s/cm2 before correcting for the lens magnification. This has resulted in several Lyα detections at z ~ 7 and tight limits on the emission line fluxes for non-detections. From an ensemble of different photometric selections, we have assembled more than 150 z >~ 7 galaxy candidates from six of the ten GLASS clusters. Among these more than 20 objects show emission lines consistent with being Lyα at z >~ 7. The spatial extent of Lyα estimated from a stack of the most promising Lyα emitters at = 7.2 is consistent with the spatial extent of the UV continuum emission. From the stack we obtain upper limits on the emission line ratios between prominent rest-frame UV emission lines, finding that f CIV/f Lyα <~ 0.32 and f CIII]/f Lyα <~ 0.23 in good agreement with values published in the literature.

Stream gradient Hotspot and Cluster Analysis (SL-HCA) for improving the longitudinal profiles metrics

NASA Astrophysics Data System (ADS)

Troiani, Francesco; Piacentini, Daniela; Seta Marta, Della

2016-04-01

Many researches successfully focused on stream longitudinal profiles analysis through Stream Length-gradient (SL) index for detecting, at different spatial scales, either tectonic structures or hillslope processes. The analysis and interpretation of spatial variability of SL values, both at a regional and local scale, is often complicated due to the concomitance of different factors generating SL anomalies, including the bedrock composition. The creation of lithologically-filtered SL maps is often problematic in areas where homogeneously surveyed geological maps, with a sufficient resolution are unavailable. Moreover, both the SL map classification and the unbiased anomaly detection are rather difficult. For instance, which is the best threshold to define the anomalous SL values? Further, is there a minimum along-channel extent of anomalous SL values for objectively defining over-steeped segments on long-profiles? This research investigates the relevance and potential of a new approach based on Hotspot and Cluster Analysis of SL values (SL-HCA) for detecting knickzones on long-profiles at a regional scale and for fine-tuning the interpretation of their geological-geomorphological meaning. We developed this procedure within a 2800 km2-wide area located in the mountainous sector of the Northern Apennines of Italy. The Getis-Ord Gi∗ statistic is applied for the SL-HCA approach. The value of SL, calculated starting from a 5x5 m Digital Elevation Model, is used as weighting factor and the Gi∗ index is calculated for each 50 m-long channel segment for the whole fluvial system. The outcomes indicate that high positive Gi∗ values imply the clustering of SL anomalies, thus the occurrence of knickzones on the stream long-profiles. Results show that high and very high Gi* values (i.e. values beyond two standard deviations from the mean) correlate well with the principal knickzones detected with existent lithologically-filtered SL maps. Field checks and remote sensing analysis conducted on 52 clusters of high and very high Gi* values indicate that mass movement of slope material represents the dominant process producing over-steeped long-profiles along connected streams, whereas the litho-structure accounts for the main anomalies along disconnected steams. Tectonic structures generally provide to the largest clusters. Our results demonstrate that SL-HCA maps have the same potential of lithologically-filtered SL maps for detecting knickzones due to hillslope processes and/or tectonic structures. The reduced-complexity model derived from SL-HCA approach highly improve the readability of the morphometric outcomes, thus the interpretation at a regional scale of the geological-geomorphological meaning of over-steeped segments on long-profiles. SL-HCA maps are useful to investigate and better interpret knickzones within regions poorly covered by geological data and where field surveys are difficult to be performed.

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 patterns of high Aedes aegypti oviposition activity in northwestern Argentina.

PubMed

Estallo, Elizabet Lilia; Más, Guillermo; Vergara-Cid, Carolina; Lanfri, Mario Alberto; Ludueña-Almeida, Francisco; Scavuzzo, Carlos Marcelo; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

2013-01-01

In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005-2007). Spatial autocorrelation was measured with Moran's Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC=0.77), obtaining 99% of sensitivity and 75.29% of specificity. Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

Spatial modelling and mapping of female genital mutilation in Kenya

PubMed Central

2014-01-01

Background Female genital mutilation/cutting (FGM/C) is still prevalent in several communities in Kenya and other areas in Africa, as well as being practiced by some migrants from African countries living in other parts of the world. This study aimed at detecting clustering of FGM/C in Kenya, and identifying those areas within the country where women still intend to continue the practice. A broader goal of the study was to identify geographical areas where the practice continues unabated and where broad intervention strategies need to be introduced. Methods The prevalence of FGM/C was investigated using the 2008 Kenya Demographic and Health Survey (KDHS) data. The 2008 KDHS used a multistage stratified random sampling plan to select women of reproductive age (15–49 years) and asked questions concerning their FGM/C status and their support for the continuation of FGM/C. A spatial scan statistical analysis was carried out using SaTScan™ to test for statistically significant clustering of the practice of FGM/C in the country. The risk of FGM/C was also modelled and mapped using a hierarchical spatial model under the Integrated Nested Laplace approximation approach using the INLA library in R. Results The prevalence of FGM/C stood at 28.2% and an estimated 10.3% of the women interviewed indicated that they supported the continuation of FGM. On the basis of the Deviance Information Criterion (DIC), hierarchical spatial models with spatially structured random effects were found to best fit the data for both response variables considered. Age, region, rural–urban classification, education, marital status, religion, socioeconomic status and media exposure were found to be significantly associated with FGM/C. The current FGM/C status of a woman was also a significant predictor of support for the continuation of FGM/C. Spatial scan statistics confirm FGM clusters in the North-Eastern and South-Western regions of Kenya (p < 0.001). Conclusion This suggests that the fight against FGM/C in Kenya is not yet over. There are still deep cultural and religious beliefs to be addressed in a bid to eradicate the practice. Interventions by government and other stakeholders must address these challenges and target the identified clusters. PMID:24661558

A Wide-Field Photometric Survey for Extratidal Tails Around Five Metal-Poor Globular Clusters in the Galactic Halo

NASA Astrophysics Data System (ADS)

Chun, Sang-Hyun; Kim, Jae-Woo; Sohn, Sangmo T.; Park, Jang-Hyun; Han, Wonyong; Kim, Ho-Il; Lee, Young-Wook; Lee, Myung Gyoon; Lee, Sang-Gak; Sohn, Young-Jong

2010-02-01

Wide-field deep g'r'i' images obtained with the Megacam of the Canada-France-Hawaii Telescope are used to investigate the spatial configuration of stars around five metal-poor globular clusters M15, M30, M53, NGC 5053, and NGC 5466, in a field-of-view ~3°. Applying a mask filtering algorithm to the color-magnitude diagrams of the observed stars, we sorted cluster's member star candidates that are used to examine the characteristics of the spatial stellar distribution surrounding the target clusters. The smoothed surface density maps and the overlaid isodensity contours indicate that all of the five metal-poor globular clusters exhibit strong evidence of extratidal overdensity features over their tidal radii, in the form of extended tidal tails around the clusters. The orientations of the observed extratidal features show signatures of tidal tails tracing the clusters' orbits, inferred from their proper motions, and effects of dynamical interactions with the Galaxy. Our findings include detections of a tidal bridge-like feature and an envelope structure around the pair of globular clusters M53 and NGC 5053. The observed radial surface density profiles of target clusters have a deviation from theoretical King models, for which the profiles show a break at 0.5-0.7rt , extending the overdensity features out to 1.5-2rt . Both radial surface density profiles for different angular sections and azimuthal number density profiles confirm the overdensity features of tidal tails around the five metal-poor globular clusters. Our results add further observational evidence that the observed metal-poor halo globular clusters originate from an accreted satellite system, indicative of the merging scenario of the formation of the Galactic halo. Based on observations carried out at the Canada-France-Hawaii Telescope, operated by the National Research Council of Canada, the Centre National de la Recherche Scientifique de France, and the University of Hawaii. This is part of the Searching for the Galactic Halo project using the CFHT, organized by the Korea Astronomy and Space Science Institute.

Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

DOE PAGES

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

2017-06-06

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

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.

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.

From dust to light: a study of star formation in NGC2264

NASA Astrophysics Data System (ADS)

Teixeira, P. S.

2008-10-01

The goal of this dissertation is to characterize the star formation history of the young cluster NGC2264 using the unique observational capabilities of the Spitzer Space Telescope. The motivation to conduct this study stems from the fact that most stars are formed within clusters, so the formation and evolution of the latter will effect the stellar mass distribution in the field. Detailed observational studies of young stellar clusters are therefore crucial to provide necessary constraints for theoretical models of cloud and cluster formation and evolution. This study also addresses the evolution of circumstellar disks in NGC2264; empirical knowledge of protoplanetary disk evolution is required for the understanding of how planetary systems such as our own form. The first result obtained from this study was both completely new and unexpected. A dense region within NGC2264 was found to be teeming with bright 24 μm Class I protostars; these sources are embedded within dense submillimeter cores and are spatially distributed along dense filamentary fingers of gas and dust that radially converge on a B-type binary Class I source. This cluster of protostars was baptized the "Spokes cluster" and its analysis provided further insight into the role of thermal support during core formation, collapse and fragmentation. The nearest neighbor projected separation distribution of these Class I sources shows a characteristic spacing that is similar to the Jeans length for the region, indicating that the dusty filaments may have undergone thermal fragmentation. The submillimeter cores of the Spokes cluster were observed at 230GHz using the SubMillimeter Array (SMA) and the resulting high resolution (~1.3") continuum observations revealed a dense grouping of 7 Class 0 sources embedded within a particular core, D-MM1 (~20"x20"). The compact sources have masses ranging between 0.4M and 1.2M, and radii of ~600AU. The mean separation of the Class 0 sources within D-MM1 is considerably smaller than the characteristic spacing between the Class I sources in the larger Spokes cluster and is consistent with hierarchical thermal fragmentation of the dense molecular gas in this region. The results obtained by the study of the Spokes cluster show that the spatial substructuring of a cluster or subcluster is correlated with age, i.e., groupings of very young protostars have clearly more concentrated and substructured spatial distributions. The Spokes cluster could thus be one of several building blocks of NGC2264, and will likely expand and disperse its members through the surrounding region, adding to the rest of NGC2264's stellar population.To further explore this scenario, I identified Pre-Main Sequence (PMS) disk bearing sources in the whole region of NGC2264, as surveyed by InfraRed Array Camera (IRAC) analyzing both their spatial distributions and ages. Of the 1404 sources detected in all four IRAC bands, 116 sources were found to have anemic IRAC disks and 217 sources were found to have thick IRAC disks; the disk fraction was calculated to be 37.5%±6.3% and found to be a function of spectral type, increasing for later type sources. I identified 4 candidate sources with transition disks (disks with inner holes), as well as 6 sources with anemic inner disks and thick outer disks that could be the immediate precursors of transition disks. This is a relevant result for it suggests planet formation may be occurring in the inner disk at very early ages. I found that the spatial distribution of the disk-bearing sources was a function of both disk type and amount of reddening. This spatial analysis enabled the identification of three groups of sources, namely, (i) embedded (AV> 3 magnitudes) sources with thick disks, (ii) unembedded sources with thick disks, and (iii) sources with anemic disks. The first group was found to have a median age of 1 Myr and its spatial distribution is highly concentrated and substructured. The second group, (ii), has a median age of 2 Myr and its spatial distribution is less concentrated and substructured than group (i), but more than the group of sources with anemic disks - the spatial distribution of this third group (age ~ 2 Myr) is not substructured and is more distributed, showing no particular peak or concentration. The star formation history of NGC2264 appears to be as follows: the northern region appears to have undergone the first epoch or episode of star formation, while the second epoch is currently occurring in the center (Spokes cluster) and south (near Allen's source). Status: RO

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.

