Optimization method of superpixel analysis for multi-contrast Jones matrix tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa K.; Miura, Masahiro; Yasuno, Yoshiaki

2017-02-01

Local statistics are widely utilized for quantification and image processing of OCT. For example, local mean is used to reduce speckle, local variation of polarization state (degree-of-polarization-uniformity (DOPU)) is used to visualize melanin. Conventionally, these statistics are calculated in a rectangle kernel whose size is uniform over the image. However, the fixed size and shape of the kernel result in a tradeoff between image sharpness and statistical accuracy. Superpixel is a cluster of pixels which is generated by grouping image pixels based on the spatial proximity and similarity of signal values. Superpixels have variant size and flexible shapes which preserve the tissue structure. Here we demonstrate a new superpixel method which is tailored for multifunctional Jones matrix OCT (JM-OCT). This new method forms the superpixels by clustering image pixels in a 6-dimensional (6-D) feature space (spatial two dimensions and four dimensions of optical features). All image pixels were clustered based on their spatial proximity and optical feature similarity. The optical features are scattering, OCT-A, birefringence and DOPU. The method is applied to retinal OCT. Generated superpixels preserve the tissue structures such as retinal layers, sclera, vessels, and retinal pigment epithelium. Hence, superpixel can be utilized as a local statistics kernel which would be more suitable than a uniform rectangle kernel. Superpixelized image also can be used for further image processing and analysis. Since it reduces the number of pixels to be analyzed, it reduce the computational cost of such image processing.

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.

The spatial clustering of obesity: does the built environment matter?

PubMed

Huang, R; Moudon, A V; Cook, A J; Drewnowski, A

2015-12-01

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes. © 2014 The British Dietetic Association Ltd.

Statistical characteristics of the spatial distribution of territorial contamination by radionuclides from the Chernobyl accident

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

Arutyunyan, R.V.; Bol`shov, L.A.; Vasil`ev, S.K.

1994-06-01

The objective of this study was to clarify a number of issues related to the spatial distribution of contaminants from the Chernobyl accident. The effects of local statistics were addressed by collecting and analyzing (for Cesium 137) soil samples from a number of regions, and it was found that sample activity differed by a factor of 3-5. The effect of local non-uniformity was estimated by modeling the distribution of the average activity of a set of five samples for each of the regions, with the spread in the activities for a {+-}2 range being equal to 25%. The statistical characteristicsmore » of the distribution of contamination were then analyzed and found to be a log-normal distribution with the standard deviation being a function of test area. All data for the Bryanskaya Oblast area were analyzed statistically and were adequately described by a log-normal function.« less

Many-Body Localization and Thermalization in Quantum Statistical Mechanics

NASA Astrophysics Data System (ADS)

Nandkishore, Rahul; Huse, David A.

2015-03-01

We review some recent developments in the statistical mechanics of isolated quantum systems. We provide a brief introduction to quantum thermalization, paying particular attention to the eigenstate thermalization hypothesis (ETH) and the resulting single-eigenstate statistical mechanics. We then focus on a class of systems that fail to quantum thermalize and whose eigenstates violate the ETH: These are the many-body Anderson-localized systems; their long-time properties are not captured by the conventional ensembles of quantum statistical mechanics. These systems can forever locally remember information about their local initial conditions and are thus of interest for possibilities of storing quantum information. We discuss key features of many-body localization (MBL) and review a phenomenology of the MBL phase. Single-eigenstate statistical mechanics within the MBL phase reveal dynamically stable ordered phases, and phase transitions among them, that are invisible to equilibrium statistical mechanics and can occur at high energy and low spatial dimensionality, where equilibrium ordering is forbidden.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

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.

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.

Humans make efficient use of natural image statistics when performing spatial interpolation.

PubMed

D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S

2013-12-16

Visual systems learn through evolution and experience over the lifespan to exploit the statistical structure of natural images when performing visual tasks. Understanding which aspects of this statistical structure are incorporated into the human nervous system is a fundamental goal in vision science. To address this goal, we measured human ability to estimate the intensity of missing image pixels in natural images. Human estimation accuracy is compared with various simple heuristics (e.g., local mean) and with optimal observers that have nearly complete knowledge of the local statistical structure of natural images. Human estimates are more accurate than those of simple heuristics, and they match the performance of an optimal observer that knows the local statistical structure of relative intensities (contrasts). This optimal observer predicts the detailed pattern of human estimation errors and hence the results place strong constraints on the underlying neural mechanisms. However, humans do not reach the performance of an optimal observer that knows the local statistical structure of the absolute intensities, which reflect both local relative intensities and local mean intensity. As predicted from a statistical analysis of natural images, human estimation accuracy is negligibly improved by expanding the context from a local patch to the whole image. Our results demonstrate that the human visual system exploits efficiently the statistical structure of natural images.

Contextual Interactions in Grating Plaid Configurations Are Explained by Natural Image Statistics and Neural Modeling

PubMed Central

Ernst, Udo A.; Schiffer, Alina; Persike, Malte; Meinhardt, Günter

2016-01-01

Processing natural scenes requires the visual system to integrate local features into global object descriptions. To achieve coherent representations, the human brain uses statistical dependencies to guide weighting of local feature conjunctions. Pairwise interactions among feature detectors in early visual areas may form the early substrate of these local feature bindings. To investigate local interaction structures in visual cortex, we combined psychophysical experiments with computational modeling and natural scene analysis. We first measured contrast thresholds for 2 × 2 grating patch arrangements (plaids), which differed in spatial frequency composition (low, high, or mixed), number of grating patch co-alignments (0, 1, or 2), and inter-patch distances (1° and 2° of visual angle). Contrast thresholds for the different configurations were compared to the prediction of probability summation (PS) among detector families tuned to the four retinal positions. For 1° distance the thresholds for all configurations were larger than predicted by PS, indicating inhibitory interactions. For 2° distance, thresholds were significantly lower compared to PS when the plaids were homogeneous in spatial frequency and orientation, but not when spatial frequencies were mixed or there was at least one misalignment. Next, we constructed a neural population model with horizontal laminar structure, which reproduced the detection thresholds after adaptation of connection weights. Consistent with prior work, contextual interactions were medium-range inhibition and long-range, orientation-specific excitation. However, inclusion of orientation-specific, inhibitory interactions between populations with different spatial frequency preferences were crucial for explaining detection thresholds. Finally, for all plaid configurations we computed their likelihood of occurrence in natural images. The likelihoods turned out to be inversely related to the detection thresholds obtained at larger inter-patch distances. However, likelihoods were almost independent of inter-patch distance, implying that natural image statistics could not explain the crowding-like results at short distances. This failure of natural image statistics to resolve the patch distance modulation of plaid visibility remains a challenge to the approach. PMID:27757076

Application of hotspot detection using spatial scan statistic: Study of criminality in Indonesia

NASA Astrophysics Data System (ADS)

Runadi, Taruga; Widyaningsih, Yekti

2017-03-01

According to the police registered data, the number of criminal cases tends to fluctuate during 2011 to 2013. It means there is no significant reduction cases number of criminal acts during that period. Local government needs to observe whether their area was a high risk of criminal case. The objectives of this study are to detect hotspot area of certain criminal cases using spatial scan statistic. This study analyzed the data of 22 criminal types cases based on province in Indonesia that occurred during 2013. The data was obtained from Badan Pusat Statistik (BPS) that was released in 2014. Hotspot detection was performed according to the likelihood ratio of the Poisson model using SaTScanTM software and then mapped using R. The spatial scan statistic method successfully detected provinces that was categorized as hotspot for 22 crime types cases being analyzed with p-value less than 0.05. The local governments of province that were detected as hotspot area of certain crime cases should provide more attention to improve security quality.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

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.

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 variability of turbulent fluxes in the roughness sublayer of an even-aged pine forest

USGS Publications Warehouse

Katul, G.; Hsieh, C.-I.; Bowling, D.; Clark, K.; Shurpali, N.; Turnipseed, A.; Albertson, J.; Tu, K.; Hollinger, D.; Evans, B. M.; Offerle, B.; Anderson, D.; Ellsworth, D.; Vogel, C.; Oren, R.

1999-01-01

The spatial variability of turbulent flow statistics in the roughness sublayer (RSL) of a uniform even-aged 14 m (= h) tall loblolly pine forest was investigated experimentally. Using seven existing walkup towers at this stand, high frequency velocity, temperature, water vapour and carbon dioxide concentrations were measured at 15.5 m above the ground surface from October 6 to 10 in 1997. These seven towers were separated by at least 100 m from each other. The objective of this study was to examine whether single tower turbulence statistics measurements represent the flow properties of RSL turbulence above a uniform even-aged managed loblolly pine forest as a best-case scenario for natural forested ecosystems. From the intensive space-time series measurements, it was demonstrated that standard deviations of longitudinal and vertical velocities (??(u), ??(w)) and temperature (??(T)) are more planar homogeneous than their vertical flux of momentum (u(*)2) and sensible heat (H) counterparts. Also, the measured H is more horizontally homogeneous when compared to fluxes of other scalar entities such as CO2 and water vapour. While the spatial variability in fluxes was significant (> 15%), this unique data set confirmed that single tower measurements represent the 'canonical' structure of single-point RSL turbulence statistics, especially flux-variance relationships. Implications to extending the 'moving-equilibrium' hypothesis for RSL flows are discussed. The spatial variability in all RSL flow variables was not constant in time and varied strongly with spatially averaged friction velocity u(*), especially when u(*) was small. It is shown that flow properties derived from two-point temporal statistics such as correlation functions are more sensitive to local variability in leaf area density when compared to single point flow statistics. Specifically, that the local relationship between the reciprocal of the vertical velocity integral time scale (I(w)) and the arrival frequency of organized structures (u??/h) predicted from a mixing-layer theory exhibited dependence on the local leaf area index. The broader implications of these findings to the measurement and modelling of RSL flows are also discussed.

Gravitational-wave localization alone can probe origin of stellar-mass black hole mergers.

PubMed

Bartos, I; Haiman, Z; Marka, Z; Metzger, B D; Stone, N C; Marka, S

2017-10-10

The recent discovery of gravitational waves from stellar-mass binary black hole mergers by the Laser Interferometer Gravitational-wave Observatory opened the door to alternative probes of stellar and galactic evolution, cosmology and fundamental physics. Probing the origin of binary black hole mergers will be difficult due to the expected lack of electromagnetic emission and limited localization accuracy. Associations with rare host galaxy types-such as active galactic nuclei-can nevertheless be identified statistically through spatial correlation. Here we establish the feasibility of statistically proving the connection between binary black hole mergers and active galactic nuclei as hosts, even if only a sub-population of mergers originate from active galactic nuclei. Our results are the demonstration that the limited localization of gravitational waves, previously written off as not useful to distinguish progenitor channels, can in fact contribute key information, broadening the range of astrophysical questions probed by binary black hole observations.Binary black hole mergers have recently been observed through the detection of gravitational wave signatures. The authors demonstrate that their association with active galactic nuclei can be made through a statistical spatial correlation.

2-Point microstructure archetypes for improved elastic properties

NASA Astrophysics Data System (ADS)

Adams, Brent L.; Gao, Xiang

2004-01-01

Rectangular models of material microstructure are described by their 1- and 2-point (spatial) correlation statistics of placement of local state. In the procedure described here the local state space is described in discrete form; and the focus is on placement of local state within a finite number of cells comprising rectangular models. It is illustrated that effective elastic properties (generalized Hashin Shtrikman bounds) can be obtained that are linear in components of the correlation statistics. Within this framework the concept of an eigen-microstructure within the microstructure hull is useful. Given the practical innumerability of the microstructure hull, however, we introduce a method for generating a sequence of archetypes of eigen-microstructure, from the 2-point correlation statistics of local state, assuming that the 1-point statistics are stationary. The method is illustrated by obtaining an archetype for an imaginary two-phase material where the objective is to maximize the combination C_{xxxx}^{*} + C_{xyxy}^{*}

Local indicators of geocoding accuracy (LIGA): theory and application

PubMed Central

Jacquez, Geoffrey M; Rommel, Robert

2009-01-01

Background Although sources of positional error in geographic locations (e.g. geocoding error) used for describing and modeling spatial patterns are widely acknowledged, research on how such error impacts the statistical results has been limited. In this paper we explore techniques for quantifying the perturbability of spatial weights to different specifications of positional error. Results We find that a family of curves describes the relationship between perturbability and positional error, and use these curves to evaluate sensitivity of alternative spatial weight specifications to positional error both globally (when all locations are considered simultaneously) and locally (to identify those locations that would benefit most from increased geocoding accuracy). We evaluate the approach in simulation studies, and demonstrate it using a case-control study of bladder cancer in south-eastern Michigan. Conclusion Three results are significant. First, the shape of the probability distributions of positional error (e.g. circular, elliptical, cross) has little impact on the perturbability of spatial weights, which instead depends on the mean positional error. Second, our methodology allows researchers to evaluate the sensitivity of spatial statistics to positional accuracy for specific geographies. This has substantial practical implications since it makes possible routine sensitivity analysis of spatial statistics to positional error arising in geocoded street addresses, global positioning systems, LIDAR and other geographic data. Third, those locations with high perturbability (most sensitive to positional error) and high leverage (that contribute the most to the spatial weight being considered) will benefit the most from increased positional accuracy. These are rapidly identified using a new visualization tool we call the LIGA scatterplot. Herein lies a paradox for spatial analysis: For a given level of positional error increasing sample density to more accurately follow the underlying population distribution increases perturbability and introduces error into the spatial weights matrix. In some studies positional error may not impact the statistical results, and in others it might invalidate the results. We therefore must understand the relationships between positional accuracy and the perturbability of the spatial weights in order to have confidence in a study's results. PMID:19863795

A geographic analysis of individual and environmental risk factors for hypospadias births

PubMed Central

Winston, Jennifer J; Meyer, Robert E; Emch, Michael E

2014-01-01

Background Hypospadias is a relatively common birth defect affecting the male urinary tract. We explored the etiology of hypospadias by examining its spatial distribution in North Carolina and the spatial clustering of residuals from individual and environmental risk factors. Methods We used data collected by the North Carolina Birth Defects Monitoring Program from 2003-2005 to estimate local Moran's I statistics to identify geographic clustering of overall and severe hypospadias, using 995 overall cases and 16,013 controls. We conducted logistic regression and local Moran's I statistics on standardized residuals to consider the contribution of individual variables (maternal age, maternal race/ethnicity, maternal education, smoking, parity, and diabetes) and environmental variables (block group land cover) to this clustering. Results Local Moran's I statistics indicated significant clustering of overall and severe hypospadias in eastern central North Carolina. Spatial clustering of hypospadias persisted when controlling for individual factors, but diminished somewhat when controlling for environmental factors. In adjusted models, maternal residence in a block group with more than 5% crop cover was associated with overall hypospadias (OR = 1.22; 95% CI = 1.04 – 1.43); that is living in a block group with greater than 5% crop cover was associated with a 22% increase in the odds of having a baby with hypospadias. Land cover was not associated with severe hypospadias. Conclusions This study illustrates the potential contribution of mapping in generating hypotheses about disease etiology. Results suggest that environmental factors including proximity to agriculture may play some role in the spatial distribution of hypospadias. PMID:25196538

Evaluating the utility of companion animal tick surveillance practices for monitoring spread and occurrence of human Lyme disease in West Virginia, 2014-2016.

PubMed

Hendricks, Brian; Mark-Carew, Miguella; Conley, Jamison

2017-11-13

Domestic dogs and cats are potentially effective sentinel populations for monitoring occurrence and spread of Lyme disease. Few studies have evaluated the public health utility of sentinel programmes using geo-analytic approaches. Confirmed Lyme disease cases diagnosed by physicians and ticks submitted by veterinarians to the West Virginia State Health Department were obtained for 2014-2016. Ticks were identified to species, and only Ixodes scapularis were incorporated in the analysis. Separate ordinary least squares (OLS) and spatial lag regression models were conducted to estimate the association between average numbers of Ix. scapularis collected on pets and human Lyme disease incidence. Regression residuals were visualised using Local Moran's I as a diagnostic tool to identify spatial dependence. Statistically significant associations were identified between average numbers of Ix. scapularis collected from dogs and human Lyme disease in the OLS (β=20.7, P<0.001) and spatial lag (β=12.0, P=0.002) regression. No significant associations were identified for cats in either regression model. Statistically significant (P≤0.05) spatial dependence was identified in all regression models. Local Moran's I maps produced for spatial lag regression residuals indicated a decrease in model over- and under-estimation, but identified a higher number of statistically significant outliers than OLS regression. Results support previous conclusions that dogs are effective sentinel populations for monitoring risk of human exposure to Lyme disease. Findings reinforce the utility of spatial analysis of surveillance data, and highlight West Virginia's unique position within the eastern United States in regards to Lyme disease occurrence.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

A perceptual space of local image statistics.

PubMed

Victor, Jonathan D; Thengone, Daniel J; Rizvi, Syed M; Conte, Mary M

2015-12-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice - a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4min. In sum, local image statistics form a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

A perceptual space of local image statistics

PubMed Central

Victor, Jonathan D.; Thengone, Daniel J.; Rizvi, Syed M.; Conte, Mary M.

2015-01-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice – a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14 min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4 min. In sum, local image statistics forms a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. PMID:26130606

Evaluating collective significance of climatic trends: A comparison of methods on synthetic data

NASA Astrophysics Data System (ADS)

Huth, Radan; Dubrovský, Martin

2017-04-01

The common approach to determine whether climatic trends are significantly different from zero is to conduct individual (local) tests at each single site (station or gridpoint). Whether the number of sites where the trends are significantly non-zero can or cannot occur by random, is almost never evaluated in trend studies. That is, collective (global) significance of trends is ignored. We compare three approaches to evaluating collective statistical significance of trends at a network of sites, using the following statistics: (i) the number of successful local tests (a successful test means here a test in which the null hypothesis of no trend is rejected); this is a standard way of assessing collective significance in various applications in atmospheric sciences; (ii) the smallest p-value among the local tests (Walker test); and (iii) the counts of positive and negative trends regardless of their magnitudes and local significance. The third approach is a new procedure that we propose; the rationale behind it is that it is reasonable to assume that the prevalence of one sign of trends at individual sites is indicative of a high confidence in the trend not being zero, regardless of the (in)significance of individual local trends. A potentially large amount of information contained in trends that are not locally significant, which are typically deemed irrelevant and neglected, is thus not lost and is retained in the analysis. In this contribution we examine the feasibility of the proposed way of significance testing on synthetic data, produced by a multi-site stochastic generator, and compare it with the two other ways of assessing collective significance, which are well established now. The synthetic dataset, mimicking annual mean temperature on an array of stations (or gridpoints), is constructed assuming a given statistical structure characterized by (i) spatial separation (density of the station network), (ii) local variance, (iii) temporal and spatial autocorrelations, and (iv) the trend magnitude. The probabilistic distributions of the three test statistics (null distributions) and critical values of the tests are determined from multiple realizations of the synthetic dataset, in which no trend is imposed at each site (that is, any trend is a result of random fluctuations only). The procedure is then evaluated by determining the type II error (the probability of a false detection of a trend) in the presence of a trend with a known magnitude, for which the synthetic dataset with an imposed spatially uniform non-zero trend is used. A sensitivity analysis is conducted for various combinations of the trend magnitude and spatial autocorrelation.

Local Geostatistical Models and Big Data in Hydrological and Ecological Applications

NASA Astrophysics Data System (ADS)

Hristopulos, Dionissios

2015-04-01

The advent of the big data era creates new opportunities for environmental and ecological modelling but also presents significant challenges. The availability of remote sensing images and low-cost wireless sensor networks implies that spatiotemporal environmental data to cover larger spatial domains at higher spatial and temporal resolution for longer time windows. Handling such voluminous data presents several technical and scientific challenges. In particular, the geostatistical methods used to process spatiotemporal data need to overcome the dimensionality curse associated with the need to store and invert large covariance matrices. There are various mathematical approaches for addressing the dimensionality problem, including change of basis, dimensionality reduction, hierarchical schemes, and local approximations. We present a Stochastic Local Interaction (SLI) model that can be used to model local correlations in spatial data. SLI is a random field model suitable for data on discrete supports (i.e., regular lattices or irregular sampling grids). The degree of localization is determined by means of kernel functions and appropriate bandwidths. The strength of the correlations is determined by means of coefficients. In the "plain vanilla" version the parameter set involves scale and rigidity coefficients as well as a characteristic length. The latter determines in connection with the rigidity coefficient the correlation length of the random field. The SLI model is based on statistical field theory and extends previous research on Spartan spatial random fields [2,3] from continuum spaces to explicitly discrete supports. The SLI kernel functions employ adaptive bandwidths learned from the sampling spatial distribution [1]. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. Hence, covariance matrix inversion is not necessary for parameter inference that is based on leave-one-out cross validation. This property helps to overcome a significant computational bottleneck of geostatistical models due to the poor scaling of the matrix inversion [4,5]. We present applications to real and simulated data sets, including the Walker lake data, and we investigate the SLI performance using various statistical cross validation measures. References [1] T. Hofmann, B. Schlkopf, A.J. Smola, Annals of Statistics, 36, 1171-1220 (2008). [2] D. T. Hristopulos, SIAM Journal on Scientific Computing, 24(6): 2125-2162 (2003). [3] D. T. Hristopulos and S. N. Elogne, IEEE Transactions on Signal Processing, 57(9): 3475-3487 (2009) [4] G. Jona Lasinio, G. Mastrantonio, and A. Pollice, Statistical Methods and Applications, 22(1):97-112 (2013) [5] Sun, Y., B. Li, and M. G. Genton (2012). Geostatistics for large datasets. In: Advances and Challenges in Space-time Modelling of Natural Events, Lecture Notes in Statistics, pp. 55-77. Springer, Berlin-Heidelberg.

Spatially disaggregated population estimates in the absence of national population and housing census data

PubMed Central

Wardrop, N. A.; Jochem, W. C.; Bird, T. J.; Chamberlain, H. R.; Clarke, D.; Kerr, D.; Bengtsson, L.; Juran, S.; Seaman, V.; Tatem, A. J.

2018-01-01

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data. PMID:29555739

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China.

PubMed

Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu

2014-05-19

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.

An Optical Flow-Based Full Reference Video Quality Assessment Algorithm.

PubMed

K, Manasa; Channappayya, Sumohana S

2016-06-01

We present a simple yet effective optical flow-based full-reference video quality assessment (FR-VQA) algorithm for assessing the perceptual quality of natural videos. Our algorithm is based on the premise that local optical flow statistics are affected by distortions and the deviation from pristine flow statistics is proportional to the amount of distortion. We characterize the local flow statistics using the mean, the standard deviation, the coefficient of variation (CV), and the minimum eigenvalue ( λ min ) of the local flow patches. Temporal distortion is estimated as the change in the CV of the distorted flow with respect to the reference flow, and the correlation between λ min of the reference and of the distorted patches. We rely on the robust multi-scale structural similarity index for spatial quality estimation. The computed temporal and spatial distortions, thus, are then pooled using a perceptually motivated heuristic to generate a spatio-temporal quality score. The proposed method is shown to be competitive with the state-of-the-art when evaluated on the LIVE SD database, the EPFL Polimi SD database, and the LIVE Mobile HD database. The distortions considered in these databases include those due to compression, packet-loss, wireless channel errors, and rate-adaptation. Our algorithm is flexible enough to allow for any robust FR spatial distortion metric for spatial distortion estimation. In addition, the proposed method is not only parameter-free but also independent of the choice of the optical flow algorithm. Finally, we show that the replacement of the optical flow vectors in our proposed method with the much coarser block motion vectors also results in an acceptable FR-VQA algorithm. Our algorithm is called the flow similarity index.

Tendency to occupy a statistically dominant spatial state of the flow as a driving force for turbulent transition.

PubMed

Chekmarev, Sergei F

2013-03-01

The transition from laminar to turbulent fluid motion occurring at large Reynolds numbers is generally associated with the instability of the laminar flow. On the other hand, since the turbulent flow characteristically appears in the form of spatially localized structures (e.g., eddies) filling the flow field, a tendency to occupy such a structured state of the flow cannot be ruled out as a driving force for turbulent transition. To examine this possibility, we propose a simple analytical model that treats the flow as a collection of localized spatial structures, each of which consists of elementary cells in which the behavior of the particles (atoms or molecules) is uncorrelated. This allows us to introduce the Reynolds number, associating it with the ratio between the total phase volume for the system and that for the elementary cell. Using the principle of maximum entropy to calculate the most probable size distribution of the localized structures, we show that as the Reynolds number increases, the elementary cells group into the localized structures, which successfully explains turbulent transition and some other general properties of turbulent flows. An important feature of the present model is that a bridge between the spatial-statistical description of the flow and hydrodynamic equations is established. We show that the basic assumptions underlying the model, i.e., that the particles are indistinguishable and elementary volumes of phase space exist in which the state of the particles is uncertain, are involved in the derivation of the Navier-Stokes equation. Taking into account that the model captures essential features of turbulent flows, this suggests that the driving force for the turbulent transition is basically the same as in the present model, i.e., the tendency of the system to occupy a statistically dominant state plays a key role. The instability of the flow at high Reynolds numbers can then be a mechanism to initiate structural rearrangement of the flow to find this state.

Changing Pattern of Indian Monsoon Extremes: Global and Local Factors

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Shastri, Hiteshri; Pathak, Amey; Paul, Supantha

2017-04-01

Indian Summer Monsoon Rainfall (ISMR) extremes have remained a major topic of discussion in the field of global change and hydro-climatology over the last decade. This attributes to multiple conclusions on changing pattern of extremes along with poor understanding of multiple processes at global and local scales associated with monsoon extremes. At a spatially aggregate scale, when number of extremes in the grids are summed over, a statistically significant increasing trend is observed for both Central India (Goswami et al., 2006) and all India (Rajeevan et al., 2008). However, such a result over Central India does not satisfy flied significance test of increase and no decrease (Krishnamurthy et al., 2009). Statistically rigorous extreme value analysis that deals with the tail of the distribution reveals a spatially non-uniform trend of extremes over India (Ghosh et al., 2012). This results into statistically significant increasing trend of spatial variability. Such an increase of spatial variability points to the importance of local factors such as deforestation and urbanization. We hypothesize that increase of spatial average of extremes is associated with the increase of events occurring over large region, while increase in spatial variability attributes to local factors. A Lagrangian approach based dynamic recycling model reveals that the major contributor of moisture to wide spread extremes is Western Indian Ocean, while land surface also contributes around 25-30% of moisture during the extremes in Central India. We further test the impacts of local urbanization on extremes and find the impacts are more visible over West central, Southern and North East India. Regional atmospheric simulations coupled with Urban Canopy Model (UCM) shows that urbanization intensifies extremes in city areas, but not uniformly all over the city. The intensification occurs over specific pockets of the urban region, resulting an increase in spatial variability even within the city. This also points to the need of setting up multiple weather stations over the city at a finer resolution for better understanding of urban extremes. We conclude that the conventional method of considering large scale factors is not sufficient for analysing the monsoon extremes and characterization of the same needs a blending of both global and local factors. Ghosh, S., Das, D., Kao, S-C. & Ganguly, A. R. Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Clim. Change 2, 86-91 (2012) Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. & Xavier, P. K. Increasing trend of extreme rain events over India in a warming environment. Science 314, 1442-1445 (2006). Krishnamurthy, C. K. B., Lall, U. & Kwon, H-H. Changing frequency and intensity of rainfall extremes over India from 1951 to 2003. J. Clim. 22, 4737-4746 (2009). Rajeevan, M., Bhate, J. & Jaswal, A. K. Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys. Res. Lett. 35, L18707 (2008).

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

Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures.

PubMed

Macià, Dídac; Pujol, Jesus; Blanco-Hinojo, Laura; Martínez-Vilavella, Gerard; Martín-Santos, Rocío; Deus, Joan

2018-06-01

There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.

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.

Continuous measurement of two spatially separated superconducting qubits: quantum trajectories and statistics

NASA Astrophysics Data System (ADS)

Roch, Nicolas

2015-03-01

Measurement can be harnessed to probabilistically generate entanglement in the absence of local interactions, for example between spatially separated quantum objects. Continuous weak measurement allows us to observe the dynamics associated with this process. In particular, we perform joint dispersive readout of two superconducting transmon qubits separated by one meter of coaxial cable. We track the evolution of a joint quantum state under the influence of measurement, both as an ensemble and as a set of individual quantum trajectories. Analyzing the statistics of such quantum trajectories can shed new light on the underlying entangling mechanism.

Formulating Spatially Varying Performance in the Statistical Fusion Framework

PubMed Central

Landman, Bennett A.

2012-01-01

To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. PMID:22438513

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches' knowledge production function: a mixed GWR approach

NASA Astrophysics Data System (ADS)

Kang, Dongwoo; Dall'erba, Sandy

2016-04-01

Griliches' knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors' previous work (Kang and Dall'erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county's innovation capacity and suggest policy implications for regional innovation strategies.

Temporal scaling and spatial statistical analyses of groundwater level fluctuations

NASA Astrophysics Data System (ADS)

Sun, H.; Yuan, L., Sr.; Zhang, Y.

2017-12-01

Natural dynamics such as groundwater level fluctuations can exhibit multifractionality and/or multifractality due likely to multi-scale aquifer heterogeneity and controlling factors, whose statistics requires efficient quantification methods. This study explores multifractionality and non-Gaussian properties in groundwater dynamics expressed by time series of daily level fluctuation at three wells located in the lower Mississippi valley, after removing the seasonal cycle in the temporal scaling and spatial statistical analysis. First, using the time-scale multifractional analysis, a systematic statistical method is developed to analyze groundwater level fluctuations quantified by the time-scale local Hurst exponent (TS-LHE). Results show that the TS-LHE does not remain constant, implying the fractal-scaling behavior changing with time and location. Hence, we can distinguish the potentially location-dependent scaling feature, which may characterize the hydrology dynamic system. Second, spatial statistical analysis shows that the increment of groundwater level fluctuations exhibits a heavy tailed, non-Gaussian distribution, which can be better quantified by a Lévy stable distribution. Monte Carlo simulations of the fluctuation process also show that the linear fractional stable motion model can well depict the transient dynamics (i.e., fractal non-Gaussian property) of groundwater level, while fractional Brownian motion is inadequate to describe natural processes with anomalous dynamics. Analysis of temporal scaling and spatial statistics therefore may provide useful information and quantification to understand further the nature of complex dynamics in hydrology.

Spatially disaggregated population estimates in the absence of national population and housing census data.

PubMed

Wardrop, N A; Jochem, W C; Bird, T J; Chamberlain, H R; Clarke, D; Kerr, D; Bengtsson, L; Juran, S; Seaman, V; Tatem, A J

2018-04-03

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data. Copyright © 2018 the Author(s). Published by PNAS.

A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Sig-NganLam, Nina; Quattrochi, Dale A.

2004-01-01

The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

Attempting to physically explain space-time correlation of extremes

NASA Astrophysics Data System (ADS)

Bernardara, Pietro; Gailhard, Joel

2010-05-01

Spatial and temporal clustering of hydro-meteorological extreme events is scientific evidence. Moreover, the statistical parameters characterizing their local frequencies of occurrence show clear spatial patterns. Thus, in order to robustly assess the hydro-meteorological hazard, statistical models need to be able to take into account spatial and temporal dependencies. Statistical models considering long term correlation for quantifying and qualifying temporal and spatial dependencies are available, such as multifractal approach. Furthermore, the development of regional frequency analysis techniques allows estimating the frequency of occurrence of extreme events taking into account spatial patterns on the extreme quantiles behaviour. However, in order to understand the origin of spatio-temporal clustering, an attempt to find physical explanation should be done. Here, some statistical evidences of spatio-temporal correlation and spatial patterns of extreme behaviour are given on a large database of more than 400 rainfall and discharge series in France. In particular, the spatial distribution of multifractal and Generalized Pareto distribution parameters shows evident correlation patterns in the behaviour of frequency of occurrence of extremes. It is then shown that the identification of atmospheric circulation pattern (weather types) can physically explain the temporal clustering of extreme rainfall events (seasonality) and the spatial pattern of the frequency of occurrence. Moreover, coupling this information with the hydrological modelization of a watershed (as in the Schadex approach) an explanation of spatio-temporal distribution of extreme discharge can also be provided. We finally show that a hydro-meteorological approach (as the Schadex approach) can explain and take into account space and time dependencies of hydro-meteorological extreme events.

Geographical Distribution Patterns of Iodine in Drinking-Water and Its Associations with Geological Factors in Shandong Province, China

PubMed Central

Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu

2014-01-01

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran’s I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors. PMID:24852390

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.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies.

PubMed

Emch, Michael; Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2012-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies

PubMed Central

Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how. PMID:24163443

Complex polarization-phase and spatial-frequency selections of laser images of blood-plasma films in diagnostics of changes in their polycrystalline structure

NASA Astrophysics Data System (ADS)

Ushenko, Yu. A.; Angelskii, P. O.; Dubolazov, A. V.; Karachevtsev, A. O.; Sidor, M. I.; Mintser, O. P.; Oleinichenko, B. P.; Bizer, L. I.

2013-10-01

We present a theoretical formalism of correlation phase analysis of laser images of human blood plasma with spatial-frequency selection of manifestations of mechanisms of linear and circular birefringence of albumin and globulin polycrystalline networks. Comparative results of the measurement of coordinate distributions of the correlation parameter—the modulus of the degree of local correlation of amplitudes—of laser images of blood plasma taken from patients of three groups—healthy patients (donors), rheumatoid-arthritis patients, and breast-cancer patients—are presented. We investigate values and ranges of change of statistical (the first to fourth statistical moments), correlation (excess of autocorrelation functions), and fractal (slopes of approximating curves and dispersion of extrema of logarithmic dependences of power spectra) parameters of coordinate distributions of the degree of local correlation of amplitudes. Objective criteria for diagnostics of occurrence and differentiation of inflammatory and oncological states are determined.