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.

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.

Detecting functional magnetic resonance imaging activation in white matter: Interhemispheric transfer across the corpus callosum

PubMed Central

Mazerolle, Erin L; D'Arcy, Ryan CN; Beyea, Steven D

2008-01-01

Background It is generally believed that activation in functional magnetic resonance imaging (fMRI) is restricted to gray matter. Despite this, a number of studies have reported white matter activation, particularly when the corpus callosum is targeted using interhemispheric transfer tasks. These findings suggest that fMRI signals may not be neatly confined to gray matter tissue. In the current experiment, 4 T fMRI was employed to evaluate whether it is possible to detect white matter activation. We used an interhemispheric transfer task modelled after neurological studies of callosal disconnection. It was hypothesized that white matter activation could be detected using fMRI. Results Both group and individual data were considered. At liberal statistical thresholds (p < 0.005, uncorrected), group level activation was detected in the isthmus of the corpus callosum. This region connects the superior parietal cortices, which have been implicated previously in interhemispheric transfer. At the individual level, five of the 24 subjects (21%) had activation clusters that were located primarily within the corpus callosum. Consistent with the group results, the clusters of all five subjects were located in posterior callosal regions. The signal time courses for these clusters were comparable to those observed for task related gray matter activation. Conclusion The findings support the idea that, despite the inherent challenges, fMRI activation can be detected in the corpus callosum at the individual level. Future work is needed to determine whether the detection of this activation can be improved by utilizing higher spatial resolution, optimizing acquisition parameters, and analyzing the data with tissue specific models of the hemodynamic response. The ability to detect white matter fMRI activation expands the scope of basic and clinical brain mapping research, and provides a new approach for understanding brain connectivity. PMID:18789154

Uncovering the Protostars in Serpens South with ALMA: Continuum Sources and Their Outflow Activity

NASA Astrophysics Data System (ADS)

Plunkett, Adele; Arce, H.; Corder, S.; Dunham, M.

2017-06-01

Serpens South is an appealing protostellar cluster to study due the combination of several factors: (1) a high protostar fraction that shows evidence for very recent and ongoing star formation; (2) iconic clustered star formation along a filamentary structure; (3) its relative proximity within a few hundred parsecs. An effective study requires the sensitivity, angular and spectral resolution, and mapping capabilities recently provided with ALMA. Here we present a multi-faceted data set acquired from Cycles 1 through 3 with ALMA, including maps of continuum sources and molecular outflows throughout the region, as well as a more focused kinematical study of the protostar that is the strongest continuum source at the cluster center. Together these data span spatial scales over several orders of magnitude, allowing us to investigate the outflow-driving sources and the impact of the outflows on the cluster environment. Currently, we focus on the census of protostars in the cluster center, numbering about 20, including low-flux, low-mass sources never before detected in mm-wavelengths and evidence for multiplicity that was previously unresolved.

Hundred Thousand Degree Gas in the Virgo Cluster of Galaxies

NASA Astrophysics Data System (ADS)

Sparks, W. B.; Pringle, J. E.; Carswell, R. F.; Donahue, M.; Martin, R.; Voit, M.; Cracraft, M.; Manset, N.; Hough, J. H.

2012-05-01

The physical relationship between low-excitation gas filaments at ~104 K, seen in optical line emission, and diffuse X-ray emitting coronal gas at ~107 K in the centers of many galaxy clusters is not understood. It is unclear whether the ~104 K filaments have cooled and condensed from the ambient hot (~107 K) medium or have some other origin such as the infall of cold gas in a merger, or the disturbance of an internal cool reservoir of gas by nuclear activity. Observations of gas at intermediate temperatures (~105-106 K) can potentially reveal whether the central massive galaxies are gaining cool gas through condensation or losing it through conductive evaporation and hence identify plausible scenarios for transport processes in galaxy cluster gas. Here we present spectroscopic detection of ~105 K gas spatially associated with the Hα filaments in a central cluster galaxy, M87, in the Virgo Cluster. The measured emission-line fluxes from triply ionized carbon (C IV 1549 Å) and singly ionized helium (He II 1640 Å) are consistent with a model in which thermal conduction determines the interaction between hot and cold phases.

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.

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

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

Mapping and modeling the urban landscape in Bangkok, Thailand: Physical-spectral-spatial relations of population-environmental interactions

NASA Astrophysics Data System (ADS)

Shao, Yang

This research focuses on the application of remote sensing, geographic information systems, statistical modeling, and spatial analysis to examine the dynamics of urban land cover, urban structure, and population-environment interactions in Bangkok, Thailand, with an emphasis on rural-to-urban migration from rural Nang Rong District, Northeast Thailand to the primate city of Bangkok. The dissertation consists of four main sections: (1) development of remote sensing image classification and change-detection methods for characterizing imperviousness for Bangkok, Thailand from 1993-2002; (2) development of 3-D urban mapping methods, using high spatial resolution IKONOS satellite images, to assess high-rises and other urban structures; (3) assessment of urban spatial structure from 2-D and 3-D perspectives; and (4) an analysis of the spatial clustering of migrants from Nang Rong District in Bangkok and the neighborhood environments of migrants' locations. Techniques are developed to improve the accuracy of the neural network classification approach for the analysis of remote sensing data, with an emphasis on the spectral unmixing problem. The 3-D building heights are derived using the shadow information on the high-resolution IKONOS image. The results from the 2-D and 3-D mapping are further examined to assess urban structure and urban feature identification. This research contributes to image processing of remotely-sensed images and urban studies. The rural-urban migration process and migrants' settlement patterns are examined using spatial statistics, GIS, and remote sensing perspectives. The results show that migrants' spatial clustering in urban space is associated with the source village and a number of socio-demographic variables. In addition, the migrants' neighborhood environments in urban setting are modeled using a set of geographic and socio-demographic variables, and the results are scale-dependent.

A Spatial Analysis of the Potato Cyst Nematode Globodera pallida in Idaho.

PubMed

Dandurand, Louise-Marie; Contina, Jean Bertrand; Knudsen, Guy R

2018-03-13

The potato cyst nematode (PCN), Globodera pallida, is a globally regulated and quarantine potato pest. It was detected for the first time in the U.S. in the state of Idaho in 2006. A spatial analysis was performed to: (i) understand the spatial arrangement of PCN infested fields in southern Idaho using spatial point pattern analysis; and (ii) evaluate the potential threat of PCN for entry to new areas using spatial interpolation techniques. Data point locations, cyst numbers and egg viability values for each infested field were collected by USDA-APHIS during 2006-2014. Results showed the presence of spatially clustered PCN infested fields (P = 0.003). We determined that the spread of PCN grew in diameter from the original center of infestation toward the southwest as an ellipsoidal-shaped cluster. Based on the aggregated spatial pattern of distribution and the low extent level of PCN infested fields in southern Idaho, we determined that PCN spread followed a contagion effect scenario, where nearby infested fields contributed to the infestation of new fields, probably through soil contaminated agricultural equipment or tubers. We determined that the recent PCN presence in southern Idaho is unlikely to be associated with new PCN entry from outside the state of Idaho. The relative aggregation of PCN infested fields, the low number of cysts recovered, and the low values in egg viability facilitate quarantine activities and confine this pest to a small area, which, in 2017, is estimated to be 1,233 hectares. The tools and methods provided in this study should facilitate comprehensive approaches to improve PCN control and eradication programs as well as to raise public awareness about this economically important potato pest.

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

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.

Extracting the regional common-mode component of GPS station position time series from dense continuous network

NASA Astrophysics Data System (ADS)

Tian, Yunfeng; Shen, Zheng-Kang

2016-02-01

We develop a spatial filtering method to remove random noise and extract the spatially correlated transients (i.e., common-mode component (CMC)) that deviate from zero mean over the span of detrended position time series of a continuous Global Positioning System (CGPS) network. The technique utilizes a weighting scheme that incorporates two factors—distances between neighboring sites and their correlations of long-term residual position time series. We use a grid search algorithm to find the optimal thresholds for deriving the CMC that minimizes the root-mean-square (RMS) of the filtered residual position time series. Comparing to the principal component analysis technique, our method achieves better (>13% on average) reduction of residual position scatters for the CGPS stations in western North America, eliminating regional transients of all spatial scales. It also has advantages in data manipulation: less intervention and applicable to a dense network of any spatial extent. Our method can also be used to detect CMC irrespective of its origins (i.e., tectonic or nontectonic), if such signals are of particular interests for further study. By varying the filtering distance range, the long-range CMC related to atmospheric disturbance can be filtered out, uncovering CMC associated with transient tectonic deformation. A correlation-based clustering algorithm is adopted to identify stations cluster that share the common regional transient characteristics.

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.

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.

Research on Quantum Algorithms at the Institute for Quantum Information

DTIC Science & Technology

2009-10-17

accuracy threshold theorem for the one-way quantum computer. Their proof is based on a novel scheme, in which a noisy cluster state in three spatial...detected. The proof applies to independent stochastic noise but (in contrast to proofs of the quantum accuracy threshold theorem based on concatenated...proved quantum threshold theorems for long-range correlated non-Markovian noise, for leakage faults, for the one-way quantum computer, for postselected

Determination of clusters and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Brazil

PubMed Central

Herbinger, Karl-Heinz; Cerutti Junior, Crispim; Malta Romano, Camila; de Souza Areias Cabidelle, Aline; Fröschl, Günter

2017-01-01

Dengue occurrence is partially influenced by the immune status of the population. Consequently, the introduction of a new Dengue virus serotype can trigger explosive epidemics in susceptible populations. The determination of clusters in this scenario can help to identify hotspots and understand the disease dispersion regardless of the influence of the population herd immunity. The present study evaluated the pattern and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Espírito Santo state, Brazil. Data on 18,861 dengue cases reported in Vitória from September 2012 to June 2013 were included in the study. The analysis of spatial variation in temporal trend was performed to detect clusters that were compared by their respective relative risk, house index, population density, and income in an ecological study. Overall, 11 clusters were detected. The time trend increase of dengue incidence in the overall study population was 636%. The five clusters that showed a lower time trend increase than the overall population presented a higher incidence in the beginning of the epidemic and, compared to the six clusters with higher time trend increase, they presented higher relative risk for their inhabitants to acquire dengue infection (P-value = 0.02) and a lower income (P-value <0.01). House index and population density did not differ between the clusters. Early increase of dengue incidence and higher relative risk for acquiring dengue infection were favored in low-income areas. Preventive actions and improvement of infrastructure in low-income areas should be prioritized in order to diminish the magnitude of dengue dispersion after the introduction of a new serotype. PMID:28388694

Geomorphological activity at a rock glacier front detected with a 3D density-based clustering algorithm

NASA Astrophysics Data System (ADS)

Micheletti, Natan; Tonini, Marj; Lane, Stuart N.