Localization in quantum field theory

NASA Astrophysics Data System (ADS)

Balachandran, A. P.

In non-relativistic quantum mechanics, Born’s principle of localization is as follows: For a single particle, if a wave function ψK vanishes outside a spatial region K, it is said to be localized in K. In particular, if a spatial region K‧ is disjoint from K, a wave function ψK‧ localized in K‧ is orthogonal to ψK. Such a principle of localization does not exist compatibly with relativity and causality in quantum field theory (QFT) (Newton and Wigner) or interacting point particles (Currie, Jordan and Sudarshan). It is replaced by symplectic localization of observables as shown by Brunetti, Guido and Longo, Schroer and others. This localization gives a simple derivation of the spin-statistics theorem and the Unruh effect, and shows how to construct quantum fields for anyons and for massless particles with “continuous” spin. This review outlines the basic principles underlying symplectic localization and shows or mentions its deep implications. In particular, it has the potential to affect relativistic quantum information theory and black hole physics.

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.

Damage localization by statistical evaluation of signal-processed mode shapes

NASA Astrophysics Data System (ADS)

Ulriksen, M. D.; Damkilde, L.

2015-07-01

Due to their inherent, ability to provide structural information on a local level, mode shapes and t.lieir derivatives are utilized extensively for structural damage identification. Typically, more or less advanced mathematical methods are implemented to identify damage-induced discontinuities in the spatial mode shape signals, hereby potentially facilitating damage detection and/or localization. However, by being based on distinguishing damage-induced discontinuities from other signal irregularities, an intrinsic deficiency in these methods is the high sensitivity towards measurement, noise. The present, article introduces a damage localization method which, compared to the conventional mode shape-based methods, has greatly enhanced robustness towards measurement, noise. The method is based on signal processing of spatial mode shapes by means of continuous wavelet, transformation (CWT) and subsequent, application of a generalized discrete Teager-Kaiser energy operator (GDTKEO) to identify damage-induced mode shape discontinuities. In order to evaluate whether the identified discontinuities are in fact, damage-induced, outlier analysis of principal components of the signal-processed mode shapes is conducted on the basis of T2-statistics. The proposed method is demonstrated in the context, of analytical work with a free-vibrating Euler-Bernoulli beam under noisy conditions.

Spatial interpolation of monthly mean air temperature data for Latvia

NASA Astrophysics Data System (ADS)

Aniskevich, Svetlana

2016-04-01

Temperature data with high spatial resolution are essential for appropriate and qualitative local characteristics analysis. Nowadays the surface observation station network in Latvia consists of 22 stations recording daily air temperature, thus in order to analyze very specific and local features in the spatial distribution of temperature values in the whole Latvia, a high quality spatial interpolation method is required. Until now inverse distance weighted interpolation was used for the interpolation of air temperature data at the meteorological and climatological service of the Latvian Environment, Geology and Meteorology Centre, and no additional topographical information was taken into account. This method made it almost impossible to reasonably assess the actual temperature gradient and distribution between the observation points. During this project a new interpolation method was applied and tested, considering auxiliary explanatory parameters. In order to spatially interpolate monthly mean temperature values, kriging with external drift was used over a grid of 1 km resolution, which contains parameters such as 5 km mean elevation, continentality, distance from the Gulf of Riga and the Baltic Sea, biggest lakes and rivers, population density. As the most appropriate of these parameters, based on a complex situation analysis, mean elevation and continentality was chosen. In order to validate interpolation results, several statistical indicators of the differences between predicted values and the values actually observed were used. Overall, the introduced model visually and statistically outperforms the previous interpolation method and provides a meteorologically reasonable result, taking into account factors that influence the spatial distribution of the monthly mean temperature.

Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity

PubMed Central

2018-01-01

Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions. PMID:29465399

Quantum jumps on Anderson attractors

NASA Astrophysics Data System (ADS)

Yusipov, I. I.; Laptyeva, T. V.; Ivanchenko, M. V.

2018-01-01

In a closed single-particle quantum system, spatial disorder induces Anderson localization of eigenstates and halts wave propagation. The phenomenon is vulnerable to interaction with environment and decoherence that is believed to restore normal diffusion. We demonstrate that for a class of experimentally feasible non-Hermitian dissipators, which admit signatures of localization in asymptotic states, quantum particle opts between diffusive and ballistic regimes, depending on the phase parameter of dissipators, with sticking about localization centers. In a diffusive regime, statistics of quantum jumps is non-Poissonian and has a power-law interval, a footprint of intermittent locking in Anderson modes. Ballistic propagation reflects dispersion of an ordered lattice and introduces the second timescale for jumps, resulting in non-nonmonotonous probability distribution. Hermitian dephasing dissipation makes localization features vanish, and Poissonian jump statistics along with normal diffusion are recovered.

Experiments with central-limit properties of spatial samples from locally covariant random fields

USGS Publications Warehouse

Barringer, T.H.; Smith, T.E.

1992-01-01

When spatial samples are statistically dependent, the classical estimator of sample-mean standard deviation is well known to be inconsistent. For locally dependent samples, however, consistent estimators of sample-mean standard deviation can be constructed. The present paper investigates the sampling properties of one such estimator, designated as the tau estimator of sample-mean standard deviation. In particular, the asymptotic normality properties of standardized sample means based on tau estimators are studied in terms of computer experiments with simulated sample-mean distributions. The effects of both sample size and dependency levels among samples are examined for various value of tau (denoting the size of the spatial kernel for the estimator). The results suggest that even for small degrees of spatial dependency, the tau estimator exhibits significantly stronger normality properties than does the classical estimator of standardized sample means. ?? 1992.

Outlier identification in urban soils and its implications for identification of potential contaminated land

NASA Astrophysics Data System (ADS)

Zhang, Chaosheng

2010-05-01

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.

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

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

PubMed Central

Goovaerts, Pierre

2006-01-01

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e. boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e. edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test. PMID:19023455

Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.

PubMed

Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

2017-01-01

Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and among boys (OR 1.28; 95%BCI; 1.12-1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting. Stunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.

The performance of the spatiotemporal Kalman filter and LORETA in seizure onset localization.

PubMed

Hamid, Laith; Sarabi, Masoud; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The assumption of spatial-smoothness is often used to solve the bioelectric inverse problem during electroencephalographic (EEG) source imaging, e.g., in low resolution electromagnetic tomography (LORETA). Since the EEG data show a temporal structure, the combination of the temporal-smoothness and the spatial-smoothness constraints may improve the solution of the EEG inverse problem. This study investigates the performance of the spatiotemporal Kalman filter (STKF) method, which is based on spatial and temporal smoothness, in the localization of a focal seizure's onset and compares its results to those of LORETA. The main finding of the study was that the STKF with an autoregressive model of order two significantly outperformed LORETA in the accuracy and consistency of the localization, provided that the source space consists of a whole-brain volumetric grid. In the future, these promising results will be confirmed using data from more patients and performing statistical analyses on the results. Furthermore, the effects of the temporal smoothness constraint will be studied using different types of focal seizures.

Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Feng, David Dagan; Luo, Jianhua; Huang, Qiu

2013-01-01

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Influence of Scale Effect and Model Performance in Downscaling ASTER Land Surface Temperatures to a Very High Spatial Resolution in an Agricultural Area

NASA Astrophysics Data System (ADS)

Zhou, J.; Li, G.; Liu, S.; Zhan, W.; Zhang, X.

2015-12-01

At present land surface temperatures (LSTs) can be generated from thermal infrared remote sensing with spatial resolutions from ~100 m to tens of kilometers. However, LSTs with high spatial resolution, e.g. tens of meters, are still lack. The purpose of LST downscaling is to generate LSTs with finer spatial resolutions than their native spatial resolutions. The statistical linear or nonlinear regression models are most frequently used for LST downscaling. The basic assumption of these models is the scale-invariant relationships between LST and its descriptors, which is questioned but rare researches have been reported. In addition, few researches can be found for downscaling satellite LST or TIR data to a high spatial resolution, i.e. better than 100 m or even finer. The lack of LST with high spatial resolution cannot satisfy the requirements of applications such as evapotranspiration mapping at the field scale. By selecting a dynamically developing agricultural oasis as the study area, the aim of this study is to downscale the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LSTs to 15 m, to satisfy the requirement of evapotranspiration mapping at the field scale. Twelve ASTER images from May to September in 2012, covering the entire growth stage of maize, were selected. Four statistical models were evaluated, including one global model, one piecewise model, and two local models. The influence from scale effect in downscaling LST was quantified. The downscaled LSTs are evaluated from accuracy and image quality. Results demonstrate that the influence from scale effect varies according to models and the maize growth stage. Significant influence about -4 K to 6 K existed at the early stage and weaker influence existed in the middle stage. When compared with the ground measured LSTs, the downscaled LSTs resulted from the global and local models yielded higher accuracies and better image qualities than the local models. In addition to the vegetation indices, the surface albedo is an important descriptor for downscaling LST through explaining its spatial variation induced by soil moisture.

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

The spatiotemporal characteristics of environmental hazards caused by offshore oil and gas operations in the Gulf of Mexico.

PubMed

Meng, Qingmin

2016-09-15

Marine ecosystems are home to a host of numerous species ranging from tiny planktonic organisms, fishes, and birds, to large mammals such as the whales, manatees, and seals. However, human activities such as offshore oil and gas operations increasingly threaten marine and coastal ecosystems, for which there has been little exploration into the spatial and temporal risks of offshore oil operations. Using the Gulf of Mexico, one of the world's hottest spots of offshore oil and gas mining, as the study area, we propose a spatiotemporal approach that integrates spatial statistics and geostatistics in a geographic information system environment to provide insight to environmental management and decision making for oil and gas operators, coastal communities, local governments, and the federal government. We use the records from 1995 to 2015 of twelve types of hazards caused by offshore oil and gas operations, and analyze them spatially over a five year period. The spatial clusters of these hazards are analyzed and mapped using Getis-Ord Gi and local Moran's I statistics. We then design a spatial correlation coefficient matrix for multivariate spatial correlation, which is the ratio of the cross variogram of two types of hazards to the product of the variograms of the two hazards, showing a primary understanding of the degrees of spatial correlation among the twelve types hazards. To the best of our knowledge, it is the first application of spatiotemporal analysis methods to environmental hazards caused by offshore oil and gas operations; the proposed methods can be applied to other regions for the management and monitoring of environmental hazards caused by offshore oil operations. Copyright © 2016 Elsevier B.V. All rights reserved.

Socio-Spatial Patterning of Off-Sale and On-Sale Alcohol Outlets in a Texas City

PubMed Central

Han, Daikwon; Gorman, Dennis M.

2014-01-01

Introduction and Aims To examine the socio-spatial patterning of off-sale and on-sale alcohol outlets following a policy change that ended prohibition of off-sale outlets in Lubbock, Texas. Design and Methods The spatial patterning of alcohol outlets by licensing type was examined using the k-function difference (D statistic) to compare the relative degree of spatial aggregation of the two types of alcohol outlets and by the spatial scan statistic to identify statistically significant geographic clusters of outlets. The sociodemographic characteristics of the areas containing clusters of outlets were compared to the rest of the city. In addition, the socioeconomic characteristics of census block groups with and without existing on-sale outlets were compared, as were the socioeconomic characteristics of census block groups with and without the newly issued off-sale licenses. Results The existing on-sale premises in Lubbock and the newly established off-sale premises introduced as a result of the 2009 policy change displayed different spatial patterns, with the latter being more spatially dispersed. A large cluster of on-sale outlets identified in the north-east of the city was located in a socially and economically disadvantaged area of the city. Discussion and Conclusion The findings support the view that it is important to understand the local context of deprivation within a city when examining the location of alcohol outlets and add to the existing research by drawing attention to the importance of geographic scale in assessing such relationships. PMID:24320205

Socio-spatial patterning of off-sale and on-sale alcohol outlets in a Texas city.

PubMed

Han, Daikwon; Gorman, Dennis M

2014-03-01

To examine the socio-spatial patterning of off-sale and on-sale alcohol outlets following a policy change that ended prohibition of off-sale outlets in Lubbock, Texas. The spatial patterning of alcohol outlets by licensing type was examined using the k-function difference (D statistic) to compare the relative degree of spatial aggregation of the two types of alcohol outlets and by the spatial scan statistic to identify statistically significant geographic clusters of outlets. The sociodemographic characteristics of the areas containing clusters of outlets were compared with the rest of the city. In addition, the socioeconomic characteristics of census block groups with and without existing on-sale outlets were compared, as were the socioeconomic characteristics of census block groups with and without the newly issued off-sale licenses. The existing on-sale premises in Lubbock and the newly established off-sale premises introduced as a result of the 2009 policy change displayed different spatial patterns, with the latter being more spatially dispersed. A large cluster of on-sale outlets identified in the north-east of the city was located in a socially and economically disadvantaged area of the city. The findings support the view that it is important to understand the local context of deprivation within a city when examining the location of alcohol outlets and add to the existing research by drawing attention to the importance of geographic scale in assessing such relationships. © 2013 Australasian Professional Society on Alcohol and other Drugs.

Statistical Quality Control of Moisture Data in GEOS DAS

NASA Technical Reports Server (NTRS)

Dee, D. P.; Rukhovets, L.; Todling, R.

1999-01-01

A new statistical quality control algorithm was recently implemented in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The final step in the algorithm consists of an adaptive buddy check that either accepts or rejects outlier observations based on a local statistical analysis of nearby data. A basic assumption in any such test is that the observed field is spatially coherent, in the sense that nearby data can be expected to confirm each other. However, the buddy check resulted in excessive rejection of moisture data, especially during the Northern Hemisphere summer. The analysis moisture variable in GEOS DAS is water vapor mixing ratio. Observational evidence shows that the distribution of mixing ratio errors is far from normal. Furthermore, spatial correlations among mixing ratio errors are highly anisotropic and difficult to identify. Both factors contribute to the poor performance of the statistical quality control algorithm. To alleviate the problem, we applied the buddy check to relative humidity data instead. This variable explicitly depends on temperature and therefore exhibits a much greater spatial coherence. As a result, reject rates of moisture data are much more reasonable and homogeneous in time and space.

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

Dental Workforce Availability and Dental Services Utilization in Appalachia: A Geospatial Analysis

PubMed Central

Feng, Xue; Sambamoorthi, Usha; Wiener, R. Constance

2016-01-01

Objectives There is considerable variation in dental services utilization across Appalachian counties, and a plausible explanation is that individuals in some geographical areas do not utilize dental care due to dental workforce shortage. We conducted an ecological study on dental workforce availability and dental services utilization in Appalachia. Methods We derived county-level (n = 364) data on demographic, socio-economic characteristics and dental services utilization in Appalachia from the 2010 Behavioral Risk Factor Surveillance System (BRFSS) using person-level data. We obtained county-level dental workforce availability and physician-to-population ratio estimates from Area Health Resource File, and linked them to the county-level BRFSS data. The dependent variable was the proportion using dental services within the last year in each county (ranging from 16.6% to 91.0%). We described the association between dental workforce availability and dental services utilization using ordinary least squares regression and spatial regression techniques. Spatial analyses consisted of bivariate Local Indicators of Spatial Association (LISA) and geographically weighted regression (GWR). Results Bivariate LISA showed that counties in the central and southern Appalachian regions had significant (p < .05) low-low spatial clusters (low dental workforce availability, low percent dental services utilization). GWR revealed considerable local variations in the association between dental utilization and dental workforce availability. In the multivariate GWR models, 8.5% (t-statistics >1.96) and 13.45% (t-statistics >1.96) of counties showed positive and statistically significant relationships between the dental services utilization and workforce availability of dentists and dental hygienists, respectively. Conclusions Dental workforce availability was associated with dental services utilization in the Appalachian region; however, this association was not statistically significant in all counties. The findings suggest that program and policy efforts to improve dental services utilization need to focus on factors other than increasing the dental workforce availability for many counties in Appalachia. PMID:27957773

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.

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.

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land.

PubMed

Zhang, Chaosheng; Tang, Ya; Luo, Lin; Xu, Weilin

2009-11-01

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.