2017-02-01

Acquisition of high density point clouds using terrestrial laser scanners (TLSs) has become commonplace in geomorphic science. The derived point clouds are often interpolated onto regular grids and the grids compared to detect change (i.e. erosion and deposition/advancement movements). This procedure is necessary for some applications (e.g. digital terrain analysis), but it inevitably leads to a certain loss of potentially valuable information contained within the point clouds. In the present study, an alternative methodology for geomorphological analysis and feature detection from point clouds is proposed. It rests on the use of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), applied to TLS data for a rock glacier front slope in the Swiss Alps. The proposed methods allowed the detection and isolation of movements directly from point clouds which yield to accuracies in the following computation of volumes that depend only on the actual registered distance between points. We demonstrated that these values are more conservative than volumes computed with the traditional DEM comparison. The results are illustrated for the summer of 2015, a season of enhanced geomorphic activity associated with exceptionally high temperatures.

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

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.

Analysis of the Seismic Activity During the Preparatory Phase of the Mw 8.2 Iquique Earthquake, Chile 2014

NASA Astrophysics Data System (ADS)

Aden-Antoniow, F.; Satriano, C.; Poiata, N.; Bernard, P.; Vilotte, J. P.; Aissaoui, E. M.; Ruiz, S.; Schurr, B.; Sobiesiak, M.

2015-12-01

The 2014 Iquique seismic crisis, culminating with the main Mw 8.2 Iquique earthquake (Chile), 1st of April 2014, and the largest Mw 7.7 aftershock, 3rd of April, highlighted a complex unlocking of the North Chile subduction interface. Indeed, during many months preceding this event, at least three large seismic clusters have been observed, in July 2013, in January and in March 2014. Their location and final migration towards the mainshock rupture area represents the main motivation of this work.We built a new, more complete catalogue for the period over December 2013 to March 2014 in Northern Chile, using a new automated array method for earthquake detection and location [Poiata et al. 2015]. With the data-set provided by the IPOC and ILN networks, we detected an average of 8000 events per month, forty times more than the catalogue produced by Centro Sismologico National del Chile. The new catalogue decreases the magnitude of completeness by more than two units, from 3.3 to 1.2. We observe two shallow clusters offshore of the cities of Iquique and Pisagua in January 2014, and a strong one covering the rupture zone of Mw 8.2 mainshock in March. A spatial-temporal statistical analysis of these three clusters allows us to better characterize the whole preparatory phase. We interpret our results in light of the location, timing and energy of several aseismic slip events, evidenced by Boudin et al. [AGU 2014], which coincide with the seismic clusters. We propose that the preparatory phase of the Iquique earthquake consists of a complex interplay of seismic and aseismic slip along the subduction surface. Furthermore, our analysis raises new questions regarding the complex slip during the Mw 7.7 aftershock, and the spatial variation of the effective coupling along the subduction interface, imaged by GPS studies, suggesting new research direction that will be outlined.

Patterning ecological risk of pesticide contamination at the river basin scale.

PubMed

Faggiano, Leslie; de Zwart, Dick; García-Berthou, Emili; Lek, Sovan; Gevrey, Muriel

2010-05-01

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale. Copyright 2010 Elsevier B.V. All rights reserved.

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.

Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters

NASA Astrophysics Data System (ADS)

Masullo, Alessandro; Theunissen, Raf

2016-03-01

The universal outlier detection scheme (Westerweel and Scarano in Exp Fluids 39:1096-1100, 2005) and the distance-weighted universal outlier detection scheme for unstructured data (Duncan et al. in Meas Sci Technol 21:057002, 2010) are the most common PIV data validation routines. However, such techniques rely on a spatial comparison of each vector with those in a fixed-size neighbourhood and their performance subsequently suffers in the presence of clusters of outliers. This paper proposes an advancement to render outlier detection more robust while reducing the probability of mistakenly invalidating correct vectors. Velocity fields undergo a preliminary evaluation in terms of local coherency, which parametrises the extent of the neighbourhood with which each vector will be compared subsequently. Such adaptivity is shown to reduce the number of undetected outliers, even when implemented in the afore validation schemes. In addition, the authors present an alternative residual definition considering vector magnitude and angle adopting a modified Gaussian-weighted distance-based averaging median. This procedure is able to adapt the degree of acceptable background fluctuations in velocity to the local displacement magnitude. The traditional, extended and recommended validation methods are numerically assessed on the basis of flow fields from an isolated vortex, a turbulent channel flow and a DNS simulation of forced isotropic turbulence. The resulting validation method is adaptive, requires no user-defined parameters and is demonstrated to yield the best performances in terms of outlier under- and over-detection. Finally, the novel validation routine is applied to the PIV analysis of experimental studies focused on the near wake behind a porous disc and on a supersonic jet, illustrating the potential gains in spatial resolution and accuracy.

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.

Why not? Understanding the spatial clustering of private facility-based delivery and financial reasons for homebirths in Nigeria.

PubMed

Wong, Kerry L M; Radovich, Emma; Owolabi, Onikepe O; Campbell, Oona M R; Brady, Oliver J; Lynch, Caroline A; Benova, Lenka

2018-06-01

In Nigeria, the provision of public and private healthcare vary geographically, contributing to variations in one's healthcare surroundings across space. Facility-based delivery (FBD) is also spatially heterogeneous. Levels of FBD and private FBD are significantly lower for women in certain south-eastern and northern regions. The potential influence of childbirth services frequented by the community on individual's barriers to healthcare utilization is under-studied, possibly due to the lack of suitable data. Using individual-level data, we present a novel analytical approach to examine the relationship between women's reasons for homebirth and community-level, health-seeking surroundings. We aim to assess the extent to which cost or finance acts as a barrier for FBD across geographic areas with varying levels of private FBD in Nigeria. The most recent live births of 20,467 women were georeferenced to 889 locations in the 2013 Nigeria Demographic and Health Survey. Using these locations as the analytical unit, spatial clusters of high/low private FBD were detected with Kulldorff statistics in the SatScan software package. We then obtained the predicted percentages of women who self-reported financial reasons for homebirth from an adjusted generalized linear model for these clusters. Overall private FBD was 13.6% (95%CI = 11.9,15.5). We found ten clusters of low private FBD (average level: 0.8, 95%CI = 0.8,0.8) and seven clusters of high private FBD (average level: 37.9, 95%CI = 37.6,38.2). Clusters of low private FBD were primarily located in the north, and the Bayelsa and Cross River States. Financial barrier was associated with high private FBD at the cluster level - 10% increase in private FBD was associated with + 1.94% (95%CI = 1.69,2.18) in nonusers citing cost as a reason for homebirth. In communities where private FBD is common, women who stay home for childbirth might have mild increased difficulties in gaining effective access to public care, or face an overriding preference to use private services, among other potential factors. The analytical approach presented in this study enables further research of the differentials in individuals' reasons for service non-uptake across varying contexts of healthcare surroundings. This will help better devise context-specific strategies to improve health service utilization in resource-scarce settings.

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.

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.

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi* statistic

PubMed Central

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-01-01

Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century. PMID:16566830

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906-1909: evaluating local clustering with the Gi* statistic.

PubMed

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-03-27

To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May - 31 October 1906 - 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.

Methods for Monitoring the Detection of Multi-Temporal Land Use Change Through the Classification of Urban Areas

NASA Astrophysics Data System (ADS)

Alhaddad, B. I.; Burns, M. C.; Roca, J.

2011-08-01

Urban areas are complicated due to the mix of man-made features and natural features. A higher level of structural information plays an important role in land cover/use classification of urban areas. Additional spatial indicators have to be extracted based on structural analysis in order to understand and identify spatial patterns or the spatial organization of features, especially for man-made feature. It's very difficult to extract such spatial patterns by using only classification approaches. Clusters of urban patterns which are integral parts of other uses may be difficult to identify. A lot of public resources have been directed towards seeking to develop a standardized classification system and to provide as much compatibility as possible to ensure the widespread use of such categorized data obtained from remote sensor sources. In this paper different methods applied to understand the change in the land use areas by understanding and monitoring the change in urban areas and as its hard to apply those methods to classification results for high elements quantities, dusts and scratches (Roca and Alhaddad, 2005). This paper focuses on a methodology developed based relation between urban elements and how to join this elements in zones or clusters have commune behaviours such as form, pattern, size. The main objective is to convert urban class category in to various structure densities depend on conjunction of pixel and shortest distance between them, Delaunay triangulation has been widely used in spatial analysis and spatial modelling. To identify these different zones, a spatial density-based clustering technique was adopted. In highly urban zones, the spatial density of the pixels is high, while in sparsely areas the density of points is much lower. Once the groups of pixels are identified, the calculation of the boundaries of the areas containing each group of pixels defines the new regions indicate the different contains inside such as high or low urban areas. Multi-temporal datasets from 1986, 1995 and 2004 used to urban region centroid to be our reference in this study which allow us to follow the urban movement, increase and decrease by the time. Kernel Density function used to Calculates urban magnitude, Voronoi algorithm is proposed for deriving explicit boundaries between objects units. To test the approach, we selected a site in a suburban area in Barcelona Municipality, the Spain.

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.

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

Spatial clustering of fatal, and non-fatal, suicide in new South Wales, Australia: implications for evidence-based prevention.

PubMed

Torok, Michelle; Konings, Paul; Batterham, Philip J; Christensen, Helen

2017-10-06

Rates of suicide appear to be increasing, indicating a critical need for more effective prevention initiatives. To increase the efficacy of future prevention initiatives, we examined the spatial distribution of suicide deaths and suicide attempts in New South Wales (NSW), Australia, to identify where high incidence 'suicide clusters' were occurring. Such clusters represent candidate regions where intervention is critically needed, and likely to have the greatest impact, thus providing an evidence-base for the targeted prioritisation of resources. Analysis is based on official suicide mortality statistics for NSW, provided by the Australian Bureau of Statistics, and hospital separations for non-fatal intentional self-harm, provided through the NSW Health Admitted Patient Data Collection at a Statistical Area 2 (SA2) geography. Geographical Information System (GIS) techniques were applied to detect suicide clusters occurring between 2005 and 2013 (aggregated), for persons aged over 5 years. The final dataset contained 5466 mortality and 86,017 non-fatal intentional self-harm cases. In total, 25 Local Government Areas were identified as primary or secondary likely candidate regions for intervention. Together, these regions contained approximately 200 SA2 level suicide clusters, which represented 46% (n = 39,869) of hospital separations and 43% (n = 2330) of suicide deaths between 2005 and 2013. These clusters primarily converged on the Eastern coastal fringe of NSW. Crude rates of suicide deaths and intentional self-harm differed at the Local Government Areas (LGA) level in NSW. There was a tendency for primary suicide clusters to occur within metropolitan and coastal regions, rather than rural areas. The findings demonstrate the importance of taking geographical variation of suicidal behaviour into account, prior to development and implementation of prevention initiatives, so that such initiatives can target key problem areas where they are likely to have maximal impact.

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

PubMed Central

Protopapadakis, Eftychios; Doulamis, Anastasios; Doulamis, Nikolaos; Dres, Dimitrios; Bimpas, Matthaios

2017-01-01

Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology's performance. PMID:29312449

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.

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)

Hundreds of new cluster candidates in the VISTA Variables in the Vía Láctea survey DR1

NASA Astrophysics Data System (ADS)

Barbá, R. H.; Roman-Lopes, A.; Nilo Castellón, J. L.; Firpo, V.; Minniti, D.; Lucas, P.; Emerson, J. P.; Hempel, M.; Soto, M.; Saito, R. K.

2015-09-01

Context. VISTA variables in the Vía Láctea is an ESO Public survey dedicated to scanning the bulge and an adjacent portion of the Galactic disk in the fourth quadrant using the VISTA telescope and its near-infrared camera VIRCAM. One of the leading goals of the VVV survey is to contribute to knowledge of the star cluster population of the Milky Way. Aims: To improve the census of Galactic star clusters, we performed a systematic and careful scan of the JHKs images of the Galactic plane section of the VVV survey. Methods: Our detection procedure is based on a combination of stellar density maps and visual inspection of promising features in the J-, H-, and KS-band images. The material examined are VVV JHKS color-composite images corresponding to Data Release 1 of VVV. Results: We report the discovery of 493 new infrared star cluster candidates. The analysis of the spatial distribution show that the clusters are very concentrated in the Galactic plane, presenting some local maxima around the position of large star-forming complexes, such as G305, RCW 95, and RCW 106. The vast majority of the new star cluster candidates are quite compact and generally surrounded by bright and/or dark nebulosities. IRAS point sources are associated with 59% of the sample, while 88% are associated with MSX point sources. GLIMPSE 8 μm images of the cluster candidates show a variety of morphologies, with 292 clusters dominated by knotty sources, while 361 clusters show some kind of nebulosity in this wavelength regime. Spatial cross-correlation with young stellar objects, masers, and extended green-object catalogs suggest that a large sample of the new cluster candidates are extremely young. In particular, 104 star clusters associated with methanol masers are excellent candidates for ongoing massive star formation. Also, there is a special set of sixteen cluster candidates that present clear signposts of star-forming activity having associated simultaneosly dark nebulae, young stellar objects, extended green objects, and masers. Full Tables 1-3 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/581/A120

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…

Evaluation of biomolecular distributions in rat brain tissues by means of ToF-SIMS using a continuous beam of Ar clusters.

PubMed

Nakano, Shusuke; Yokoyama, Yuta; Aoyagi, Satoka; Himi, Naoyuki; Fletcher, John S; Lockyer, Nicholas P; Henderson, Alex; Vickerman, John C

2016-06-08

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides detailed chemical structure information and high spatial resolution images. Therefore, ToF-SIMS is useful for studying biological phenomena such as ischemia. In this study, in order to evaluate cerebral microinfarction, the distribution of biomolecules generated by ischemia was measured with ToF-SIMS. ToF-SIMS data sets were analyzed by means of multivariate analysis for interpreting complex samples containing unknown information and to obtain biomolecular mapping indicated by fragment ions from the target biomolecules. Using conventional ToF-SIMS (primary ion source: Bi cluster ion), it is difficult to detect secondary ions beyond approximately 1000 u. Moreover, the intensity of secondary ions related to biomolecules is not always high enough for imaging because of low concentration even if the masses are lower than 1000 u. However, for the observation of biomolecular distributions in tissues, it is important to detect low amounts of biological molecules from a particular area of tissue. Rat brain tissue samples were measured with ToF-SIMS (J105, Ionoptika, Ltd., Chandlers Ford, UK), using a continuous beam of Ar clusters as a primary ion source. ToF-SIMS with Ar clusters efficiently detects secondary ions related to biomolecules and larger molecules. Molecules detected by ToF-SIMS were examined by analyzing ToF-SIMS data using multivariate analysis. Microspheres (45 μm diameter) were injected into the rat unilateral internal carotid artery (MS rat) to cause cerebral microinfarction. The rat brain was sliced and then measured with ToF-SIMS. The brain samples of a normal rat and the MS rat were examined to find specific secondary ions related to important biomolecules, and then the difference between them was investigated. Finally, specific secondary ions were found around vessels incorporating microspheres in the MS rat. The results suggest that important biomolecules related to cerebral microinfarction can be detected by ToF-SIMS.

A {sup 13}CO Detection in a Brightest Cluster Galaxy

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

Vantyghem, A. N.; McNamara, B. R.; Hogan, M. T.

We present ALMA Cycle 4 observations of CO(1-0), CO(3-2), and {sup 13}CO(3-2) line emission in the brightest cluster galaxy (BCG) of RXJ0821+0752. This is one of the first detections of {sup 13}CO line emission in a galaxy cluster. Half of the CO(3-2) line emission originates from two clumps of molecular gas that are spatially offset from the galactic center. These clumps are surrounded by diffuse emission that extends 8 kpc in length. The detected {sup 13}CO emission is confined entirely to the two bright clumps, with any emission outside of this region lying below our detection threshold. Two distinct velocitymore » components with similar integrated fluxes are detected in the {sup 12}CO spectra. The narrower component (60 km s{sup −1} FWHM) is consistent in both velocity centroid and linewidth with {sup 13}CO(3-2) emission, while the broader (130–160 km s{sup −1}), slightly blueshifted wing has no associated {sup 13}CO(3-2) emission. A simple local thermodynamic model indicates that the {sup 13}CO emission traces 2.1 × 10{sup 9} M {sub ⊙} of molecular gas. Isolating the {sup 12}CO velocity component that accompanies the {sup 13}CO emission yields a CO-to-H{sub 2} conversion factor of α {sub CO} = 2.3 M {sub ⊙} (K km s{sup −1}){sup −1}, which is a factor of two lower than the Galactic value. Adopting the Galactic CO-to-H{sub 2} conversion factor in BCGs may therefore overestimate their molecular gas masses by a factor of two. This is within the object-to-object scatter from extragalactic sources, so calibrations in a larger sample of clusters are necessary in order to confirm a sub-Galactic conversion factor.« less

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

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

Multi-Scale Clustering of Lyme Disease Risk at the Expanding Leading Edge of the Range of Ixodes scapularis in Canada.

PubMed

Ripoche, Marion; Lindsay, Leslie Robbin; Ludwig, Antoinette; Ogden, Nicholas H; Thivierge, Karine; Leighton, Patrick A

2018-03-27

Since its detection in Canada in the early 1990s, Ixodes scapularis , the primary tick vector of Lyme disease in eastern North America, has continued to expand northward. Estimates of the tick's broad-scale distribution are useful for tracking the extent of the Lyme disease risk zone; however, tick distribution may vary widely within this zone. Here, we investigated I. scapularis nymph distribution at three spatial scales across the Lyme disease emergence zone in southern Quebec, Canada. We collected ticks and compared the nymph densities among different woodlands and different plots and transects within the same woodland. Hot spot analysis highlighted significant nymph clustering at each spatial scale. In regression models, nymph abundance was associated with litter depth, humidity, and elevation, which contribute to a suitable habitat for ticks, but also with the distance from the trail and the type of trail, which could be linked to host distribution and human disturbance. Accounting for this heterogeneous nymph distribution at a fine spatial scale could help improve Lyme disease management strategies but also help people to understand the risk variation around them and to adopt appropriate behaviors, such as staying on the trail in infested parks to limit their exposure to the vector and associated pathogens.

Multi-Scale Clustering of Lyme Disease Risk at the Expanding Leading Edge of the Range of Ixodes scapularis in Canada

PubMed Central

Lindsay, Leslie Robbin; Ludwig, Antoinette; Ogden, Nicholas H.; Thivierge, Karine; Leighton, Patrick A.

2018-01-01

Since its detection in Canada in the early 1990s, Ixodes scapularis, the primary tick vector of Lyme disease in eastern North America, has continued to expand northward. Estimates of the tick’s broad-scale distribution are useful for tracking the extent of the Lyme disease risk zone; however, tick distribution may vary widely within this zone. Here, we investigated I. scapularis nymph distribution at three spatial scales across the Lyme disease emergence zone in southern Quebec, Canada. We collected ticks and compared the nymph densities among different woodlands and different plots and transects within the same woodland. Hot spot analysis highlighted significant nymph clustering at each spatial scale. In regression models, nymph abundance was associated with litter depth, humidity, and elevation, which contribute to a suitable habitat for ticks, but also with the distance from the trail and the type of trail, which could be linked to host distribution and human disturbance. Accounting for this heterogeneous nymph distribution at a fine spatial scale could help improve Lyme disease management strategies but also help people to understand the risk variation around them and to adopt appropriate behaviors, such as staying on the trail in infested parks to limit their exposure to the vector and associated pathogens. PMID:29584627

Comparative study of icy patches on comet nuclei

NASA Astrophysics Data System (ADS)

Oklay, Nilda; Pommerol, Antoine; Barucci, Maria Antonietta; Sunshine, Jessica; Sierks, Holger; Pajola, Maurizio

2016-07-01

Cometary missions Deep Impact, EPOXI and Rosetta investigated the nuclei of comets 9P/Tempel 1, 103P/Hartley 2 and 67P/Churyumov-Gerasimenko respectively. Bright patches were observed on the surfaces of each of these three comets [1-5]. Of these, the surface of 67P is mapped at the highest spatial resolution via narrow angle camera (NAC) of the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS, [6]) on board the Rosetta spacecraft. OSIRIS NAC is equipped with twelve filters covering the wavelength range of 250 nm to 1000 nm. Various filters combinations are used during surface mapping. With high spatial resolution data of comet 67P, three types of bright features were detected on the comet surface: Clustered, isolated and bright boulders [2]. In the visible spectral range, clustered bright features on comet 67P display bluer spectral slopes than the average surface [2, 4] while isolated bright features on comet 67P have flat spectra [4]. Icy patches observed on the surface of comets 9P and 103P display bluer spectral slopes than the average surface [1, 5]. Clustered and isolated bright features are blue in the RGB composites generated by using the images taken in NIR, visible and NUV wavelengths [2, 4]. This is valid for the icy patches observed on comets 9P and 103P [1, 5]. Spectroscopic observations of bright patches on comets 9P and 103P confirmed the existence of water [1, 5]. There were more than a hundred of bright features detected on the northern hemisphere of comet 67P [2]. Analysis of those features from both multispectral data and spectroscopic data is an ongoing work. Water ice is detected in eight of the bright features so far [7]. Additionally, spectroscopic observations of two clustered bright features on the surface of comet 67P revealed the existence of water ice [3]. The spectral properties of one of the icy patches were studied by [4] using OSIRIS NAC images and compared with the spectral properties of the active regions observed on comet 67P. Additionally jets rising from the same clustered bright feature were detected visually [4]. We analyzed bright patches on the surface of comets 9P, 103P and 67P using multispectral data obtained by the high-resolution instrument (HRI), medium- resolution instrument (MRI) and OSIRIS NAC using various spectral analysis techniques. Clustered bright features on comet 67P have similar visible spectra to the bright patches on comets 9P and 103P. The comparison of the bright patches includes the published results of the IR spectra. References: [1] Sunshine et al., 2006, Science, 311, 1453 [2] Pommerol et al., 2015, A&A, 583, A25 [3] Filacchione et al., 2016, Nature, 529, 368-372 [4] Oklay et al., 2016, A&A, 586, A80 [5] Sunshine et al. 2012, ACM [6] Keller et al., 2007, Space Sci. Rev., 128, 433 [7] Barucci et al., 2016, COSPAR, B04

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.