Exploratory Temporal and Spatial Analysis of Myocardial Infarction Hospitalizations in Calgary, Canada

PubMed Central

Liu, Xiaoxiao; Bertazzon, Stefania

2017-01-01

Spatial and temporal analyses are critical to understand the pattern of myocardial infarction (MI) hospitalizations over space and time, and to identify their underlying determinants. In this paper, we analyze MI hospitalizations in Calgary from 2004 to 2013, stratified by age and gender. First, a seasonal trend decomposition analyzes the seasonality; then a linear regression models the trend component. Moran’s I and hot spot analyses explore the spatial pattern. Though exploratory, results show that most age and gender groups feature a statistically significant decline over the 10 years, consistent with previous studies in Canada. Decline rates vary across ages and genders, with the slowest decline observed for younger males. Each gender exhibits a seasonal pattern with peaks in both winter and summer. Spatially, MI hot spots are identified in older communities, and in socioeconomically and environmentally disadvantaged communities. In the older communities, higher MI rates appear to be more highly associated with demographics. Conversely, worse air quality appears to be locally associated with higher MI incidence in younger age groups. The study helps identify areas of concern, where MI hot spots are identified for younger age groups, suggesting the need for localized public health policies to target local risk factors. PMID:29232910

Spatial Statistics of atmospheric particulate matter in China

NASA Astrophysics Data System (ADS)

Huang, Yongxiang; Wang, Yangjun; Liu, Yulu

2017-04-01

In this work, the spatial dynamics of the atmospheric particulate matters (resp. PM10 and PM2.5) are studied using turbulence methodologies. The hourly concentrations of particulate matter were released by the Chinese government (http://www.cnemc.cn). We first processed these data into daily average concentrations. Totally, there are 305 monitor stations with an observations period of 425 days. It is found experimentally that the spatial correlation function ρ(r) shows a log-law on the mesoscale range, i.e., 50 ≤ r ≤ 500 km, with an experimental scaling exponent β = 0.45. The spatial structure function shows a power-law behavior on the mesoscale range 90 ≤ r ≤ 500 km. The experimental scaling exponent ζ(q) is convex, showing that the intermittent correction is relevant in characterizing the spatial dynamics of particulate matter. The measured singularity spectrum f(α) also shows its multifractal nature. Experimentally, the particulate matter is more intermittent than the passive scalar, which could be partially due to the mesoscale movements of the atmosphere, and also due to local sources, such as local industry activities.

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.

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.

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

NASA Technical Reports Server (NTRS)

Itterly, Kyle F.; Taylor, Patrick C.

2016-01-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Spatial and temporal patterns of dengue infections in Timor-Leste, 2005-2013.

PubMed

Wangdi, Kinley; Clements, Archie C A; Du, Tai; Nery, Susana Vaz

2018-01-04

Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characteristics and the distribution and transmission of the disease. Notifications of dengue cases that occurred from January 2005 to December 2013 were obtained from the Ministry of Health, Timor-Leste. The population of each suco (the third-level administrative subdivision) was obtained from the Population and Housing Census 2010. Spatial autocorrelation in dengue incidence was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate, Zero-Inflated, Poisson (ZIP) regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. The analysis used data from 3206 cases. Dengue incidence was highly seasonal with a large peak in January. Patients ≥ 14 years were found to be 74% [95% credible interval (CrI): 72-76%] less likely to be infected than those < 14 years, and females were 12% (95% CrI: 4-21%) more likely to suffer from dengue as compared to males. Dengue incidence increased by 0.7% (95% CrI: 0.6-0.8%) for a 1 °C increase in mean temperature; and 47% (95% CrI: 29-59%) for a 1 mm increase in precipitation. There was no significant residual spatial clustering after accounting for climate and demographic variables. Dengue incidence was highly seasonal and spatially clustered, with positive associations with temperature, precipitation and demographic factors. These factors explained the observed spatial heterogeneity of infection.

GEMAS: Spatial pattern analysis of Ni by using digital image processing techniques on European agricultural soil data

NASA Astrophysics Data System (ADS)

Jordan, Gyozo; Petrik, Attila; De Vivo, Benedetto; Albanese, Stefano; Demetriades, Alecos; Sadeghi, Martiya

2017-04-01

Several studies have investigated the spatial distribution of chemical elements in topsoil (0-20 cm) within the framework of the EuroGeoSurveys Geochemistry Expert Group's 'Geochemical Mapping of Agricultural and Grazing Land Soil' project . Most of these studies used geostatistical analyses and interpolated concentration maps, Exploratory and Compositional Data and Analysis to identify anomalous patterns. The objective of our investigation is to demonstrate the use of digital image processing techniques for reproducible spatial pattern recognition and quantitative spatial feature characterisation. A single element (Ni) concentration in agricultural topsoil is used to perform the detailed spatial analysis, and to relate these features to possible underlying processes. In this study, simple univariate statistical methods were implemented first, and Tukey's inner-fence criterion was used to delineate statistical outliers. The linear and triangular irregular network (TIN) interpolation was used on the outlier-free Ni data points, which was resampled to a 10*10 km grid. Successive moving average smoothing was applied to generalise the TIN model and to suppress small- and at the same time enhance significant large-scale features of Nickel concentration spatial distribution patterns in European topsoil. The TIN map smoothed with a moving average filter revealed the spatial trends and patterns without losing much detail, and it was used as the input into digital image processing, such as local maxima and minima determination, digital cross sections, gradient magnitude and gradient direction calculation, second derivative profile curvature calculation, edge detection, local variability assessment, lineament density and directional variogram analyses. The detailed image processing analysis revealed several NE-SW, E-W and NW-SE oriented elongated features, which coincide with different spatial parameter classes and alignment with local maxima and minima. The NE-SW oriented linear pattern is the dominant feature to the south of the last glaciation limit. Some of these linear features are parallel to the suture zone of the Iapetus Ocean, while the others follow the Alpine and Carpathian Chains. The highest variability zones of Ni concentration in topsoil are located in the Alps and in the Balkans where mafic and ultramafic rocks outcrop. The predominant NE-SW oriented pattern is also captured by the strong anisotropy in the semi-variograms in this direction. A single major E-W oriented north-facing feature runs along the southern border of the last glaciation zone. This zone also coincides with a series of local maxima in Ni concentration along the glaciofluvial deposits. The NW-SE elongated spatial features are less dominant and are located in the Pyrenees and Scandinavia. This study demonstrates the efficiency of systematic image processing analysis in identifying and characterising spatial geochemical patterns that often remain uncovered by the usual visual map interpretation techniques.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models.

PubMed

Lovejoy, S; de Lima, M I P

2015-07-01

Over the range of time scales from about 10 days to 30-100 years, in addition to the familiar weather and climate regimes, there is an intermediate "macroweather" regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be "homogenized" by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

Scaling issues in local productivity hotspots in marine ecosystems using remote sensing data: A case study in the Gulf of Maine

NASA Astrophysics Data System (ADS)

Ribera, M.; Gopal, S.

2014-12-01

Productivity hotspots are traditionally defined as concentrations of relatively high biomass compared to global reference values. These hotspots often signal atypical processes occurring in a location, and identifying them is a great first step at understanding the complexity inherent in the system. However, identifying local hotspots can be difficult when an overarching global pattern (i.e. spatial autocorrelation) already exists. This problem is particularly apparent in marine ecosystems because values of productivity in near-shore areas are consistently higher than those of the open ocean due to oceanographic processes such as upwelling. In such cases, if the global reference layer used to detect hotspots is too wide, hotspots may be only identified near the coast while missing known concentrations of organisms in offshore waters. On the other hand, if the global reference layer is too small, every single location may be considered a hotspot. We applied spatial and traditional statistics to remote sensing data to determine the optimal reference global spatial scale for identifying marine productivity hotspots in the Gulf of Maine. Our iterative process measured Getis and Ord's local G* statistic at different global scales until the variance of each hotspot was maximized. We tested this process with different full resolution MERIS chlorophyll layers (300m spatial resolution) for the whole Gulf of Maine. We concluded that the optimal global scale depends on the time of the year the remote sensing data was collected, particularly when coinciding with known seasonal phytoplankton blooms. The hotspots found through this process were also spatially heterogeneous in size, with bigger hotspots in areas offshore than in locations inshore. These results may be instructive for both managers and fisheries researchers as they adapt their fisheries management policies and methods to an ecosystem based approach (EBM).

Contrast statistics for foveated visual systems: fixation selection by minimizing contrast entropy

NASA Astrophysics Data System (ADS)

Raj, Raghu; Geisler, Wilson S.; Frazor, Robert A.; Bovik, Alan C.

2005-10-01

The human visual system combines a wide field of view with a high-resolution fovea and uses eye, head, and body movements to direct the fovea to potentially relevant locations in the visual scene. This strategy is sensible for a visual system with limited neural resources. However, for this strategy to be effective, the visual system needs sophisticated central mechanisms that efficiently exploit the varying spatial resolution of the retina. To gain insight into some of the design requirements of these central mechanisms, we have analyzed the effects of variable spatial resolution on local contrast in 300 calibrated natural images. Specifically, for each retinal eccentricity (which produces a certain effective level of blur), and for each value of local contrast observed at that eccentricity, we measured the probability distribution of the local contrast in the unblurred image. These conditional probability distributions can be regarded as posterior probability distributions for the ``true'' unblurred contrast, given an observed contrast at a given eccentricity. We find that these conditional probability distributions are adequately described by a few simple formulas. To explore how these statistics might be exploited by central perceptual mechanisms, we consider the task of selecting successive fixation points, where the goal on each fixation is to maximize total contrast information gained about the image (i.e., minimize total contrast uncertainty). We derive an entropy minimization algorithm and find that it performs optimally at reducing total contrast uncertainty and that it also works well at reducing the mean squared error between the original image and the image reconstructed from the multiple fixations. Our results show that measurements of local contrast alone could efficiently drive the scan paths of the eye when the goal is to gain as much information about the spatial structure of a scene as possible.

Geostatistical Modeling of Sediment Abundance in a Heterogeneous Basalt Aquifer at the Idaho National Laboratory, Idaho

USGS Publications Warehouse

Welhan, John A.; Farabaugh, Renee L.; Merrick, Melissa J.; Anderson, Steven R.

2007-01-01

The spatial distribution of sediment in the eastern Snake River Plain aquifer was evaluated and modeled to improve the parameterization of hydraulic conductivity (K) for a subregional-scale ground-water flow model being developed by the U.S. Geological Survey. The aquifer is hosted within a layered series of permeable basalts within which intercalated beds of fine-grained sediment constitute local confining units. These sediments have K values as much as six orders of magnitude lower than the most permeable basalt, and previous flow-model calibrations have shown that hydraulic conductivity is sensitive to the proportion of intercalated sediment. Stratigraphic data in the form of sediment thicknesses from 333 boreholes in and around the Idaho National Laboratory were evaluated as grouped subsets of lithologic units (composite units) corresponding to their relative time-stratigraphic position. The results indicate that median sediment abundances of the stratigraphic units below the water table are statistically invariant (stationary) in a spatial sense and provide evidence of stationarity across geologic time, as well. Based on these results, the borehole data were kriged as two-dimensional spatial data sets representing the sediment content of the layers that discretize the ground-water flow model in the uppermost 300 feet of the aquifer. Multiple indicator kriging (mIK) was used to model the geographic distribution of median sediment abundance within each layer by defining the local cumulative frequency distribution (CFD) of sediment via indicator variograms defined at multiple thresholds. The mIK approach is superior to ordinary kriging because it provides a statistically best estimate of sediment abundance (the local median) drawn from the distribution of local borehole data, independent of any assumption of normality. A methodology is proposed for delineating and constraining the assignment of hydraulic conductivity zones for parameter estimation, based on the locally estimated CFDs and relative kriging uncertainty. A kriging-based methodology improves the spatial resolution of hydraulic property zones that can be considered during parameter estimation and should improve calibration performance and sensitivity by more accurately reflecting the nuances of sediment distribution within the aquifer.

Downscaling climate information for local disease mapping.

PubMed

Bernardi, M; Gommes, R; Grieser, J

2006-06-01

The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.

Species sorting and patch dynamics in harlequin metacommunities affect the relative importance of environment and space.

PubMed

Leibold, Mathew A; Loeuille, Nicolas

2015-12-01

Metacommunity theory indicates that variation in local community structure can be partitioned into components including those related to local environmental conditions vs. spatial effects and that these can be quantified using statistical methods based on variation partitioning. It has been hypothesized that joint associations of community composition with environment and space could be due to patch dynamics involving colonization-extinction processes in environmentally heterogeneous landscapes but this has yet to be theoretically shown. We develop a two-patch, type-two, species competition model in such a "harlequin" landscape (where different patches have different environments) to evaluate how composition is related to environmental and spatial effects as a function of background extinction rate. Using spatially implicit analytical models, we find that the environmental association of community composition declines with extinction rate as expected. Using spatially explicit simulation models, we further find that there is an increase in the spatial structure with extinction due to spatial patterning into clusters that are not related to environmental conditions but that this increase is limited. Natural metacommunities often show both environment and spatial determination even under conditions of relatively high isolation and these could be more easily explained by our model than alternative metacommunity models.

Spatial Distribution of Coffee Wilt Disease Under Roguing and Replanting Conditions: A Case Study from Kaweri Estate in Uganda.

PubMed

Pinard, F; Makune, S E; Campagne, P; Mwangi, J

2016-11-01

Based on time and spatial dynamic considerations, this study evaluates the potential role of short- and long-distance dispersal in the spread of coffee wilt disease (CWD) in a large commercial Robusta coffee estate in Uganda (Kaweri, 1,755 ha) over a 4-year period (2008 to 2012). In monthly surveys, total disease incidence, expansion of infection foci, and the occurrence of isolated infected trees were recorded and submitted to spatial analysis. Incidence was higher and disease progression faster in old coffee plantings compared with young plantings, indicating a lack of efficiency of roguing for reducing disease development in old plantings. At large spatial scale (approximately 1 km), Moran indices (both global and local) revealed the existence of clusters characterized by contrasting disease incidences. This suggested that local environmental conditions were heterogeneous or there were spatial interactions among blocks. At finer spatial scale (approximately 200 m), O-ring statistics revealed positive correlation between distant infection sites across distances as great as 60 m. Although these observations indicate the role of short-distance dispersal in foci expansion, dispersal at greater distances (>20 m) appeared to also contribute to both initiation of new foci and disease progression at coarser spatial scales. Therefore, our results suggested the role of aerial dispersal in CWD progression.

Uncertainties and implications of applying aggregated data for spatial modelling of atmospheric ammonia emissions.

PubMed

Hellsten, S; Dragosits, U; Place, C J; Dore, A J; Tang, Y S; Sutton, M A

2018-05-09

Ammonia emissions vary greatly at a local scale, and effects (eutrophication, acidification) occur primarily close to sources. Therefore it is important that spatially distributed emission estimates are located as accurately as possible. The main source of ammonia emissions is agriculture, and therefore agricultural survey statistics are the most important input data to an ammonia emission inventory alongside per activity estimates of emission potential. In the UK, agricultural statistics are collected at farm level, but are aggregated to parish level, NUTS-3 level or regular grid resolution for distribution to users. In this study, the Modifiable Areal Unit Problem (MAUP), associated with such amalgamation, is investigated in the context of assessing the spatial distribution of ammonia sources for emission inventories. England was used as a test area to study the effects of the MAUP. Agricultural survey data at farm level (point data) were obtained under license and amalgamated to different areal units or zones: regular 1-km, 5-km, 10-km grids and parish level, before they were imported into the emission model. The results of using the survey data at different levels of amalgamation were assessed to estimate the effects of the MAUP on the spatial inventory. The analysis showed that the size and shape of aggregation zones applied to the farm-level agricultural statistics strongly affect the location of the emissions estimated by the model. If the zones are too small, this may result in false emission "hot spots", i.e., artificially high emission values that are in reality not confined to the zone to which they are allocated. Conversely, if the zones are too large, detail may be lost and emissions smoothed out, which may give a false impression of the spatial patterns and magnitude of emissions in those zones. The results of the study indicate that the MAUP has a significant effect on the location and local magnitude of emissions in spatial inventories where amalgamated, zonal data are used. Copyright © 2018 Elsevier Ltd. All rights reserved.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

Optoelectronic scanning system upgrade by energy center localization methods

NASA Astrophysics Data System (ADS)

Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.

2016-11-01

A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.

The Geography of Diabetes in London, Canada: The Need for Local Level Policy for Prevention and Management

PubMed Central

Tompkins, Jordan W.; Luginaah, Isaac N.; Booth, Gillian L.; Harris, Stewart B.

2010-01-01

Recent reports aimed at improving diabetes care in socially disadvantaged populations suggest that interventions must be tailored to meet the unique needs of the local community—specifically, the community’s geography. We have examined the spatial distribution of diabetes in the context of socioeconomic determinants of health in London (Ontario, Canada) to characterize neighbourhoods in an effort to target these neighbourhoods for local level community-based program planning and intervention. Multivariate spatial-statistical techniques and geographic information systems were used to examine diabetes rates and socioeconomic variables aggregated at the census tract level. Creation of a deprivation index facilitated investigation across multiple determinants of health. Findings from our research identified ‘at risk’ neighbourhoods in London with socioeconomic disadvantage and high diabetes. Future endeavours must continue to identify local level trends in order to support policy development, resource planning and care for improved health outcomes and improved equity in access to care across geographic regions. PMID:20623032

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

PubMed Central

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

2006-01-01

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

Local introduction and heterogeneous spatial spread of dengue-suppressing Wolbachia through an urban population of Aedes aegypti

PubMed Central

Schmidt, Tom L.; Barton, Nicholas H.; Rašić, Gordana; Turley, Andrew P.; Montgomery, Brian L.; Iturbe-Ormaetxe, Inaki; Cook, Peter E.; Ryan, Peter A.; Ritchie, Scott A.; Hoffmann, Ary A.; O’Neill, Scott L.

2017-01-01

Dengue-suppressing Wolbachia strains are promising tools for arbovirus control, particularly as they have the potential to self-spread following local introductions. To test this, we followed the frequency of the transinfected Wolbachia strain wMel through Ae. aegypti in Cairns, Australia, following releases at 3 nonisolated locations within the city in early 2013. Spatial spread was analysed graphically using interpolation and by fitting a statistical model describing the position and width of the wave. For the larger 2 of the 3 releases (covering 0.97 km2 and 0.52 km2), we observed slow but steady spatial spread, at about 100–200 m per year, roughly consistent with theoretical predictions. In contrast, the smallest release (0.11 km2) produced erratic temporal and spatial dynamics, with little evidence of spread after 2 years. This is consistent with the prediction concerning fitness-decreasing Wolbachia transinfections that a minimum release area is needed to achieve stable local establishment and spread in continuous habitats. Our graphical and likelihood analyses produced broadly consistent estimates of wave speed and wave width. Spread at all sites was spatially heterogeneous, suggesting that environmental heterogeneity will affect large-scale Wolbachia transformations of urban mosquito populations. The persistence and spread of Wolbachia in release areas meeting minimum area requirements indicates the promise of successful large-scale population transformation. PMID:28557993

Local introduction and heterogeneous spatial spread of dengue-suppressing Wolbachia through an urban population of Aedes aegypti.

PubMed

Schmidt, Tom L; Barton, Nicholas H; Rašić, Gordana; Turley, Andrew P; Montgomery, Brian L; Iturbe-Ormaetxe, Inaki; Cook, Peter E; Ryan, Peter A; Ritchie, Scott A; Hoffmann, Ary A; O'Neill, Scott L; Turelli, Michael

2017-05-01

Dengue-suppressing Wolbachia strains are promising tools for arbovirus control, particularly as they have the potential to self-spread following local introductions. To test this, we followed the frequency of the transinfected Wolbachia strain wMel through Ae. aegypti in Cairns, Australia, following releases at 3 nonisolated locations within the city in early 2013. Spatial spread was analysed graphically using interpolation and by fitting a statistical model describing the position and width of the wave. For the larger 2 of the 3 releases (covering 0.97 km2 and 0.52 km2), we observed slow but steady spatial spread, at about 100-200 m per year, roughly consistent with theoretical predictions. In contrast, the smallest release (0.11 km2) produced erratic temporal and spatial dynamics, with little evidence of spread after 2 years. This is consistent with the prediction concerning fitness-decreasing Wolbachia transinfections that a minimum release area is needed to achieve stable local establishment and spread in continuous habitats. Our graphical and likelihood analyses produced broadly consistent estimates of wave speed and wave width. Spread at all sites was spatially heterogeneous, suggesting that environmental heterogeneity will affect large-scale Wolbachia transformations of urban mosquito populations. The persistence and spread of Wolbachia in release areas meeting minimum area requirements indicates the promise of successful large-scale population transformation.

Assessment of spatial variation of risks in small populations.

PubMed Central

Riggan, W B; Manton, K G; Creason, J P; Woodbury, M A; Stallard, E

1991-01-01

Often environmental hazards are assessed by examining the spatial variation of disease-specific mortality or morbidity rates. These rates, when estimated for small local populations, can have a high degree of random variation or uncertainty associated with them. If those rate estimates are used to prioritize environmental clean-up actions or to allocate resources, then those decisions may be influenced by this high degree of uncertainty. Unfortunately, the effect of this uncertainty is not to add "random noise" into the decision-making process, but to systematically bias action toward the smallest populations where uncertainty is greatest and where extreme high and low rate deviations are most likely to be manifest by chance. We present a statistical procedure for adjusting rate estimates for differences in variability due to differentials in local area population sizes. Such adjustments produce rate estimates for areas that have better properties than the unadjusted rates for use in making statistically based decisions about the entire set of areas. Examples are provided for county variation in bladder, stomach, and lung cancer mortality rates for U.S. white males for the period 1970 to 1979. PMID:1820268

Very high resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality

NASA Astrophysics Data System (ADS)

Mairota, Paola; Cafarelli, Barbara; Labadessa, Rocco; Lovergine, Francesco P.; Tarantino, Cristina; Nagendra, Harini; Didham, Raphael K.

2015-02-01

Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that 'habitat amount' in the landscape is sufficient to explain patterns of biodiversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch- and landscape levels. As the statistical approaches needed to implement such hierarchical causal models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality.

The importance of regional models in assessing canine cancer incidences in Switzerland

PubMed Central

Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships. PMID:29652921

The importance of regional models in assessing canine cancer incidences in Switzerland.

PubMed

Boo, Gianluca; Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Projecting climate change impacts on hydrology: the potential role of daily GCM output

NASA Astrophysics Data System (ADS)

Maurer, E. P.; Hidalgo, H. G.; Das, T.; Dettinger, M. D.; Cayan, D.

2008-12-01

A primary challenge facing resource managers in accommodating climate change is determining the range and uncertainty in regional and local climate projections. This is especially important for assessing changes in extreme events, which will drive many of the more severe impacts of a changed climate. Since global climate models (GCMs) produce output at a spatial scale incompatible with local impact assessment, different techniques have evolved to downscale GCM output so locally important climate features are expressed in the projections. We compared skill and hydrologic projections using two statistical downscaling methods and a distributed hydrology model. The downscaling methods are the constructed analogues (CA) and the bias correction and spatial downscaling (BCSD). CA uses daily GCM output, and can thus capture GCM projections for changing extreme event occurrence, while BCSD uses monthly output and statistically generates historical daily sequences. We evaluate the hydrologic impacts projected using downscaled climate (from the NCEP/NCAR reanalysis as a surrogate GCM) for the late 20th century with both methods, comparing skill in projecting soil moisture, snow pack, and streamflow at key locations in the Western United States. We include an assessment of a new method for correcting for GCM biases in a hybrid method combining the most important characteristics of both methods.

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

A neurophysiological explanation for biases in visual localization.

PubMed

Moreland, James C; Boynton, Geoffrey M

2017-02-01

Observers show small but systematic deviations from equal weighting of all elements when asked to localize the center of an array of dots. Counter-intuitively, with small numbers of dots drawn from a Gaussian distribution, this bias results in subjects overweighting the influence of outlier dots - inconsistent with traditional statistical estimators of central tendency. Here we show that this apparent statistical anomaly can be explained by the observation that outlier dots also lie in regions of lower dot density. Using a standard model of V1 processing, which includes spatial integration followed by a compressive static nonlinearity, we can successfully predict the finding that dots in less dense regions of an array have a relatively greater influence on the perceived center.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

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

Lovejoy, S., E-mail: lovejoy@physics.mcgill.ca; Lima, M. I. P. de; Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra

2015-07-15

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spitemore » of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.« less

Spatial/Temporal Variations of Crime: A Routine Activity Theory Perspective.

PubMed

de Melo, Silas Nogueira; Pereira, Débora V S; Andresen, Martin A; Matias, Lindon Fonseca

2018-05-01

Temporal and spatial patterns of crime in Campinas, Brazil, are analyzed considering the relevance of routine activity theory in a Latin American context. We use geo-referenced criminal event data, 2010-2013, analyzing spatial patterns using census tracts and temporal patterns considering seasons, months, days, and hours. Our analyses include difference in means tests, count-based regression models, and Kulldorff's scan test. We find that crime in Campinas, Brazil, exhibits both temporal and spatial-temporal patterns. However, the presence of these patterns at the different temporal scales varies by crime type. Specifically, not all crime types have statistically significant temporal patterns at all scales of analysis. As such, routine activity theory works well to explain temporal and spatial-temporal patterns of crime in Campinas, Brazil. However, local knowledge of Brazilian culture is necessary for understanding a portion of these crime patterns.

Spatial patterns of hydro-social metrics in the Northeastern United States from the Colonial Era through the Industrial Revolution (1600-1920)

NASA Astrophysics Data System (ADS)

Witherell, B. B.; Bain, D. J.; Salant, N.; Aloysius, N. R.

2009-12-01

Humans impact the hydrologic cycle at local, regional and global scales. Understanding how spatial patterns of human water use and hydrologic impact have changed over time is important to future water management in an era of increasing water constraints and globalization of high water-use resources. This study investigates spatial dependence and spatial patterns of hydro-social metrics for the Northeastern United States from 1600 to 1920 through the use of spatial statistical techniques. Several relevant hydro-social metrics, including water residence time, surface water storage (natural and human engineered) and per capita water availability, are analyzed. This study covers a region and period of time that saw significant population growth, landscape change, and industrial growth. These changes had important impacts on water availability. Although some changes such as the elimination of beavers, and the resulting loss of beaver ponds on low-order streams, are felt at a regional scale, preliminary analysis indicates that humans responded to water constraints by acting locally (e.g., mill ponds for water power and water supply reservoirs for public health). This 320-year historical analysis of spatial patterns of hydro-social metrics provides unique insight into long-term changes in coupled human-water systems.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands.

PubMed

Rood, Ente J J; Goris, Marga G A; Pijnacker, Roan; Bakker, Mirjam I; Hartskeerl, Rudy A

2017-01-01

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands

PubMed Central

Goris, Marga G. A.; Pijnacker, Roan; Bakker, Mirjam I.; Hartskeerl, Rudy A.

2017-01-01

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995–2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas. PMID:29065186

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2012-01-01

This product "Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area" is a 1:250,000-scale vector spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

A spatial database of wildfires in the United States, 1992-2011

Treesearch

K. C. Short

2014-01-01

The statistical analysis of wildfire activity is a critical component of national wildfire planning, operations, and research in the United States (US). However, there are multiple federal, state, and local entities with wildfire protection and reporting responsibilities in the US, and no single, unified system of wildfire record keeping exists. To conduct even the...

A spatial database of wildfires in the United States, 1992-2011 [Discussions

Treesearch

K. C. Short

2013-01-01

The statistical analysis of wildfire activity is a critical component of national wildfire planning, operations, and research in the United States (US). However, there are multiple federal, state, and local entities with wildfire protection and reporting responsibilities in the US, and no single, unified system of wildfire record-keeping exists. To conduct even the...

A statistical model to estimate the local vulnerability to severe weather

NASA Astrophysics Data System (ADS)

Pardowitz, Tobias

2018-06-01

We present a spatial analysis of weather-related fire brigade operations in Berlin. By comparing operation occurrences to insured losses for a set of severe weather events we demonstrate the representativeness and usefulness of such data in the analysis of weather impacts on local scales. We investigate factors influencing the local rate of operation occurrence. While depending on multiple factors - which are often not available - we focus on publicly available quantities. These include topographic features, land use information based on satellite data and information on urban structure based on data from the OpenStreetMap project. After identifying suitable predictors such as housing coverage or local density of the road network we set up a statistical model to be able to predict the average occurrence frequency of local fire brigade operations. Such model can be used to determine potential hotspots for weather impacts even in areas or cities where no systematic records are available and can thus serve as a basis for a broad range of tools or applications in emergency management and planning.

Revealing spatially heterogeneous relaxation in a model nanocomposite.

PubMed

Cheng, Shiwang; Mirigian, Stephen; Carrillo, Jan-Michael Y; Bocharova, Vera; Sumpter, Bobby G; Schweizer, Kenneth S; Sokolov, Alexei P

2015-11-21

The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no "glassy" layer, but the α-relaxation time near the nanoparticle grows with cooling faster than the α-relaxation time in the bulk and is ∼20 times longer at low temperatures. The interfacial layer thickness increases from ∼1.8 nm at higher temperatures to ∼3.5 nm upon cooling to near bulk Tg. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory. Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. The theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range.

Bagging Voronoi classifiers for clustering spatial functional data

NASA Astrophysics Data System (ADS)

Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria

2013-06-01

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

Revealing spatially heterogeneous relaxation in a model nanocomposite

DOE PAGES

Cheng, Shiwang; Mirigian, Stephen; Carrillo, Jan-Michael Y.; ...

2015-11-18

The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no glassy layer, but the -relaxation time near the nanoparticle grows with cooling faster than the -relaxation time in the bulk and is ~20 times longer at low temperatures. The interfacial layer thickness increases from ~1.8 nm at higher temperatures to ~3.5 nm upon cooling to near bulk T g. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory.more » Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. As a result, the theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range.« less

Web-based GIS for spatial pattern detection: application to malaria incidence in Vietnam.

PubMed

Bui, Thanh Quang; Pham, Hai Minh

2016-01-01

There is a great concern on how to build up an interoperable health information system of public health and health information technology within the development of public information and health surveillance programme. Technically, some major issues remain regarding to health data visualization, spatial processing of health data, health information dissemination, data sharing and the access of local communities to health information. In combination with GIS, we propose a technical framework for web-based health data visualization and spatial analysis. Data was collected from open map-servers and geocoded by open data kit package and data geocoding tools. The Web-based system is designed based on Open-source frameworks and libraries. The system provides Web-based analyst tool for pattern detection through three spatial tests: Nearest neighbour, K function, and Spatial Autocorrelation. The result is a web-based GIS, through which end users can detect disease patterns via selecting area, spatial test parameters and contribute to managers and decision makers. The end users can be health practitioners, educators, local communities, health sector authorities and decision makers. This web-based system allows for the improvement of health related services to public sector users as well as citizens in a secure manner. The combination of spatial statistics and web-based GIS can be a solution that helps empower health practitioners in direct and specific intersectional actions, thus provide for better analysis, control and decision-making.

Shiga toxigenic Escherichia coli incidence is related to small area variation in cattle density in a region in Ireland.

PubMed

Brehony, C; Cullinan, J; Cormican, M; Morris, D

2018-10-01

Shiga toxigenic Escherichia coli (STEC) are pathogenic E. coli that cause infectious diarrhoea. In some cases infection may be complicated by renal failure and death. The incidence of human infection with STEC in Ireland is the highest in Europe. The objective of the study was to examine the spatial incidence of human STEC infection in a region of Ireland with significantly higher rates of STEC incidence than the national average and to identify possible risk factors of STEC incidence at area level. Anonymised laboratory records (n = 379) from 2009 to 2015 were obtained from laboratories serving three counties in the West of Ireland. Data included location and sample date. Population and electoral division (ED) data were obtained from the Irish 2011 Census of Population. STEC incidence was calculated for each ED (n = 498) and used to map hotspots/coldspots using the Getis-Ord Gi* spatial statistic and significant spatial clustering using the Anselin's Local Moran's I statistic. Multivariable regression analysis was used to consider the importance of a number of potential predictors of STEC incidence. Incidence rates for the seven-year period ranged from 0 to 10.9 cases per 1000. A number of areas with significant local clustering of STEC incidence as well as variation in the spatial distribution of the two main serogroups associated with disease in the region i.e. O26 and O157 were identified. Cattle density was found to be a statistically significant predictor of STEC in the region. GIS analysis of routine data indicates that cattle density is associated STEC infection in this high incidence region. This finding points to the importance of agricultural practices for human health and the importance of a "one-health" approach to public policy in relation to agriculture, health and environment. Copyright © 2018 Elsevier B.V. All rights reserved.

Modelling the Effects of Land-Use Changes on Climate: a Case Study on Yamula DAM

NASA Astrophysics Data System (ADS)

Köylü, Ü.; Geymen, A.

2016-10-01

Dams block flow of rivers and cause artificial water reservoirs which affect the climate and the land use characteristics of the river basin. In this research, the effect of the huge water body obtained by Yamula Dam in Kızılırmak Basin is analysed over surrounding spatial's land use and climate change. Mann Kendal non-parametrical statistical test, Theil&Sen Slope method, Inverse Distance Weighting (IDW), Soil Conservation Service-Curve Number (SCS-CN) methods are integrated for spatial and temporal analysis of the research area. For this research humidity, temperature, wind speed, precipitation observations which are collected in 16 weather stations nearby Kızılırmak Basin are analyzed. After that these statistical information is combined by GIS data over years. An application is developed for GIS analysis in Python Programming Language and integrated with ArcGIS software. Statistical analysis calculated in the R Project for Statistical Computing and integrated with developed application. According to the statistical analysis of extracted time series of meteorological parameters, statistical significant spatiotemporal trends are observed for climate change and land use characteristics. In this study, we indicated the effect of big dams in local climate on semi-arid Yamula Dam.

Deforestation, agriculture and farm jobs: a good recipe for Plasmodium vivax in French Guiana.