AzTEC Millimetre Survey of the COSMOS field - II. Source count overdensity and correlations with large-scale structure

NASA Astrophysics Data System (ADS)

Austermann, J. E.; Aretxaga, I.; Hughes, D. H.; Kang, Y.; Kim, S.; Lowenthal, J. D.; Perera, T. A.; Sanders, D. B.; Scott, K. S.; Scoville, N.; Wilson, G. W.; Yun, M. S.

2009-03-01

We report an overdensity of bright submillimetre galaxies (SMGs) in the 0.15 deg2 AzTEC/COSMOS survey and a spatial correlation between the SMGs and the optical-IR galaxy density at z <~ 1.1. This portion of the COSMOS field shows a ~3σ overdensity of robust SMG detections when compared to a background, or `blank-field', population model that is consistent with SMG surveys of fields with no extragalactic bias. The SMG overdensity is most significant in the number of very bright detections (14 sources with measured fluxes S1.1mm > 6 mJy), which is entirely incompatible with sample variance within our adopted blank-field number densities and infers an overdensity significance of >> 4σ. We find that the overdensity and spatial correlation to optical-IR galaxy density are most consistent with lensing of a background SMG population by foreground mass structures along the line of sight, rather than physical association of the SMGs with the z <~ 1.1 galaxies/clusters. The SMG positions are only weakly correlated with weak-lensing maps, suggesting that the dominant sources of correlation are individual galaxies and the more tenuous structures in the survey region, and not the massive and compact clusters. These results highlight the important roles cosmic variance and large-scale structure can play in the study of SMGs.

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

Murray, S. G.; Trott, C. M.; Jordan, C. H.

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions formore » the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.« less

The Connection Between X-ray Binaries and Star Clusters in the Antennae

NASA Astrophysics Data System (ADS)

Rangelov, Blagoy; Chandar, R.; Prestwich, A.

2011-05-01

High Mass X-ray Binaries (HMXBs) are believed to form in massive, compact star clusters. However the correlation between these young binary star systems and properties of their parent clusters are still poorly known. We compare the locations of 82 X-ray binaries detected in the merging Antennae galaxies by Zezas et al. (2006) based on observations taken with the Chandra Space Telescope, with a catalog of optically selected star clusters presented recently by Whitmore et al. (2010) based on observations taken with the Hubble Space Telescope. We find 22 X-ray binaries coincident or nearly coincident with star clusters. The ages of the clusters were estimated by comparing their UBVIHα colors with predictions from stellar evolutionary models. We find that 14 of the 22 coincident sources (64%) are hosted by star clusters with ages of 6 Myr or less. At these very young ages, only stars initially more massive than M ≥ 30 Msun have evolved into compact remnants, almost certainly black holes. Therefore, these 14 sources are likely to be black hole binaries. Five of the XRBs are hosted by young clusters with ages τ 30-50 Myr, while three are hosted by intermediate age clusters with τ 100-300 Myr. We suggest that these older X-ray binaries likely have neutron stars as the compact object. We conclude that precision age-dating of star clusters, which are spatially coincident with XRBs in nearby star forming galaxies, is a powerful method of constraining the nature of the XRBs.

Spatial Distribution of Megacopta cribraria (Hemiptera: Plataspidae) Adults, Eggs and Parasitism by Paratelenomus saccharalis (Hymenoptera: Platygastridae) in Soybean.

PubMed

Knight, Ian A; Roberts, Phillip M; Gardner, Wayne A; Oliver, Kerry M; Reay-Jones, Francis P F; Reisig, Dominic D; Toews, Michael D

2017-12-08

Since 2014, populations of the kudzu bug, Megacopta cribraria (F.) (Hemiptera: Plataspidae), have declined in the southeastern United States and seldom require treatment. This decline follows the discovery of Paratelenomus saccharalis (Dodd; Hymenoptera: Platygastridae), a non-native egg parasitoid. The objective of this project was to observe the temporal and spatial dynamics of P. saccharalis parasitism of kudzu bug egg masses in commercial soybean fields. Four fields were sampled weekly for kudzu bugs and egg masses at a density of one sample per 0.6 ha. Sampling commenced when soybean reached the R2 maturity stage and continued until no more egg masses were present. Responses including kudzu bugs, egg masses, and parasitism rates were analyzed using ANOVA, Spatial Analysis by Distance Indices (SADIE), and SaTScan spatial analysis software. Egg masses were collected from the field, held in the lab and monitored for emergence of kudzu bug nymphs or P. saccharalis. Kudzu bug populations were generally lower than previously reported in the literature and spatial aggregation was not consistently observed. Egg parasitism was first detected in early July and increased to nearly 40% in mid-August. Significant spatial patterns in parasitism were observed with spatio-temporal clusters being loosely associated with clusters of egg masses. There were no significant differences in parasitism rates between field margins and interiors, suggesting that P. saccharalis is an effective parasitoid of kudzu bug egg masses on a whole-field scale. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand.

PubMed

Jeefoo, Phaisarn; Tripathi, Nitin Kumar; Souris, Marc

2011-01-01

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.

Tennessee Valley Total and Cloud-to-Ground Lightning Climatology Comparison

NASA Technical Reports Server (NTRS)

Buechler, Dennis; Blakeslee, R. J.; Hall, J. M.; McCaul, E. W.

2008-01-01

The North Alabama Lightning Mapping Array (NALMA) has been in operation since 2001 and consists often VHF receivers deployed across northern Alabama. The NALMA locates sources of impulsive VHF radio signals from total lightning by accurately measuring the time that the signals arrive at the different receiving stations. The sources detected are then clustered into flashes by applying spatially and temporally constraints. This study examines the total lightning climatology of the region derived from NALMA and compares it to the cloud-to-ground (CG) climatology derived from the National Lightning Detection Network (NLDN) The presentation compares the total and CG lightning trends for monthly, daily, and hourly periods.

THE POPULATION OF COMPACT RADIO SOURCES IN THE ORION NEBULA CLUSTER

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

Forbrich, J.; Meingast, S.; Rivilla, V. M.

We present a deep centimeter-wavelength catalog of the Orion Nebula Cluster (ONC), based on a 30 hr single-pointing observation with the Karl G. Jansky Very Large Array in its high-resolution A-configuration using two 1 GHz bands centered at 4.7 and 7.3 GHz. A total of 556 compact sources were detected in a map with a nominal rms noise of 3 μ Jy bm{sup −1}, limited by complex source structure and the primary beam response. Compared to previous catalogs, our detections increase the sample of known compact radio sources in the ONC by more than a factor of seven. The newmore » data show complex emission on a wide range of spatial scales. Following a preliminary correction for the wideband primary-beam response, we determine radio spectral indices for 170 sources whose index uncertainties are less than ±0.5. We compare the radio to the X-ray and near-infrared point-source populations, noting similarities and differences.« less

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.

A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic.

PubMed

Read, S; Bath, P A; Willett, P; Maheswaran, R

2013-08-30

The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case-control study. A recent study suggests the inferential accuracy of several versions of the spatial scan statistic (principally the Poisson version) can be improved, at little computational cost, by using the Gumbel distribution, a method now available in SaTScan(TM) (www.satscan.org). We study in detail the effect of this technique when applied to the Bernoulli version and demonstrate that it is highly effective, albeit with some increase in false alarm rates at certain significance thresholds. We explain how this increase is due to the discrete nature of the Bernoulli spatial scan statistic and demonstrate that it can affect even small p-values. Despite this, we argue that the Gumbel method is actually preferable for very small p-values. Furthermore, we extend previous research by running benchmark trials on 12 000 synthetic datasets, thus demonstrating that the overall detection capability of the Bernoulli version (i.e. ratio of power to false alarm rate) is not noticeably affected by the use of the Gumbel method. We also provide an example application of the Gumbel method using data on hospital admissions for chronic obstructive pulmonary disease. Copyright © 2013 John Wiley & Sons, Ltd.

Spatial frequency characteristics at image decision-point locations for observers with different radiological backgrounds in lung nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Manning, David J.; Dix, Alan; Donovan, Tim

2009-02-01

Aim: The goal of the study is to determine the spatial frequency characteristics at locations in the image of overt and covert observers' decisions and find out if there are any similarities in different observers' groups: the same radiological experience group or the same accuracy scored level. Background: The radiological task is described as a visual searching decision making procedure involving visual perception and cognitive processing. Humans perceive the world through a number of spatial frequency channels, each sensitive to visual information carried by different spatial frequency ranges and orientations. Recent studies have shown that particular physical properties of local and global image-based elements are correlated with the performance and the level of experience of human observers in breast cancer and lung nodule detections. Neurological findings in visual perception were an inspiration for wavelet applications in vision research because the methodology tries to mimic the brain processing algorithms. Methods: The wavelet approach to the set of postero-anterior chest radiographs analysis has been used to characterize perceptual preferences observers with different levels of experience in the radiological task. Psychophysical methodology has been applied to track eye movements over the image, where particular ROIs related to the observers' fixation clusters has been analysed in the spaces frame by Daubechies functions. Results: Significance differences have been found between the spatial frequency characteristics at the location of different decisions.

Construction and comparative evaluation of different activity detection methods in brain FDG-PET.

PubMed

Buchholz, Hans-Georg; Wenzel, Fabian; Gartenschläger, Martin; Thiele, Frank; Young, Stewart; Reuss, Stefan; Schreckenberger, Mathias

2015-08-18

We constructed and evaluated reference brain FDG-PET databases for usage by three software programs (Computer-aided diagnosis for dementia (CAD4D), Statistical Parametric Mapping (SPM) and NEUROSTAT), which allow a user-independent detection of dementia-related hypometabolism in patients' brain FDG-PET. Thirty-seven healthy volunteers were scanned in order to construct brain FDG reference databases, which reflect the normal, age-dependent glucose consumption in human brain, using either software. Databases were compared to each other to assess the impact of different stereotactic normalization algorithms used by either software package. In addition, performance of the new reference databases in the detection of altered glucose consumption in the brains of patients was evaluated by calculating statistical maps of regional hypometabolism in FDG-PET of 20 patients with confirmed Alzheimer's dementia (AD) and of 10 non-AD patients. Extent (hypometabolic volume referred to as cluster size) and magnitude (peak z-score) of detected hypometabolism was statistically analyzed. Differences between the reference databases built by CAD4D, SPM or NEUROSTAT were observed. Due to the different normalization methods, altered spatial FDG patterns were found. When analyzing patient data with the reference databases created using CAD4D, SPM or NEUROSTAT, similar characteristic clusters of hypometabolism in the same brain regions were found in the AD group with either software. However, larger z-scores were observed with CAD4D and NEUROSTAT than those reported by SPM. Better concordance with CAD4D and NEUROSTAT was achieved using the spatially normalized images of SPM and an independent z-score calculation. The three software packages identified the peak z-scores in the same brain region in 11 of 20 AD cases, and there was concordance between CAD4D and SPM in 16 AD subjects. The clinical evaluation of brain FDG-PET of 20 AD patients with either CAD4D-, SPM- or NEUROSTAT-generated databases from an identical reference dataset showed similar patterns of hypometabolism in the brain regions known to be involved in AD. The extent of hypometabolism and peak z-score appeared to be influenced by the calculation method used in each software package rather than by different spatial normalization parameters.

Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

USGS Publications Warehouse

Garrity, Steven R.; Allen, Craig D.; Brumby, Steven P.; Gangodagamage, Chandana; McDowell, Nate G.; Cai, D. Michael

2013-01-01

Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper (Pinus edulis-Juniperus monosperma) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas experiencing wildfire and management activity. -Our results demonstrate that unsupervised clustering of bi-temporal NDVI and RGI differences can be used to detect tree mortality resulting from numerous causes and in several forest cover types.

Spatial and temporal patterns of imported malaria cases and local transmission in Trinidad.

PubMed

Chadee, D D; Kitron, U

1999-10-01

Over a 30-year period (1968-1997) 213 malaria cases in Trinidad were investigated by the Trinidad and Tobago Ministry of Health. Using a global positional system and a geographic information system, we mapped the precise location of all reported malaria cases, and associated them with breeding habitats of anopheline vectors. The majority of the cases (138, 63%) were individual imported cases around the big port cities. Plasmodium falciparum was the most common parasite, and Africa the most common source of imported cases. Two clusters of cases occurred: an introduced P. vivax outbreak associated with Anopheles aquasalis in 1990-1991, and an autochtonous focus of P. malariae associated with An. bellator and An. homunculus in 1994-1995. Application of a space-time statistic showed a significant clustering of P. malariae cases, and, to a lesser extent of P. vivax cases, but not of P. falciparum cases. Based on potential for occurrence of local transmission, we are developing risk maps to determine surveillance priorities, outbreak potential, and necessary degree and spatial range of control activities following case detections.

Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7 T.

PubMed

Boyacioğlu, Rasim; Schulz, Jenni; Koopmans, Peter J; Barth, Markus; Norris, David G

2015-10-01

A multiband multi-echo (MBME) sequence is implemented and compared to a matched standard multi-echo (ME) protocol to investigate the potential improvement in sensitivity and spatial specificity at 7 T for resting state and task fMRI. ME acquisition is attractive because BOLD sensitivity is less affected by variation in T2*, and because of the potential for separating BOLD and non-BOLD signal components. MBME further reduces TR thus increasing the potential reduction in physiological noise. In this study we used FSL-FIX to clean ME and MBME resting state and task fMRI data (both 3.5mm isotropic). After noise correction, the detection of resting state networks improves with more non-artifactual independent components being observed. Additional activation clusters for task data are discovered for MBME data (increased sensitivity) whereas existing clusters become more localized for resting state (improved spatial specificity). The results obtained indicate that MBME is superior to ME at high field strengths. Copyright © 2015 Elsevier Inc. All rights reserved.

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

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 genetic structuring of baobab (Adansonia digitata, Malvaceae) in the traditional agroforestry systems of West Africa.

PubMed

Kyndt, Tina; Assogbadjo, Achille E; Hardy, Olivier J; Glele Kakaï, Romain; Sinsin, Brice; Van Damme, Patrick; Gheysen, Godelieve

2009-05-01

This study evaluates the spatial genetic structure of baobab (Adansonia digitata) populations from West African agroforestry systems at different geographical scales using AFLP fingerprints. Eleven populations from four countries (Benin, Ghana, Burkina Faso, and Senegal) had comparable levels of genetic diversity, although the two populations in the extreme west (Senegal) had less diversity. Pairwise F(ST) ranged from 0.02 to 0.28 and increased with geographic distance, even at a regional scale. Gene pools detected by Bayesian clustering seem to be a byproduct of the isolation-by-distance pattern rather than representing actual discrete entities. The organization of genetic diversity appears to result essentially from spatially restricted gene flow, with some influences of human seed exchange. Despite the potential for relatively long-distance pollen and seed dispersal by bats within populations, statistically significant spatial genetic structuring within populations (SGS) was detected and gave a mean indirect estimate of neighborhood size of ca. 45. This study demonstrated that relatively high levels of genetic structuring are present in baobab at both large and within-population level, which was unexpected in regard to its dispersal by bats and the influence of human exchange of seeds. Implications of these results for the conservation of baobab populations are discussed.

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

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.

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

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.

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.

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.

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Evaluating the Use of Multilocus Variable Number Tandem Repeat Analysis of Shiga Toxin-Producing Escherichia coli O157 as a Routine Public Health Tool in England

PubMed Central

Byrne, Lisa; Elson, Richard; Dallman, Timothy J.; Perry, Neil; Ashton, Philip; Wain, John; Adak, Goutam K.; Grant, Kathie A.; Jenkins, Claire

2014-01-01

Multilocus variable number tandem repeat analysis (MLVA) provides microbiological support for investigations of clusters of cases of infection with Shiga toxin-producing E. coli (STEC) O157. All confirmed STEC O157 isolated in England and submitted to the Gastrointestinal Bacteria Reference Unit (GBRU) during a six month period were typed using MLVA, with the aim of assessing the impact of this approach on epidemiological investigations. Of 539 cases investigated, 341 (76%) had unique (>2 single locus variants) MLVA profiles, 12% of profiles occurred more than once due to known household transmission and 12% of profiles occurred as part of 41 clusters, 21 of which were previously identified through routine public health investigation of cases. The remaining 20 clusters were not previously detected and STEC enhanced surveillance data for associated cases were retrospectively reviewed for epidemiological links including shared exposures, geography and/or time. Additional evidence of a link between cases was found in twelve clusters. Compared to phage typing, the number of sporadic cases was reduced from 69% to 41% and the diversity index for MLVA was 0.996 versus 0.782 for phage typing. Using MLVA generates more data on the spatial and temporal dispersion of cases, better defining the epidemiology of STEC infection than phage typing. The increased detection of clusters through MLVA typing highlights the challenges to health protection practices, providing a forerunner to the advent of whole genome sequencing as a diagnostic tool. PMID:24465775

Evaluating the use of multilocus variable number tandem repeat analysis of Shiga toxin-producing Escherichia coli O157 as a routine public health tool in England.

PubMed

Byrne, Lisa; Elson, Richard; Dallman, Timothy J; Perry, Neil; Ashton, Philip; Wain, John; Adak, Goutam K; Grant, Kathie A; Jenkins, Claire

2014-01-01

Multilocus variable number tandem repeat analysis (MLVA) provides microbiological support for investigations of clusters of cases of infection with Shiga toxin-producing E. coli (STEC) O157. All confirmed STEC O157 isolated in England and submitted to the Gastrointestinal Bacteria Reference Unit (GBRU) during a six month period were typed using MLVA, with the aim of assessing the impact of this approach on epidemiological investigations. Of 539 cases investigated, 341 (76%) had unique (>2 single locus variants) MLVA profiles, 12% of profiles occurred more than once due to known household transmission and 12% of profiles occurred as part of 41 clusters, 21 of which were previously identified through routine public health investigation of cases. The remaining 20 clusters were not previously detected and STEC enhanced surveillance data for associated cases were retrospectively reviewed for epidemiological links including shared exposures, geography and/or time. Additional evidence of a link between cases was found in twelve clusters. Compared to phage typing, the number of sporadic cases was reduced from 69% to 41% and the diversity index for MLVA was 0.996 versus 0.782 for phage typing. Using MLVA generates more data on the spatial and temporal dispersion of cases, better defining the epidemiology of STEC infection than phage typing. The increased detection of clusters through MLVA typing highlights the challenges to health protection practices, providing a forerunner to the advent of whole genome sequencing as a diagnostic tool.

Spatial Variations in Dengue Transmission in Schools in Thailand

PubMed Central

Ratanawong, Pitcha; Kittayapong, Pattamaporn; Olanratmanee, Phanthip; Wilder-Smith, Annelies; Byass, Peter; Tozan, Yesim; Dambach, Peter; Quiñonez, Carlos Alberto Montenegro; Louis, Valérie R.

2016-01-01

Background Dengue is an important neglected tropical disease, with more than half of the world’s population living in dengue endemic areas. Good understanding of dengue transmission sites is a critical factor to implement effective vector control measures. Methods A cohort of 1,811 students from 10 schools in rural, semi-rural and semi-urban Thailand participated in this study. Seroconversion data and location of participants’ residences and schools were recorded to determine spatial patterns of dengue infections. Blood samples were taken to confirm dengue infections in participants at the beginning and the end of school term. Entomological factors included a survey of adult mosquito density using a portable vacuum aspirator during the school term and a follow up survey of breeding sites of Aedes vectors in schools after the school term. Clustering analyses were performed to detect spatial aggregation of dengue infections among participants. Results A total of 57 dengue seroconversions were detected among the 1,655 participants who provided paired blood samples. Of the 57 confirmed dengue infections, 23 (40.0%) occurred in students from 6 (6.8%) of the 88 classrooms in 10 schools. Dengue infections did not show significant clustering by residential location in the study area. During the school term, a total of 66 Aedes aegypti mosquitoes were identified from the 278 mosquitoes caught in 50 classrooms of the 10 schools. In a follow-up survey of breeding sites, 484 out of 2,399 water containers surveyed (20.2%) were identified as active mosquito breeding sites. Discussion and Conclusion Our findings suggest that dengue infections were clustered among schools and among classrooms within schools. The schools studied were found to contain a large number of different types of breeding sites. Aedes vector densities in schools were correlated with dengue infections and breeding sites in those schools. Given that only a small proportion of breeding sites in the schools were subjected to vector control measures (11%), this study emphasizes the urgent need to implement vector control strategies at schools, while maintaining efforts at the household level. PMID:27669170

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.

Effects of landscape features on population genetic variation of a tropical stream fish, Stone lapping minnow, Garra cambodgiensis, in the upper Nan River drainage basin, northern Thailand.