PubMed

Basurko, Célia; Demattei, Christophe; Han-Sze, René; Grenier, Claire; Joubert, Michel; Nacher, Mathieu; Carme, Bernard

2013-03-11

In a malaria-endemic area the distribution of patients is neither constant in time nor homogeneous in space. The WHO recommends the stratification of malaria risk on a fine geographical scale. In the village of Cacao in French Guiana, the study of the spatial and temporal distribution of malaria cases, during an epidemic, allowed a better understanding of the environmental factors promoting malaria transmission. A dynamic cohort of 839 persons living in 176 households (only people residing permanently in the village) was constituted between January 1st, 2002 and December 31st, 2007.The information about the number of inhabitants per household, the number of confirmed cases of Plasmodium vivax and house GPS coordinates were collected to search for spatial or temporal clustering using Kurlldorff's statistical method. Of the 839 persons living permanently in the village of Cacao, 359 persons presented at least one vivax malaria episode between 2002 and 2007. Five temporal clusters and four spatial clusters were identified during the study period. In all temporal clusters, April was included. Two spatial clusters were localized at the north of the village near the Comté River and two others localized close to orchards. The spatial heterogeneity of malaria in the village may have been influenced by environmental disturbances due to local agricultural policies: deforestation, cultures of fresh produce, or drainage of water for agriculture. This study allowed generating behavioural, entomological, or environmental hypotheses that could be useful to improve prevention campaigns.

Spatial 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.

Statistical Exploration of Local Transmission Routes for African Swine Fever in Pigs in the Russian Federation, 2007-2014.

PubMed

Vergne, T; Gogin, A; Pfeiffer, D U

2017-04-01

African swine fever (ASF) is a devastating viral disease of swine that is present in both pigs and wild boar in the western part of the Russian Federation and the eastern part of the European Union. It represents a significant threat for the European pig production industry as neither treatment nor vaccine is available. This study analysed the spatial and spatio-temporal distributions of ASF cases that were reported in domestic pigs and wild boar for assessing the likelihood of wild boar-to-domestic pig and farm-to-farm transmission routes in the epidemic that occurred from 2007 to 2014 in the Krasnodar and the Tver regions, two of the most affected areas of the Russian Federation. Results suggest that in both regions, the spatial proximity to an infectious farm was a strong risk factor for infection of a susceptible farm. In the Krasnodar region, the results of the statistical analysis suggest that the epidemics in wild boar and in domestic pigs were independent from each other. In contrast, there seemed to be a dependence between the two epidemics in the Tver region. But because outbreaks in domestic pigs were not statistically significantly clustered around wild boar cases, the joint spatial distribution of wild boar cases and of outbreaks in domestic pigs in the Tver region may be explained by regular spillovers from the domestic pig to the wild boar population. These findings confirm the need to maintain high biosecurity standards on pig farms and justify strict control measures targeted at domestic pig production such as culling of infected herds and local movement restrictions. © 2015 Blackwell Verlag GmbH.

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

PubMed

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

Nonequilibrium quantum dynamics and transport: from integrability to many-body localization

NASA Astrophysics Data System (ADS)

Vasseur, Romain; Moore, Joel E.

2016-06-01

We review the non-equilibrium dynamics of many-body quantum systems after a quantum quench with spatial inhomogeneities, either in the Hamiltonian or in the initial state. We focus on integrable and many-body localized systems that fail to self-thermalize in isolation and for which the standard hydrodynamical picture breaks down. The emphasis is on universal dynamics, non-equilibrium steady states and new dynamical phases of matter, and on phase transitions far from thermal equilibrium. We describe how the infinite number of conservation laws of integrable and many-body localized systems lead to complex non-equilibrium states beyond the traditional dogma of statistical mechanics.

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

PubMed Central

Kwan, Paul; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops. PMID:28875085

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling

PubMed Central

2013-01-01

Background As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population are needed to adequately plan targeted preventive intervention. We propose a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, a tropical urban environment of Colombia. Methods Based on a set of socioeconomic and demographic indicators derived from census data and ancillary geospatial datasets, we develop a spatial approach for both expert-based and purely statistical-based modeling of current vulnerability levels across 340 neighborhoods of the city using a Geographic Information System (GIS). The results of both approaches are comparatively evaluated by means of spatial statistics. A web-based approach is proposed to facilitate the visualization and the dissemination of the output vulnerability index to the community. Results The statistical and the expert-based modeling approach exhibit a high concordance, globally, and spatially. The expert-based approach indicates a slightly higher vulnerability mean (0.53) and vulnerability median (0.56) across all neighborhoods, compared to the purely statistical approach (mean = 0.48; median = 0.49). Both approaches reveal that high values of vulnerability tend to cluster in the eastern, north-eastern, and western part of the city. These are poor neighborhoods with high percentages of young (i.e., < 15 years) and illiterate residents, as well as a high proportion of individuals being either unemployed or doing housework. Conclusions Both modeling approaches reveal similar outputs, indicating that in the absence of local expertise, statistical approaches could be used, with caution. By decomposing identified vulnerability “hotspots” into their underlying factors, our approach provides valuable information on both (1) the location of neighborhoods, and (2) vulnerability factors that should be given priority in the context of targeted intervention strategies. The results support decision makers to allocate resources in a manner that may reduce existing susceptibilities and strengthen resilience, and thus help to reduce the burden of vector-borne diseases. PMID:23945265

A multi-scale comparison of trait linkages to environmental and spatial variables in fish communities across a large freshwater lake.

PubMed

Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J

2011-07-01

Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.

Medical Image Retrieval Using Multi-Texton Assignment.

PubMed

Tang, Qiling; Yang, Jirong; Xia, Xianfu

2018-02-01

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

40 CFR Appendix N to Part 50 - Interpretation of the National Ambient Air Quality Standards for PM2.5

Code of Federal Regulations, 2012 CFR

2012-07-01

... midnight to midnight (local standard time) that are used in NAAQS computations. Designated monitors are... accordance with part 58 of this chapter. Design values are the metrics (i.e., statistics) that are compared... (referred to as the “annual standard design value”). If spatial averaging has been approved by EPA for a...

Spatial relationships between alcohol-related road crashes and retail alcohol availability.

PubMed

Morrison, Christopher; Ponicki, William R; Gruenewald, Paul J; Wiebe, Douglas J; Smith, Karen

2016-05-01

This study examines spatial relationships between alcohol outlet density and the incidence of alcohol-related crashes. The few prior studies conducted in this area used relatively large spatial units; here we use highly resolved units from Melbourne, Australia (Statistical Area level 1 [SA1] units: mean land area=0.5 km(2); SD=2.2 km(2)), in order to assess different micro-scale spatial relationships for on- and off-premise outlets. Bayesian conditional autoregressive Poisson models were used to assess cross-sectional relationships of three-year counts of alcohol-related crashes (2010-2012) attended by Ambulance Victoria paramedics to densities of bars, restaurants, and off-premise outlets controlling for other land use, demographic and roadway characteristics. Alcohol-related crashes were not related to bar density within local SA1 units, but were positively related to bar density in adjacent SA1 units. Alcohol-related crashes were negatively related to off-premise outlet density in local SA1 units. Examined in one metropolitan area using small spatial units, bar density is related to greater crash risk in surrounding areas. Observed negative relationships for off-premise outlets may be because the origins and destinations of alcohol-affected journeys are in distal locations relative to outlets. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Spatial differences between stars and brown dwarfs: a dynamical origin?

NASA Astrophysics Data System (ADS)

Parker, Richard J.; Andersen, Morten

2014-06-01

We use N-body simulations to compare the evolution of spatial distributions of stars and brown dwarfs in young star-forming regions. We use three different diagnostics: the ratio of stars to brown dwarfs as a function of distance from the region's centre, {R}_SSR, the local surface density of stars compared to brown dwarfs, ΣLDR, and we compare the global spatial distributions using the ΛMSR method. From a suite of 20 initially statistically identical simulations, 6/20 attain {R}_SSR ≪ 1 and ΣLDR ≪ 1 and ΛMSR ≪ 1, indicating that dynamical interactions could be responsible for observed differences in the spatial distributions of stars and brown dwarfs in star-forming regions. However, many simulations also display apparently contradictory results - for example, in some cases the brown dwarfs have much lower local densities than stars (ΣLDR ≪ 1), but their global spatial distributions are indistinguishable (ΛMSR = 1) and the relative proportion of stars and brown dwarfs remains constant across the region ({R}_SSR = 1). Our results suggest that extreme caution should be exercised when interpreting any observed difference in the spatial distribution of stars and brown dwarfs, and that a much larger observational sample of regions/clusters (with complete mass functions) is necessary to investigate whether or not brown dwarfs form through similar mechanisms to stars.

Testing the dual-pathway model for auditory processing in human cortex.

PubMed

Zündorf, Ida C; Lewald, Jörg; Karnath, Hans-Otto

2016-01-01

Analogous to the visual system, auditory information has been proposed to be processed in two largely segregated streams: an anteroventral ("what") pathway mainly subserving sound identification and a posterodorsal ("where") stream mainly subserving sound localization. Despite the popularity of this assumption, the degree of separation of spatial and non-spatial auditory information processing in cortex is still under discussion. In the present study, a statistical approach was implemented to investigate potential behavioral dissociations for spatial and non-spatial auditory processing in stroke patients, and voxel-wise lesion analyses were used to uncover their neural correlates. The results generally provided support for anatomically and functionally segregated auditory networks. However, some degree of anatomo-functional overlap between "what" and "where" aspects of processing was found in the superior pars opercularis of right inferior frontal gyrus (Brodmann area 44), suggesting the potential existence of a shared target area of both auditory streams in this region. Moreover, beyond the typically defined posterodorsal stream (i.e., posterior superior temporal gyrus, inferior parietal lobule, and superior frontal sulcus), occipital lesions were found to be associated with sound localization deficits. These results, indicating anatomically and functionally complex cortical networks for spatial and non-spatial auditory processing, are roughly consistent with the dual-pathway model of auditory processing in its original form, but argue for the need to refine and extend this widely accepted hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

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

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

Shearlet-based measures of entropy and complexity for two-dimensional patterns

NASA Astrophysics Data System (ADS)

Brazhe, Alexey

2018-06-01

New spatial entropy and complexity measures for two-dimensional patterns are proposed. The approach is based on the notion of disequilibrium and is built on statistics of directional multiscale coefficients of the fast finite shearlet transform. Shannon entropy and Jensen-Shannon divergence measures are employed. Both local and global spatial complexity and entropy estimates can be obtained, thus allowing for spatial mapping of complexity in inhomogeneous patterns. The algorithm is validated in numerical experiments with a gradually decaying periodic pattern and Ising surfaces near critical state. It is concluded that the proposed algorithm can be instrumental in describing a wide range of two-dimensional imaging data, textures, or surfaces, where an understanding of the level of order or randomness is desired.

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

DOE PAGES

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.; ...

2018-02-09

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

A framework for the assessment of the spatial and temporal patterns of threatened coastal delphinids

NASA Astrophysics Data System (ADS)

Wang, Jingzhen; Yang, Yingting; Yang, Feng; Li, Yuelin; Li, Lianjie; Lin, Derun; He, Tangtian; Liang, Bo; Zhang, Tao; Lin, Yao; Li, Ping; Liu, Wenhua

2016-01-01

The massively accelerated biodiversity loss rate in the Anthropocene calls for an efficient and effective way to identify the spatial and temporal dynamics of endangered species. To this end, we developed a useful identification framework based on a case study of locally endangered Sousa chinensis by combining both LEK (local ecological knowledge) evaluation and regional boat-based survey methods. Our study investigated the basic ecological information of Sousa chinensis in the estuaries of eastern Guangdong that had previously been neglected, which could guide the future study and conservation. Based on the statistical testing of reported spatial and temporal dolphins sighting data from fishermen and the ecological monitoring analyses, including sighting rate, site fidelity and residence time estimations, some of the current Sousa chinensis units are likely to be geographically isolated and critically endangered, which calls for much greater conservation efforts. Given the accelerated population extinction rate and increasing budgetary constraints, our survey pattern can be applied in a timely and economically acceptable manner to the spatial and temporal assessment of other threatened coastal delphinids, particularly when population distributions are on a large scale and traditional sampling methods are difficult to implement.

Use of local statistics to reveal hidden information of pollution hotspots in urban soil geochemistry

NASA Astrophysics Data System (ADS)

Zhang, Chaosheng

2017-04-01

The identification of pollution hotspots is an important approach for a better understanding of spatial distribution patterns and the exploration for their influencing factors in environmental studies. One of the most often asked questions in an environmental investigation is: Where are the pollution hotspots? This presentation explains one of the popularly used methodologies called local index of spatial association (LISA) and its applications in urban geochemical studies in Galway, Ireland and London of the UK. The LISA is a useful tool for identifying pollution hotspots and classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values, and it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. This method has been applied to identify Pb pollution in Galway, polluted areas in bonfires sites, elevated P and REE concentrations in London. Hotspots in identified in urban soils are related to locations of high road density, traditional festival bonfires, industries and other human activities. The results of hotspots analysis provide useful information for the management of urban soils.

A framework for the assessment of the spatial and temporal patterns of threatened coastal delphinids.

PubMed

Wang, Jingzhen; Yang, Yingting; Yang, Feng; Li, Yuelin; Li, Lianjie; Lin, Derun; He, Tangtian; Liang, Bo; Zhang, Tao; Lin, Yao; Li, Ping; Liu, Wenhua

2016-01-25

The massively accelerated biodiversity loss rate in the Anthropocene calls for an efficient and effective way to identify the spatial and temporal dynamics of endangered species. To this end, we developed a useful identification framework based on a case study of locally endangered Sousa chinensis by combining both LEK (local ecological knowledge) evaluation and regional boat-based survey methods. Our study investigated the basic ecological information of Sousa chinensis in the estuaries of eastern Guangdong that had previously been neglected, which could guide the future study and conservation. Based on the statistical testing of reported spatial and temporal dolphins sighting data from fishermen and the ecological monitoring analyses, including sighting rate, site fidelity and residence time estimations, some of the current Sousa chinensis units are likely to be geographically isolated and critically endangered, which calls for much greater conservation efforts. Given the accelerated population extinction rate and increasing budgetary constraints, our survey pattern can be applied in a timely and economically acceptable manner to the spatial and temporal assessment of other threatened coastal delphinids, particularly when population distributions are on a large scale and traditional sampling methods are difficult to implement.

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

NASA Astrophysics Data System (ADS)

Gabey, A. M.; Grimmond, C. S. B.; Capel-Timms, I.

2018-02-01

Anthropogenic heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and "hot spots" representing 30-40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total QF, but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns.

An ecological genetic delineation of local seed-source provenance for ecological restoration

PubMed Central

Krauss, Siegfried L; Sinclair, Elizabeth A; Bussell, John D; Hobbs, Richard J

2013-01-01

An increasingly important practical application of the analysis of spatial genetic structure within plant species is to help define the extent of local provenance seed collection zones that minimize negative impacts in ecological restoration programs. Here, we derive seed sourcing guidelines from a novel range-wide assessment of spatial genetic structure of 24 populations of Banksia menziesii (Proteaceae), a widely distributed Western Australian tree of significance in local ecological restoration programs. An analysis of molecular variance (AMOVA) of 100 amplified fragment length polymorphism (AFLP) markers revealed significant genetic differentiation among populations (ΦPT = 0.18). Pairwise population genetic dissimilarity was correlated with geographic distance, but not environmental distance derived from 15 climate variables, suggesting overall neutrality of these markers with regard to these climate variables. Nevertheless, Bayesian outlier analysis identified four markers potentially under selection, although these were not correlated with the climate variables. We calculated a global R-statistic using analysis of similarities (ANOSIM) to test the statistical significance of population differentiation and to infer a threshold seed collection zone distance of ∼60 km (all markers) and 100 km (outlier markers) when genetic distance was regressed against geographic distance. Population pairs separated by >60 km were, on average, twice as likely to be significantly genetically differentiated than population pairs separated by <60 km, suggesting that habitat-matched sites within a 30-km radius around a restoration site genetically defines a local provenance seed collection zone for B. menziesii. Our approach is a novel probability-based practical solution for the delineation of a local seed collection zone to minimize negative genetic impacts in ecological restoration. PMID:23919158

The Impact of Sampling Schemes on the Site Frequency Spectrum in Nonequilibrium Subdivided Populations

PubMed Central

Städler, Thomas; Haubold, Bernhard; Merino, Carlos; Stephan, Wolfgang; Pfaffelhuber, Peter

2009-01-01

Using coalescent simulations, we study the impact of three different sampling schemes on patterns of neutral diversity in structured populations. Specifically, we are interested in two summary statistics based on the site frequency spectrum as a function of migration rate, demographic history of the entire substructured population (including timing and magnitude of specieswide expansions), and the sampling scheme. Using simulations implementing both finite-island and two-dimensional stepping-stone spatial structure, we demonstrate strong effects of the sampling scheme on Tajima's D (DT) and Fu and Li's D (DFL) statistics, particularly under specieswide (range) expansions. Pooled samples yield average DT and DFL values that are generally intermediate between those of local and scattered samples. Local samples (and to a lesser extent, pooled samples) are influenced by local, rapid coalescence events in the underlying coalescent process. These processes result in lower proportions of external branch lengths and hence lower proportions of singletons, explaining our finding that the sampling scheme affects DFL more than it does DT. Under specieswide expansion scenarios, these effects of spatial sampling may persist up to very high levels of gene flow (Nm > 25), implying that local samples cannot be regarded as being drawn from a panmictic population. Importantly, many data sets on humans, Drosophila, and plants contain signatures of specieswide expansions and effects of sampling scheme that are predicted by our simulation results. This suggests that validating the assumption of panmixia is crucial if robust demographic inferences are to be made from local or pooled samples. However, future studies should consider adopting a framework that explicitly accounts for the genealogical effects of population subdivision and empirical sampling schemes. PMID:19237689

Atmospheric deposition maps for the Rocky Mountains

USGS Publications Warehouse

Nanus, L.; Campbell, D.H.; Ingersoll, G.P.; Clow, D.W.; Mast, M.A.

2003-01-01

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5-3.0kg/ha N) and sulfate (10.0-12.0kg/ha SO4) deposition is found in northern Colorado.

From Medical to Recreational Marijuana Sales: Marijuana Outlets and Crime in an Era of Changing Marijuana Legislation.

PubMed

Freisthler, Bridget; Gaidus, Andrew; Tam, Christina; Ponicki, William R; Gruenewald, Paul J

2017-06-01

A movement from medical to recreational marijuana use allows for a larger base of potential users who have easier access to marijuana, because they do not have to visit a physician before using marijuana. This study examines whether changes in the density of marijuana outlets were related to violent, property, and marijuana-specific crimes in Denver, CO during a time in which marijuana outlets began selling marijuana for recreational, and not just medical, use. We collected data on locations of crimes, marijuana outlets and covariates for 481 Census block groups over 34 months (N = 16,354 space-time units). A Bayesian Poisson space-time model assessed statistical relationships between independent measures and crime counts within "local" Census block groups. We examined spatial "lag" effects to assess whether crimes in Census block groups adjacent to locations of outlets were also affected. Independent of the effects of covariates, densities of marijuana outlets were unrelated to property and violent crimes in local areas. However, the density of marijuana outlets in spatially adjacent areas was positively related to property crime in spatially adjacent areas over time. Further, the density of marijuana outlets in local and spatially adjacent blocks groups was related to higher rates of marijuana-specific crime. This study suggests that the effects of the availability of marijuana outlets on crime do not necessarily occur within the specific areas within which these outlets are located, but may occur in adjacent areas. Thus studies assessing the effects of these outlets in local areas alone may risk underestimating their true effects.

MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy.

PubMed

Chowdhury, Rasheda Arman; Zerouali, Younes; Hedrich, Tanguy; Heers, Marcel; Kobayashi, Eliane; Lina, Jean-Marc; Grova, Christophe

2015-11-01

The purpose of this study is to develop and quantitatively assess whether fusion of EEG and MEG (MEEG) data within the maximum entropy on the mean (MEM) framework increases the spatial accuracy of source localization, by yielding better recovery of the spatial extent and propagation pathway of the underlying generators of inter-ictal epileptic discharges (IEDs). The key element in this study is the integration of the complementary information from EEG and MEG data within the MEM framework. MEEG was compared with EEG and MEG when localizing single transient IEDs. The fusion approach was evaluated using realistic simulation models involving one or two spatially extended sources mimicking propagation patterns of IEDs. We also assessed the impact of the number of EEG electrodes required for an efficient EEG-MEG fusion. MEM was compared with minimum norm estimate, dynamic statistical parametric mapping, and standardized low-resolution electromagnetic tomography. The fusion approach was finally assessed on real epileptic data recorded from two patients showing IEDs simultaneously in EEG and MEG. Overall the localization of MEEG data using MEM provided better recovery of the source spatial extent, more sensitivity to the source depth and more accurate detection of the onset and propagation of IEDs than EEG or MEG alone. MEM was more accurate than the other methods. MEEG proved more robust than EEG and MEG for single IED localization in low signal-to-noise ratio conditions. We also showed that only few EEG electrodes are required to bring additional relevant information to MEG during MEM fusion.

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.

A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2012-11-01

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

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.

The gravity of pollination: integrating at-site features into spatial analysis of contemporary pollen movement.

PubMed

DiLeo, Michelle F; Siu, Jenna C; Rhodes, Matthew K; López-Villalobos, Adriana; Redwine, Angela; Ksiazek, Kelly; Dyer, Rodney J

2014-08-01

Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of interindividual distance using spatial and increasingly ecological separation, yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered interindividual spatial distance (e.g. IBD). Models parameterized using conditional genetic covariance (e.g. population graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (isolation by resistance) variables. Overall, our results show that including interindividual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow. © 2014 John Wiley & Sons Ltd.

Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality.

PubMed

Fritscher, Karl; Grunerbl, Agnes; Hanni, Markus; Suhm, Norbert; Hengg, Clemens; Schubert, Rainer

2009-10-01

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively.

The Use of Spatial Analysis to Estimate the Prevalence of Canine Leishmaniasis in Greece and Cyprus to Predict Its Future Variation and Relate It to Human Disease

PubMed Central

Sifaki-Pistola, Dimitra; Ntais, Pantelis; Christodoulou, Vasiliki; Mazeris, Apostolos; Antoniou, Maria

2014-01-01

Climatic, environmental, and demographic changes favor the emergence of neglected vector-borne diseases like leishmaniasis, which is spreading through dogs, the principle host of the protozoan Leishmania infantum. Surveillance of the disease in dogs is important, because the number of infected animals in an area determines the local risk of human infection. However, dog epidemiological studies are costly. Our aim was to evaluate the Emerging Diseases in a Changing European Environment (EDEN) veterinary questionnaire as a cost-effective tool in providing reliable, spatially explicit indicators of canine leishmaniasis prevalence. For this purpose, the data from the questionnaire were compared with data from two epidemiological studies on leishmaniasis carried out in Greece and Cyprus at the same time using statistical methods and spatial statistics. Although the questionnaire data cannot provide a quantitative measure of leishmaniasis in an area, it indicates the dynamic of the disease; information is obtained in a short period of time at low cost. PMID:24957543

Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images.

PubMed

Nguyen, Luong; Tosun, Akif Burak; Fine, Jeffrey L; Lee, Adrian V; Taylor, D Lansing; Chennubhotla, S Chakra

2017-07-01

Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures.

Chaos and Forecasting - Proceedings of the Royal Society Discussion Meeting

NASA Astrophysics Data System (ADS)

Tong, Howell

1995-04-01

The Table of Contents for the full book PDF is as follows: * Preface * Orthogonal Projection, Embedding Dimension and Sample Size in Chaotic Time Series from a Statistical Perspective * A Theory of Correlation Dimension for Stationary Time Series * On Prediction and Chaos in Stochastic Systems * Locally Optimized Prediction of Nonlinear Systems: Stochastic and Deterministic * A Poisson Distribution for the BDS Test Statistic for Independence in a Time Series * Chaos and Nonlinear Forecastability in Economics and Finance * Paradigm Change in Prediction * Predicting Nonuniform Chaotic Attractors in an Enzyme Reaction * Chaos in Geophysical Fluids * Chaotic Modulation of the Solar Cycle * Fractal Nature in Earthquake Phenomena and its Simple Models * Singular Vectors and the Predictability of Weather and Climate * Prediction as a Criterion for Classifying Natural Time Series * Measuring and Characterising Spatial Patterns, Dynamics and Chaos in Spatially-Extended Dynamical Systems and Ecologies * Non-Linear Forecasting and Chaos in Ecology and Epidemiology: Measles as a Case Study

Evaluation of the sensitivity of the Amazonian diurnal cycle to convective intensity in reanalyses

NASA Astrophysics Data System (ADS)

Itterly, Kyle F.; Taylor, Patrick C.

2017-02-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics—specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Spatial and Temporal scales of time-averaged 700 MB height anomalies

NASA Technical Reports Server (NTRS)

Gutzler, D.

1981-01-01

The monthly and seasonal forecasting technique is based to a large extent on the extrapolation of trends in the positions of the centers of time averaged geopotential height anomalies. The complete forecasted height pattern is subsequently drawn around the forecasted anomaly centers. The efficacy of this technique was tested and time series of observed monthly mean and 5 day mean 700 mb geopotential heights were examined. Autocorrelation statistics are generated to document the tendency for persistence of anomalies. These statistics are compared to a red noise hypothesis to check for evidence of possible preferred time scales of persistence. Space-time spectral analyses at middle latitudes are checked for evidence of periodicities which could be associated with predictable month-to-month trends. A local measure of the average spatial scale of anomalies is devised for guidance in the completion of the anomaly pattern around the forecasted centers.

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

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

PubMed

Erdogan, Saffet

2009-10-01

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

Automated delineation and characterization of drumlins using a localized contour tree approach

NASA Astrophysics Data System (ADS)

Wang, Shujie; Wu, Qiusheng; Ward, Dylan

2017-10-01

Drumlins are ubiquitous landforms in previously glaciated regions, formed through a series of complex subglacial processes operating underneath the paleo-ice sheets. Accurate delineation and characterization of drumlins are essential for understanding the formation mechanism of drumlins as well as the flow behaviors and basal conditions of paleo-ice sheets. Automated mapping of drumlins is particularly important for examining the distribution patterns of drumlins across large spatial scales. This paper presents an automated vector-based approach to mapping drumlins from high-resolution light detection and ranging (LiDAR) data. The rationale is to extract a set of concentric contours by building localized contour trees and establishing topological relationships. This automated method can overcome the shortcomings of previously manual and automated methods for mapping drumlins, for instance, the azimuthal biases during the generation of shaded relief images. A case study was carried out over a portion of the New York Drumlin Field. Overall 1181 drumlins were identified from the LiDAR-derived DEM across the study region, which had been underestimated in previous literature. The delineation results were visually and statistically compared to the manual digitization results. The morphology of drumlins was characterized by quantifying the length, width, elongation ratio, height, area, and volume. Statistical and spatial analyses were conducted to examine the distribution pattern and spatial variability of drumlin size and form. The drumlins and the morphologic characteristics exhibit significant spatial clustering rather than randomly distributed patterns. The form of drumlins varies from ovoid to spindle shapes towards the downstream direction of paleo ice flows, along with the decrease in width, area, and volume. This observation is in line with previous studies, which may be explained by the variations in sediment thickness and/or the velocity increases of ice flows towards ice front.

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

DNA viewed as an out-of-equilibrium structure

NASA Astrophysics Data System (ADS)

Provata, A.; Nicolis, C.; Nicolis, G.

2014-05-01

The complexity of the primary structure of human DNA is explored using methods from nonequilibrium statistical mechanics, dynamical systems theory, and information theory. A collection of statistical analyses is performed on the DNA data and the results are compared with sequences derived from different stochastic processes. The use of χ2 tests shows that DNA can not be described as a low order Markov chain of order up to r =6. Although detailed balance seems to hold at the level of a binary alphabet, it fails when all four base pairs are considered, suggesting spatial asymmetry and irreversibility. Furthermore, the block entropy does not increase linearly with the block size, reflecting the long-range nature of the correlations in the human genomic sequences. To probe locally the spatial structure of the chain, we study the exit distances from a specific symbol, the distribution of recurrence distances, and the Hurst exponent, all of which show power law tails and long-range characteristics. These results suggest that human DNA can be viewed as a nonequilibrium structure maintained in its state through interactions with a constantly changing environment. Based solely on the exit distance distribution accounting for the nonequilibrium statistics and using the Monte Carlo rejection sampling method, we construct a model DNA sequence. This method allows us to keep both long- and short-range statistical characteristics of the native DNA data. The model sequence presents the same characteristic exponents as the natural DNA but fails to capture spatial correlations and point-to-point details.

DNA viewed as an out-of-equilibrium structure.

PubMed

Provata, A; Nicolis, C; Nicolis, G

2014-05-01

The complexity of the primary structure of human DNA is explored using methods from nonequilibrium statistical mechanics, dynamical systems theory, and information theory. A collection of statistical analyses is performed on the DNA data and the results are compared with sequences derived from different stochastic processes. The use of χ^{2} tests shows that DNA can not be described as a low order Markov chain of order up to r=6. Although detailed balance seems to hold at the level of a binary alphabet, it fails when all four base pairs are considered, suggesting spatial asymmetry and irreversibility. Furthermore, the block entropy does not increase linearly with the block size, reflecting the long-range nature of the correlations in the human genomic sequences. To probe locally the spatial structure of the chain, we study the exit distances from a specific symbol, the distribution of recurrence distances, and the Hurst exponent, all of which show power law tails and long-range characteristics. These results suggest that human DNA can be viewed as a nonequilibrium structure maintained in its state through interactions with a constantly changing environment. Based solely on the exit distance distribution accounting for the nonequilibrium statistics and using the Monte Carlo rejection sampling method, we construct a model DNA sequence. This method allows us to keep both long- and short-range statistical characteristics of the native DNA data. The model sequence presents the same characteristic exponents as the natural DNA but fails to capture spatial correlations and point-to-point details.

Analysis of spatial and temporal rainfall trends in Sicily during the 1921-2012 period

NASA Astrophysics Data System (ADS)

Liuzzo, Lorena; Bono, Enrico; Sammartano, Vincenzo; Freni, Gabriele

2016-10-01

Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the 1921-2012 period. Downward trends frequently occurred during the autumn and winter months. However, an increase in total annual precipitation was detected during the period from 1981 to 2012.

A spatial analysis of health-related resources in three diverse metropolitan areas

PubMed Central

Smiley, Melissa J.; Diez Roux, Ana V.; Brines, Shannon J.; Brown, Daniel G.; Evenson, Kelly R.; Rodriguez, Daniel A.

2010-01-01

Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5-mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with the others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning. PMID:20478737

The MAC aerosol climatology

NASA Astrophysics Data System (ADS)

Kinne, S.

2015-12-01

Aerosol is highly diverse in space and time. And many different aerosol optical properties are needed (consistent to each other) for the determination of radiative effects. To sidestep a complex (and uncertain) aerosol treatment (emission to mass to optics) a monthly gridded climatology for aerosol properties has been developed. This MPI Aerosol Climatology (MAC) is strongly tied to observational statistics for aerosol column optical properties by AERONET (over land) and by MAN (over oceans). To fill spatial gaps, to address decadal change and to address vertical variability, these sparsely distributed local data are extended with central data of an ensemble of output from global models with complex aerosol modules. This data merging in performed for aerosol column amount (AOD), for aerosol size (AOD,fine) and for aerosol absorption (AAOD). The resulting MAC aerosol climatology is an example for the combination of high quality local observations with spatial, temporal and vertical context from model simulations.

Statistics of natural binaural sounds.

PubMed

Młynarski, Wiktor; Jost, Jürgen

2014-01-01

Binaural sound localization is usually considered a discrimination task, where interaural phase (IPD) and level (ILD) disparities at narrowly tuned frequency channels are utilized to identify a position of a sound source. In natural conditions however, binaural circuits are exposed to a stimulation by sound waves originating from multiple, often moving and overlapping sources. Therefore statistics of binaural cues depend on acoustic properties and the spatial configuration of the environment. Distribution of cues encountered naturally and their dependence on physical properties of an auditory scene have not been studied before. In the present work we analyzed statistics of naturally encountered binaural sounds. We performed binaural recordings of three auditory scenes with varying spatial configuration and analyzed empirical cue distributions from each scene. We have found that certain properties such as the spread of IPD distributions as well as an overall shape of ILD distributions do not vary strongly between different auditory scenes. Moreover, we found that ILD distributions vary much weaker across frequency channels and IPDs often attain much higher values, than can be predicted from head filtering properties. In order to understand the complexity of the binaural hearing task in the natural environment, sound waveforms were analyzed by performing Independent Component Analysis (ICA). Properties of learned basis functions indicate that in natural conditions soundwaves in each ear are predominantly generated by independent sources. This implies that the real-world sound localization must rely on mechanisms more complex than a mere cue extraction.

Statistics of Natural Binaural Sounds

PubMed Central

Młynarski, Wiktor; Jost, Jürgen

2014-01-01

Binaural sound localization is usually considered a discrimination task, where interaural phase (IPD) and level (ILD) disparities at narrowly tuned frequency channels are utilized to identify a position of a sound source. In natural conditions however, binaural circuits are exposed to a stimulation by sound waves originating from multiple, often moving and overlapping sources. Therefore statistics of binaural cues depend on acoustic properties and the spatial configuration of the environment. Distribution of cues encountered naturally and their dependence on physical properties of an auditory scene have not been studied before. In the present work we analyzed statistics of naturally encountered binaural sounds. We performed binaural recordings of three auditory scenes with varying spatial configuration and analyzed empirical cue distributions from each scene. We have found that certain properties such as the spread of IPD distributions as well as an overall shape of ILD distributions do not vary strongly between different auditory scenes. Moreover, we found that ILD distributions vary much weaker across frequency channels and IPDs often attain much higher values, than can be predicted from head filtering properties. In order to understand the complexity of the binaural hearing task in the natural environment, sound waveforms were analyzed by performing Independent Component Analysis (ICA). Properties of learned basis functions indicate that in natural conditions soundwaves in each ear are predominantly generated by independent sources. This implies that the real-world sound localization must rely on mechanisms more complex than a mere cue extraction. PMID:25285658

Towards a street-level pollen concentration and exposure forecast

NASA Astrophysics Data System (ADS)

van der Molen, Michiel; Krol, Maarten; van Vliet, Arnold; Heuvelink, Gerard

2015-04-01

Atmospheric pollen are an increasing source of nuisance for people in industrialised countries and are associated with significant cost of medication and sick leave. Citizen pollen warnings are often based on emission mapping based on local temperature sum approaches or on long-range atmospheric model approaches. In practise, locally observed pollen may originate from both local sources (plants in streets and gardens) and from long-range transport. We argue that making this distinction is relevant because the diurnal and spatial variation in pollen concentrations is much larger for pollen from local sources than for pollen from long-range transport due to boundary layer processes. This may have an important impact on exposure of citizens to pollen and on mitigation strategies. However, little is known about the partitioning of pollen into local and long-range origin categories. Our objective is to study how the concentrations of pollen from different sources vary temporally and spatially, and how the source region influences exposure and mitigation strategies. We built a Hay Fever Forecast system (HFF) based on WRF-chem, Allergieradar.nl, and geo-statistical downscaling techniques. HFF distinguishes between local (individual trees) and regional sources (based on tree distribution maps). We show first results on how the diurnal variation of pollen concentrations depends on source proximity. Ultimately, we will compare the model with local pollen counts, patient nuisance scores and medicine use.