PubMed

Jaisuk, Chaowalee; Senanan, Wansuk

2018-01-01

Spatial genetic variation of river-dwelling freshwater fishes is typically affected by the historical and contemporary river landscape as well as life-history traits. Tropical river and stream landscapes have endured extended geological change, shaping the existing pattern of genetic diversity, but were not directly affected by glaciation. Thus, spatial genetic variation of tropical fish populations should look very different from the pattern observed in temperate fish populations. These data are becoming important for designing appropriate management and conservation plans, as these aquatic systems are undergoing intense development and exploitation. This study evaluated the effects of landscape features on population genetic diversity of Garra cambodgiensis, a stream cyprinid , in eight tributary streams in the upper Nan River drainage basin ( n  = 30-100 individuals/location), Nan Province, Thailand. These populations are under intense fishing pressure from local communities. Based on 11 microsatellite loci, we detected moderate genetic diversity within eight population samples (average number of alleles per locus = 10.99 ± 3.00; allelic richness = 10.12 ± 2.44). Allelic richness within samples and stream order of the sampling location were negatively correlated ( P  < 0.05). We did not detect recent bottleneck events in these populations, but we did detect genetic divergence among populations (Global F ST = 0.022, P  < 0.01). The Bayesian clustering algorithms (TESS and STRUCTURE) suggested that four to five genetic clusters roughly coincide with sub-basins: (1) headwater streams/main stem of the Nan River, (2) a middle tributary, (3) a southeastern tributary and (4) a southwestern tributary. We observed positive correlation between geographic distance and linearized F ST ( P  < 0.05), and the genetic differentiation pattern can be moderately explained by the contemporary stream network (STREAMTREE analysis, R 2 = 0.75). The MEMGENE analysis suggested genetic division between northern (genetic clusters 1 and 2) and southern (clusters 3 and 4) sub-basins. We observed a high degree of genetic admixture in each location, highlighting the importance of natural flooding patterns and possible genetic impacts of supplementary stocking. Insights obtained from this research advance our knowledge of the complexity of a tropical stream system, and guide current conservation and restoration efforts for this species in Thailand.

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.

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.

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.

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.

Characterization of Differential Toll-Like Receptor Responses below the Optical Diffraction Limit**

PubMed Central

Aaron, Jesse S.; Carson, Bryan D.; Timlin, Jerilyn A.

2013-01-01

Many membrane receptors are recruited to specific cell surface domains to form nanoscale clusters upon ligand activation. This step appears to be necessary to initiate signaling, including pathways in innate immune system activation. However, virulent pathogens such as Yersinia pestis (the causative agent of plague) are known to evade innate immune detection, in contrast to similar microbes (such as E. coli) that elicit a robust response. This disparity has been partly attributed to the structure of lipopolysaccharides (LPS) on the bacterial cell wall, which are recognized by the innate immune receptor TLR4. As such, we hypothesized that nanoscale differences would exist between the spatial clustering of TLR4 upon binding of LPS derived from Y. pestis and E. coli. Although optical imaging can provide exquisite details of the spatial organization of biomolecules, there is a mismatch between the scale at which receptor clustering occurs (<300 nm) and the optical diffraction limit (>400 nm). The last decade has seen the emergence of super-resolution imaging methods that effectively break the optical diffraction barrier to yield truly nanoscale information in intact biological samples. This study reports the first visualizations of TLR4 distributions on intact cells at image resolutions of <30 nm using a novel, dual-color stochastic optical reconstruction microscopy (STORM) technique. This methodology permits distinction between receptors containing bound LPS from those without at the nanoscale. Importantly, we also show that LPS derived from immuno-stimulatory bacteria resulted in significantly higher LPS-TLR4 cluster sizes and a nearly two-fold greater ligand/receptor colocalization as compared to immuno-evading LPS. PMID:22807232

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.

Spatiotemporal epidemiology of scarlet fever in Jiangsu Province, China, 2005-2015.

PubMed

Zhang, Qi; Liu, Wendong; Ma, Wang; Shi, Yingying; Wu, Ying; Li, Yuan; Liang, Shuyi; Zhu, Yefei; Zhou, Minghao

2017-08-30

A marked increase in the incidence rate of scarlet fever imposed a considerable burden on the health of children aged 5 to 15 years. The main purpose of this study was to depict the spatiotemporal epidemiological characteristics of scarlet fever in Jiangsu Province, China in order to develop and implement effective scientific prevention and control strategies. Smoothed map was used to demonstrate the spatial distribution of scarlet fever in Jiangsu Province. In addition, a retrospective space-time analysis based on a discrete Poisson model was utilized to detect clusters of scarlet fever from 2005 to 2015. During the years 2005-2015, a total of 15,873 scarlet fever cases occurred in Jiangsu Province, with an average annual incidence rate of 1.87 per 100,000. A majority of the cases (83.67%) occurred in children aged 3 to 9 years. Each year, two seasonal incidence peaks were observed, the higher occurring between March and July, the lower between November and the following January. The incidence in the southern regions of the province was generally higher than that in the northern regions. Seven clusters, all of which occurred during incidence peaks, were detected via space-time scan statistical analysis. The most likely cluster and one of the secondary clusters were detected in the southern and northern high endemic regions, respectively. The prevalence of scarlet fever in Jiangsu Province had a marked seasonality variation and was relatively endemic in some regions. Children aged 3 to 9 years were the major victims of this disease, and kindergartens and primary schools were the focus of surveillance and control. Targeted strategies and measures should be taken to reduce the incidence.

Spatial organization of RNA polymerase II inside a mammalian cell nucleus revealed by reflected light-sheet superresolution microscopy.

PubMed

Zhao, Ziqing W; Roy, Rahul; Gebhardt, J Christof M; Suter, David M; Chapman, Alec R; Xie, X Sunney

2014-01-14

Superresolution microscopy based on single-molecule centroid determination has been widely applied to cellular imaging in recent years. However, quantitative imaging of the mammalian nucleus has been challenging due to the lack of 3D optical sectioning methods for normal-sized cells, as well as the inability to accurately count the absolute copy numbers of biomolecules in highly dense structures. Here we report a reflected light-sheet superresolution microscopy method capable of imaging inside the mammalian nucleus with superior signal-to-background ratio as well as molecular counting with single-copy accuracy. Using reflected light-sheet superresolution microscopy, we probed the spatial organization of transcription by RNA polymerase II (RNAP II) molecules and quantified their global extent of clustering inside the mammalian nucleus. Spatiotemporal clustering analysis that leverages on the blinking photophysics of specific organic dyes showed that the majority (>70%) of the transcription foci originate from single RNAP II molecules, and no significant clustering between RNAP II molecules was detected within the length scale of the reported diameter of "transcription factories." Colocalization measurements of RNAP II molecules equally labeled by two spectrally distinct dyes confirmed the primarily unclustered distribution, arguing against a prevalent existence of transcription factories in the mammalian nucleus as previously proposed. The methods developed in our study pave the way for quantitative mapping and stoichiometric characterization of key biomolecular species deep inside mammalian cells.

Spatial Patterns of High Aedes aegypti Oviposition Activity in Northwestern Argentina

PubMed Central

Estallo, Elizabet Lilia; Más, Guillermo; Vergara-Cid, Carolina; Lanfri, Mario Alberto; Ludueña-Almeida, Francisco; Scavuzzo, Carlos Marcelo; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

2013-01-01

Background In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Methodology Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005–2007). Spatial autocorrelation was measured with Moran’s Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Principal Findings Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC = 0.77), obtaining 99% of sensitivity and 75.29% of specificity. Conclusions Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively. PMID:23349813

Detecting and Quantifying Topography in Neural Maps

PubMed Central

Yarrow, Stuart; Razak, Khaleel A.; Seitz, Aaron R.; Seriès, Peggy

2014-01-01

Topographic maps are an often-encountered feature in the brains of many species, yet there are no standard, objective procedures for quantifying topography. Topographic maps are typically identified and described subjectively, but in cases where the scale of the map is close to the resolution limit of the measurement technique, identifying the presence of a topographic map can be a challenging subjective task. In such cases, an objective topography detection test would be advantageous. To address these issues, we assessed seven measures (Pearson distance correlation, Spearman distance correlation, Zrehen's measure, topographic product, topological correlation, path length and wiring length) by quantifying topography in three classes of cortical map model: linear, orientation-like, and clusters. We found that all but one of these measures were effective at detecting statistically significant topography even in weakly-ordered maps, based on simulated noisy measurements of neuronal selectivity and sparse sampling of the maps. We demonstrate the practical applicability of these measures by using them to examine the arrangement of spatial cue selectivity in pallid bat A1. This analysis shows that significantly topographic arrangements of interaural intensity difference and azimuth selectivity exist at the scale of individual binaural clusters. PMID:24505279

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.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

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.

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

Point process statistics in atom probe tomography.

PubMed

Philippe, T; Duguay, S; Grancher, G; Blavette, D

2013-09-01

We present a review of spatial point processes as statistical models that we have designed for the analysis and treatment of atom probe tomography (APT) data. As a major advantage, these methods do not require sampling. The mean distance to nearest neighbour is an attractive approach to exhibit a non-random atomic distribution. A χ(2) test based on distance distributions to nearest neighbour has been developed to detect deviation from randomness. Best-fit methods based on first nearest neighbour distance (1 NN method) and pair correlation function are presented and compared to assess the chemical composition of tiny clusters. Delaunay tessellation for cluster selection has been also illustrated. These statistical tools have been applied to APT experiments on microelectronics materials. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Changes of the time-varying percentiles of daily extreme temperature in China

NASA Astrophysics Data System (ADS)

Li, Bin; Chen, Fang; Xu, Feng; Wang, Xinrui

2017-11-01

Identifying the air temperature frequency distributions and evaluating the trends in time-varying percentiles are very important for climate change studies. In order to get a better understanding of the recent temporal and spatial pattern of the temperature changes in China, we have calculated the trends in temporal-varying percentiles of the daily extreme air temperature firstly. Then we divide all the stations to get the spatial patterns for the percentile trends using the average linkage cluster analysis method. To make a comparison, the shifts of trends percentile frequency distribution from 1961-1985 to 1986-2010 are also examined. Important results in three aspects have been achieved: (1) In terms of the trends in temporal-varying percentiles of the daily extreme air temperature, the most intense warming for daily maximum air temperature (Tmax) was detected in the upper percentiles with a significant increasing tendency magnitude (>2.5 °C/50year), and the greatest warming for daily minimum air temperature (Tmin) occurred with very strong trends exceeding 4 °C/50year. (2) The relative coherent spatial patterns for the percentile trends were found, and stations for the whole country had been divided into three clusters. The three primary clusters were distributed regularly to some extent from north to south, indicating the possible large influence of the latitude. (3) The most significant shifts of trends percentile frequency distribution from 1961-1985 to 1986-2010 was found in Tmax. More than half part of the frequency distribution show negative trends less than -0.5 °C/50year in 1961-1985, while showing trends less than 2.5 °C/50year in 1986-2010.

Spatio-temporal scan statistics for the detection of outbreaks involving common molecular subtypes: using human cases of Escherichia coli O157:H7 provincial PFGE pattern 8 (National Designation ECXAI.0001) in Alberta as an example.

PubMed

So, H C; Pearl, D L; von Königslöw, T; Louie, M; Chui, L; Svenson, L W

2013-08-01

Molecular typing methods have become a common part of the surveillance of foodborne pathogens. In particular, pulsed-field gel electrophoresis (PFGE) has been used successfully to identify outbreaks of Escherichia coli O157:H7 in humans from a variety of food and environmental sources. However, some PFGE patterns appear commonly in surveillance systems, making it more difficult to distinguish between outbreak and sporadic cases based on molecular data alone. In addition, it is unknown whether these common patterns might have unique epidemiological characteristics reflected in their spatial and temporal distributions. Using E. coli O157:H7 surveillance data from Alberta, collected from 2000 to 2002, we investigated whether E. coli O157:H7 with provincial PFGE pattern 8 (national designation ECXAI.0001) clustered in space, time and space-time relative to other PFGE patterns using the spatial scan statistic. Based on our purely spatial and temporal scans using a Bernoulli model, there did not appear to be strong evidence that isolates of E. coli O157:H7 with provincial PFGE pattern 8 are distributed differently from other PFGE patterns. However, we did identify space-time clusters of isolates with PFGE pattern 8, using a Bernoulli model and a space-time permutation model, which included known outbreaks and potentially unrecognized outbreaks or additional outbreak cases. There were differences between the two models in the space-time clusters identified, which suggests that the use of both models could increase the sensitivity of a quantitative surveillance system for identifying outbreaks involving isolates sharing a common PFGE pattern. © 2012 Blackwell Verlag GmbH.

Targeted Screening Strategies to Detect Trypanosoma cruzi Infection in Children

PubMed Central

Levy, Michael Z.; Kawai, Vivian; Bowman, Natalie M.; Waller, Lance A.; Cabrera, Lilia; Pinedo-Cancino, Viviana V.; Seitz, Amy E.; Steurer, Frank J.; Cornejo del Carpio, Juan G.; Cordova-Benzaquen, Eleazar; Maguire, James H.; Gilman, Robert H.; Bern, Caryn

2007-01-01

Background Millions of people are infected with Trypanosoma cruzi, the causative agent of Chagas disease in Latin America. Anti-trypanosomal drug therapy can cure infected individuals, but treatment efficacy is highest early in infection. Vector control campaigns disrupt transmission of T. cruzi, but without timely diagnosis, children infected prior to vector control often miss the window of opportunity for effective chemotherapy. Methods and Findings We performed a serological survey in children 2–18 years old living in a peri-urban community of Arequipa, Peru, and linked the results to entomologic, spatial and census data gathered during a vector control campaign. 23 of 433 (5.3% [95% CI 3.4–7.9]) children were confirmed seropositive for T. cruzi infection by two methods. Spatial analysis revealed that households with infected children were very tightly clustered within looser clusters of households with parasite-infected vectors. Bayesian hierarchical mixed models, which controlled for clustering of infection, showed that a child's risk of being seropositive increased by 20% per year of age and 4% per vector captured within the child's house. Receiver operator characteristic (ROC) plots of best-fit models suggest that more than 83% of infected children could be identified while testing only 22% of eligible children. Conclusions We found evidence of spatially-focal vector-borne T. cruzi transmission in peri-urban Arequipa. Ongoing vector control campaigns, in addition to preventing further parasite transmission, facilitate the collection of data essential to identifying children at high risk of T. cruzi infection. Targeted screening strategies could make integration of diagnosis and treatment of children into Chagas disease control programs feasible in lower-resource settings. PMID:18160979

Differential association of GABAB receptors with their effector ion channels in Purkinje cells.

PubMed

Luján, Rafael; Aguado, Carolina; Ciruela, Francisco; Cózar, Javier; Kleindienst, David; de la Ossa, Luis; Bettler, Bernhard; Wickman, Kevin; Watanabe, Masahiko; Shigemoto, Ryuichi; Fukazawa, Yugo

2018-04-01

Metabotropic GABA B receptors mediate slow inhibitory effects presynaptically and postsynaptically through the modulation of different effector signalling pathways. Here, we analysed the distribution of GABA B receptors using highly sensitive SDS-digested freeze-fracture replica labelling in mouse cerebellar Purkinje cells. Immunoreactivity for GABA B1 was observed on presynaptic and, more abundantly, on postsynaptic compartments, showing both scattered and clustered distribution patterns. Quantitative analysis of immunoparticles revealed a somato-dendritic gradient, with the density of immunoparticles increasing 26-fold from somata to dendritic spines. To understand the spatial relationship of GABA B receptors with two key effector ion channels, the G protein-gated inwardly rectifying K + (GIRK/Kir3) channel and the voltage-dependent Ca 2+ channel, biochemical and immunohistochemical approaches were performed. Co-immunoprecipitation analysis demonstrated that GABA B receptors co-assembled with GIRK and Ca V 2.1 channels in the cerebellum. Using double-labelling immunoelectron microscopic techniques, co-clustering between GABA B1 and GIRK2 was detected in dendritic spines, whereas they were mainly segregated in the dendritic shafts. In contrast, co-clustering of GABA B1 and Ca V 2.1 was detected in dendritic shafts but not spines. Presynaptically, although no significant co-clustering of GABA B1 and GIRK2 or Ca V 2.1 channels was detected, inter-cluster distance for GABA B1 and GIRK2 was significantly smaller in the active zone than in the dendritic shafts, and that for GABA B1 and Ca V 2.1 was significantly smaller in the active zone than in the dendritic shafts and spines. Thus, GABA B receptors are associated with GIRK and Ca V 2.1 channels in different subcellular compartments. These data provide a better framework for understanding the different roles played by GABA B receptors and their effector ion channels in the cerebellar network.

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.

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, 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.

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.

WIYN Open Cluster Study. XXXII. Stellar Radial Velocities in the Old Open Cluster NGC 188

NASA Astrophysics Data System (ADS)

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

2008-06-01

We present the results of our ongoing radial-velocity (RV) survey of the old (7 Gyr) open cluster NGC 188. Our WIYN 3.5 m data set spans a time baseline of 11 years, a magnitude range of 12 <= V <= 16.5 (1.18-0.94 M sun), and a 1° diameter region on the sky. With the addition of a Domain Astrophysical Observatory data set we extend our bright limit to V = 10.8 and, for some stars, extend our time baseline to 35 years. Our magnitude limits include solar-mass main-sequence stars, subgiants, giants, and blue stragglers (BSs), and our spatial coverage extends radially to 17 pc (~13 core radii). For the WIYN data we present a detailed description of our data reduction process and a thorough analysis of our measurement precision of 0.4 km s-1 for narrow-lined stars. We have measured radial velocities for 1046 stars in the direction of NGC 188, and have calculated RV membership probabilities for stars with >=3 measurements, finding 473 to be likely cluster members. We detect 124 velocity-variable cluster members, all of which are likely to be dynamically hard-binary stars. Using our single member stars, we find an average cluster radial velocity of -42.36 ± 0.04 km s-1. We use our precise RV and proper-motion membership data to greatly reduce field-star contamination in our cleaned color-magnitude diagram, from which we identify six stars of note that lie far from a standard single-star isochrone. We present a detailed study of the spatial distribution of cluster-member populations, and find the binaries to be centrally concentrated, providing evidence for the presence of mass segregation in NGC 188. We observe the BSs to populate a bimodal spatial distribution that is not centrally concentrated, suggesting that we may be observing two populations of BSs in NGC 188, including a centrally concentrated distribution as well as a halo population. Finally, we find NGC 188 to have a global RV dispersion of 0.64 ± 0.04 km s-1, which may be inflated by up to 0.23 km s-1 from unresolved binaries. When corrected for unresolved binaries, the NGC 188 RV dispersion has a nearly isothermal radial distribution. We use this mean-corrected velocity dispersion to derive a virial mass of 2300 ± 460 M sun .

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.

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.

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

Fine-Scale Analysis Reveals Cryptic Landscape Genetic Structure in Desert Tortoises

PubMed Central

Latch, Emily K.; Boarman, William I.; Walde, Andrew; Fleischer, Robert C.

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately. PMID:22132143

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises.

PubMed

Latch, Emily K; Boarman, William I; Walde, Andrew; Fleischer, Robert C

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.

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.

Can single molecule localization microscopy be used to map closely spaced RGD nanodomains?

PubMed Central

Nicovich, Philip R.; Soeriyadi, Alexander; Nieves, Daniel J.; Gooding, J. Justin; Gaus, Katharina

2017-01-01

Cells sense and respond to nanoscale variations in the distribution of ligands to adhesion receptors. This makes single molecule localization microscopy (SMLM) an attractive tool to map the distribution of ligands on nanopatterned surfaces. We explore the use of SMLM spatial cluster analysis to detect nanodomains of the cell adhesion-stimulating tripeptide arginine-glycine-aspartic acid (RGD). These domains were formed by the phase separation of block copolymers with controllable spacing on the scale of tens of nanometers. We first determined the topology of the block copolymer with atomic force microscopy (AFM) and then imaged the localization of individual RGD peptides with direct stochastic optical reconstruction microscopy (dSTORM). To compare the data, we analyzed the dSTORM data with DBSCAN (density-based spatial clustering application with noise). The ligand distribution and polymer topology are not necessary identical since peptides may attach to the polymer outside the nanodomains and/or coupling and detection of peptides within the nanodomains is incomplete. We therefore performed simulations to explore the extent to which nanodomains could be mapped with dSTORM. We found that successful detection of nanodomains by dSTORM was influenced by the inter-domain spacing and the localization precision of individual fluorophores, and less by non-specific absorption of ligands to the substratum. For example, under our imaging conditions, DBSCAN identification of nanodomains spaced further than 50 nm apart was largely independent of background localisations, while nanodomains spaced closer than 50 nm required a localization precision of ~11 nm to correctly estimate the modal nearest neighbor distance (NDD) between nanodomains. We therefore conclude that SMLM is a promising technique to directly map the distribution and nanoscale organization of ligands and would benefit from an improved localization precision. PMID:28723958

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

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

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

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.

Rapid broad area search and detection of Chinese surface-to-air missile sites using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Marcum, Richard A.; Davis, Curt H.; Scott, Grant J.; Nivin, Tyler W.

2017-10-01

We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth's surface. Various DCNN were trained and tested using fewer than 100 positive training examples (China only) from a worldwide surface-to-air-missile (SAM) site dataset. A ResNet-101 DCNN achieved a 98.2% average accuracy for the China SAM site data. The ResNet-101 DCNN was used to process ˜19.6 M image chips over a large study area in southeastern China. DCNN chip detections (˜9300) were postprocessed with a spatial clustering algorithm to produce a ranked list of ˜2100 candidate SAM site locations. The combination of DCNN processing and spatial clustering effectively reduced the search area by ˜660X (0.15% of the DCNN-processed land area). An efficient web interface was used to facilitate a rapid serial human review of the candidate SAM sites in the China study area. Four novice imagery analysts with no prior imagery analysis experience were able to complete a DCNN-assisted SAM site search in an average time of ˜42 min. This search was ˜81X faster than a traditional visual search over an equivalent land area of ˜88,640 km2 while achieving nearly identical statistical accuracy (˜90% F1).

An Improved Statistical Point-source Foreground Model for the Epoch of Reionization

NASA Astrophysics Data System (ADS)

Murray, S. G.; Trott, C. M.; Jordan, C. H.

2017-08-01

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.

Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.

PubMed

Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F

2012-06-01

This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the spatiotemporal characteristics of sources located in the epileptic focus. The results were validated by standard methods, ensuring good spatial resolution by MRI and SPECT and optimal temporal resolution by intracerebral EEG. Surface EEG can be used to identify different spike clusters and sources of the successive epileptic states. The method that was used in this study will provide physicians with a better understanding of the pathophysiological characteristics of epileptic activities. In particular, this method may be useful for more effective positioning of implantable intracerebral electrodes. Copyright © 2011 Elsevier Masson SAS. All rights reserved.