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.

Mining Claim Activity on Federal Land in the United States

USGS Publications Warehouse

Causey, J. Douglas

2007-01-01

Several statistical compilations of mining claim activity on Federal land derived from the Bureau of Land Management's LR2000 database have previously been published by the U.S Geological Survey (USGS). The work in the 1990s did not include Arkansas or Florida. None of the previous reports included Alaska because it is stored in a separate database (Alaska Land Information System) and is in a different format. This report includes data for all states for which there are Federal mining claim records, beginning in 1976 and continuing to the present. The intent is to update the spatial and statistical data associated with this report on an annual basis, beginning with 2005 data. The statistics compiled from the databases are counts of the number of active mining claims in a section of land each year from 1976 to the present for all states within the United States. Claim statistics are subset by lode and placer types, as well as a dataset summarizing all claims including mill site and tunnel site claims. One table presents data by case type, case status, and number of claims in a section. This report includes a spatial database for each state in which mining claims were recorded, except North Dakota, which only has had two claims. A field is present that allows the statistical data to be joined to the spatial databases so that spatial displays and analysis can be done by using appropriate geographic information system (GIS) software. The data show how mining claim activity has changed in intensity, space, and time. Variations can be examined on a state, as well as a national level. The data are tied to a section of land, approximately 640 acres, which allows it to be used at regional, as well as local scale. The data only pertain to Federal land and mineral estate that was open to mining claim location at the time the claims were staked.

Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

PubMed Central

Zhao, Liang; Chen, Feng; Dai, Jing; Hua, Ting; Lu, Chang-Tien; Ramakrishnan, Naren

2014-01-01

Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. PMID:25350136

Effects of spatial variability and scale on areal -average evapotranspiration

NASA Technical Reports Server (NTRS)

Famiglietti, J. S.; Wood, Eric F.

1993-01-01

This paper explores the effect of spatial variability and scale on areally-averaged evapotranspiration. A spatially-distributed water and energy balance model is employed to determine the effect of explicit patterns of model parameters and atmospheric forcing on modeled areally-averaged evapotranspiration over a range of increasing spatial scales. The analysis is performed from the local scale to the catchment scale. The study area is King's Creek catchment, an 11.7 sq km watershed located on the native tallgrass prairie of Kansas. The dominant controls on the scaling behavior of catchment-average evapotranspiration are investigated by simulation, as is the existence of a threshold scale for evapotranspiration modeling, with implications for explicit versus statistical representation of important process controls. It appears that some of our findings are fairly general, and will therefore provide a framework for understanding the scaling behavior of areally-averaged evapotranspiration at the catchment and larger scales.

BATSE analysis techniques for probing the GRB spatial and luminosity distributions

NASA Technical Reports Server (NTRS)

Hakkila, Jon; Meegan, Charles A.

1992-01-01

The Burst And Transient Source Experiment (BATSE) has measured homogeneity and isotropy parameters from an increasingly large sample of observed gamma-ray bursts (GRBs), while also maintaining a summary of the way in which the sky has been sampled. Measurement of both of these are necessary for any study of the BATSE data statistically, as they take into account the most serious observational selection effects known in the study of GRBs: beam-smearing and inhomogeneous, anisotropic sky sampling. Knowledge of these effects is important to analysis of GRB angular and intensity distributions. In addition to determining that the bursts are local, it is hoped that analysis of such distributions will allow boundaries to be placed on the true GRB spatial distribution and luminosity function. The technique for studying GRB spatial and luminosity distributions is direct. Results of BATSE analyses are compared to Monte Carlo models parameterized by a variety of spatial and luminosity characteristics.

Virtual Auditory Space Training-Induced Changes of Auditory Spatial Processing in Listeners with Normal Hearing.

PubMed

Nisha, Kavassery Venkateswaran; Kumar, Ajith Uppunda

2017-04-01

Localization involves processing of subtle yet highly enriched monaural and binaural spatial cues. Remediation programs aimed at resolving spatial deficits are surprisingly scanty in literature. The present study is designed to explore the changes that occur in the spatial performance of normal-hearing listeners before and after subjecting them to virtual acoustic space (VAS) training paradigm using behavioral and electrophysiological measures. Ten normal-hearing listeners participated in the study, which was conducted in three phases, including a pre-training, training, and post-training phase. At the pre- and post-training phases both behavioral measures of spatial acuity and electrophysiological P300 were administered. The spatial acuity of the participants in the free field and closed field were measured apart from quantifying their binaural processing abilities. The training phase consisted of 5-8 sessions (20 min each) carried out using a hierarchy of graded VAS stimuli. The results obtained from descriptive statistics were indicative of an improvement in all the spatial acuity measures in the post-training phase. Statistically, significant changes were noted in interaural time difference (ITD) and virtual acoustic space identification scores measured in the post-training phase. Effect sizes (r) for all of these measures were substantially large, indicating the clinical relevance of these measures in documenting the impact of training. However, the same was not reflected in P300. The training protocol used in the present study on a preliminary basis proves to be effective in normal-hearing listeners, and its implications can be extended to other clinical population as well.

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.

Statistical Ensemble of Large Eddy Simulations

NASA Technical Reports Server (NTRS)

Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)

2001-01-01

A statistical ensemble of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an ensemble averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the ensemble averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the ensemble of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The ensemble averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical ensemble provided that the ensemble contains at least 16 realizations.

Functional localization of the human color center by decreased water displacement using diffusion-weighted fMRI.

PubMed

Williams, Rebecca J; Reutens, David C; Hocking, Julia

2015-11-01

Decreased water displacement following increased neural activity has been observed using diffusion-weighted functional MRI (DfMRI) at high b-values. The physiological mechanisms underlying the diffusion signal change may be unique from the standard blood oxygenation level-dependent (BOLD) contrast and closer to the source of neural activity. Whether DfMRI reflects neural activity more directly than BOLD outside the primary cerebral regions remains unclear. Colored and achromatic Mondrian visual stimuli were statistically contrasted to functionally localize the human color center Area V4 in neurologically intact adults. Spatial and temporal properties of DfMRI and BOLD activation were examined across regions of the visual cortex. At the individual level, DfMRI activation patterns showed greater spatial specificity to V4 than BOLD. The BOLD activation patterns were more prominent in the primary visual cortex than DfMRI, where activation was localized to the ventral temporal lobe. Temporally, the diffusion signal change in V4 and V1 both preceded the corresponding hemodynamic response, however the early diffusion signal change was more evident in V1. DfMRI may be of use in imaging applications implementing cognitive subtraction paradigms, and where highly precise individual functional localization is required.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Swain, P. H. (Principal Investigator); Vardeman, S. B.

1981-01-01

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

USGS Publications Warehouse

Wallace, Cynthia S.A.; Advised by Marsh, Stuart E.

2002-01-01

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created.Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition.Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population.Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Spatial Patterns and Socioecological Drivers of Dengue Fever Transmission in Queensland, Australia

PubMed Central

Clements, Archie; Williams, Gail; Tong, Shilu; Mengersen, Kerrie

2011-01-01

Background: Understanding how socioecological factors affect the transmission of dengue fever (DF) may help to develop an early warning system of DF. Objectives: We examined the impact of socioecological factors on the transmission of DF and assessed potential predictors of locally acquired and overseas-acquired cases of DF in Queensland, Australia. Methods: We obtained data from Queensland Health on the numbers of notified DF cases by local government area (LGA) in Queensland for the period 1 January 2002 through 31 December 2005. Data on weather and the socioeconomic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive model was fitted at the LGA level to quantify the relationship between DF and socioecological factors. Results: Our estimates suggest an increase in locally acquired DF of 6% [95% credible interval (CI): 2%, 11%] and 61% (95% CI: 2%, 241%) in association with a 1-mm increase in average monthly rainfall and a 1°C increase in average monthly maximum temperature between 2002 and 2005, respectively. By contrast, overseas-acquired DF cases increased by 1% (95% CI: 0%, 3%) and by 1% (95% CI: 0%, 2%) in association with a 1-mm increase in average monthly rainfall and a 1-unit increase in average socioeconomic index, respectively. Conclusions: Socioecological factors appear to influence the transmission of DF in Queensland, but the drivers of locally acquired and overseas-acquired DF may differ. DF risk is spatially clustered with different patterns for locally acquired and overseas-acquired cases. PMID:22015625

The relative influence of local and regional environmental drivers of algal biomass (chlorophyll-a) varies by estuarine location

NASA Astrophysics Data System (ADS)

Wainger, Lisa; Yu, Hao; Gazenski, Kim; Boynton, Walter

2016-09-01

A major question in restoring estuarine water quality is whether local actions to manage excess nutrients can be effective, given that estuaries are also responding to tidal inputs from adjacent water bodies. Several types of statistical analysis were used to examine spatially-detailed and long-term water quality monitoring data in eight sub-estuaries of Chesapeake Bay. These sub-estuaries are likely to be similar to other shallow systems with moderate to long water residence times. Statistical cluster analysis of spatial water quality data suggested that estuaries had spatially distinct water quality zones and that the peak algal biomass (as measured by chlorophyll-a) was most often controlled by local watershed inputs in all but one estuary, although mainstem inputs affected most estuaries at some times and places. An elasticity indicator that compared inter-annual changes in sub-estuaries to parallel changes in the mainstem Chesapeake Bay supported the idea that water quality in sub-estuaries was not strongly coupled to the mainstem. A cross-channel zonation of water quality observed near the mouth of estuaries suggested that Bay influences were stronger on the right side of the lower channel (looking up estuary) at times in all estuaries, and was most common in small estuaries closest to the mouth of the primary water source to the estuary. Where Bay influences were strong, estuarine water quality would be expected to be less responsive to nutrient reductions made in the local watershed. Regression analysis was used to evaluate hypothesized relationships between environmental driver variables and average chlorophyll-a (chl-a) concentrations. Chl-a values were calculated from unusually detailed levels of spatial sampling, potentially providing a more comprehensive view of system conditions than that provided by traditional sparse sampling networks. The univariate models with the best data support to explain variability in averaged chl-a concentration were those that reflected water residence time. Of the land cover variables tested, septic density in the riparian zone explained the most variance in chl-a. The multivariate models that most improved upon the residence time effect added TN or TP flows (normalized by volume) and suggested that chl-a will be less responsive to nutrient reductions in estuaries that are poorly flushed.

Observatory enabled discovery of diffuse discharge temperature structure

NASA Astrophysics Data System (ADS)

Bemis, K. G.; Lee, R.; Ivakin, A. N.

2016-12-01

Underwater cabled observatories provide long term but short time and spatial scale measurements of hydrothermal discharge properties. For the first time, an intricate picture of diffuse discharge has been captured at both Axial Volcano (Axial) and the Main Endeavour Field (MEF) on the Juan de Fuca Ridge. This study combines thermistor (3D array, 2D array and spot) and acoustic data to compare the statistical and distribution characteristics of diffuse discharge for narrow crack flow (at ASHES field on Axial) and distributive flow out of a sulfide structure (at Grotto vent in MEF). Two surprising observations seem to apply to both styles of diffuse discharge: (1) thermal variance scales with the mean temperature suggesting coherent flow structures exist in the form of plumes, wakes or boundary layers, and (2) thermal hot spots are persistently localized in space, despite tidal current disruption. Thermal variance was measured at ASHES using a 3D thermistor array (TMPSF) with 10 s sampling over two years and at Grotto using 2D thermistor arrays with 1 hr sampling over several years and a ROV-held CTD (Seabird 39plus) with 0.5 second sampling over several minutes. For locations with temperatures greater than ambient, the variance in temperature scales with the mean temperature. This unusual statistical property is characteristic of self-similar flows like plumes, wakes, and boundary layers and arises from the bounded mixing of a cooling high temperature fluid with a cold ambient fluid. Thus this observation implies an underlying coherence to the diffuse discharge that has not yet been adequately captured or described. A coherent flow like a plume should have a discoverable spatial pattern, albeit one that may vary with the influence of tides. Acoustic observations ( 1m diameter footprint) of the Grotto sulfide edifice found stable local hot spots of diffuse discharge that sway with tides. In contrast, the 3D thermistor array at ASHES sees very localized (single thermistor) hot spots that persist for months. Is this a fundamental difference between two styles of diffuse discharge? Alternate conceptual models of diffuse discharge are used to place localized observations in a spatial context and develop a rigorous understanding of the spatial and temporal pattern of diffuse discharge for both crack and distributive styles.

CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

1998-10-01

Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

A statistical model and national data set for partioning fish-tissue mercury concentration variation between spatiotemporal and sample characteristic effects

USGS Publications Warehouse

Wente, Stephen P.

2004-01-01

Many Federal, Tribal, State, and local agencies monitor mercury in fish-tissue samples to identify sites with elevated fish-tissue mercury (fish-mercury) concentrations, track changes in fish-mercury concentrations over time, and produce fish-consumption advisories. Interpretation of such monitoring data commonly is impeded by difficulties in separating the effects of sample characteristics (species, tissues sampled, and sizes of fish) from the effects of spatial and temporal trends on fish-mercury concentrations. Without such a separation, variation in fish-mercury concentrations due to differences in the characteristics of samples collected over time or across space can be misattributed to temporal or spatial trends; and/or actual trends in fish-mercury concentration can be misattributed to differences in sample characteristics. This report describes a statistical model and national data set (31,813 samples) for calibrating the aforementioned statistical model that can separate spatiotemporal and sample characteristic effects in fish-mercury concentration data. This model could be useful for evaluating spatial and temporal trends in fishmercury concentrations and developing fish-consumption advisories. The observed fish-mercury concentration data and model predictions can be accessed, displayed geospatially, and downloaded via the World Wide Web (http://emmma.usgs.gov). This report and the associated web site may assist in the interpretation of large amounts of data from widespread fishmercury monitoring efforts.

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.

Applications of spatial statistical network models to stream data

USGS Publications Warehouse

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

2014-01-01

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

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

PubMed

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

2008-07-15

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

Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation.

PubMed

Mesadi, Fitsum; Cetin, Mujdat; Tasdizen, Tolga

2015-10-01

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.

Spatial statistical network models for stream and river temperature in New England, USA

NASA Astrophysics Data System (ADS)

Detenbeck, Naomi E.; Morrison, Alisa C.; Abele, Ralph W.; Kopp, Darin A.

2016-08-01

Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May-September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.5°C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total).

Spatio-Temporal Trends of Fire in Slash and Burn Agriculture Landscape: A Case Study from Nagaland, India

NASA Astrophysics Data System (ADS)

Padalia, H.; Mondal, P. P.

2014-11-01

Increasing incidences of fire from land conversion and residue burning in tropics is the major concern in global warming. Spatial and temporal monitoring of trends of fire incidences is, therefore, significant in order to determine contribution of carbon emissions from slash and burn agriculture. In this study, we analyzed time-series Terra / Aqua MODIS satellite hotspot products from 2001 to 2013 to derive intra- and inter-annual trends in fire incidences in Nagaland state, located in the Indo-Burma biodiversity hotspot. Time-series regression was applied to MODIS fire products at variable spatial scales in GIS. Significance of change in fire frequency at each grid level was tested using t statistic. Spatial clustering of higher or lower fire incidences across study area was determined using Getis-OrdGi statistic. Maximum fire incidences were encountered in moist mixed deciduous forests (46%) followed by secondary moist bamboo brakes (30%). In most parts of the study area fire incidences peaked during March while in warmer parts (e.g. Mon district dominated by indigenous people) fire activity starts as early as during November and peaks in January. Regression trend analysis captured noticeable areas with statistically significant positive (e.g. Mokokchung, Wokha, Mon, Tuensang and Kiphire districts) and negative (e.g. Kohima and north-western part of Mokokchung district) inter-annual fire frequency trends based on area-based aggregation of fire occurrences at different grid sizes. Localization of spatial clusters of high fire incidences was observed in Mokokchung, Wokha, Mon,Tuensang and Kiphire districts.

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

PubMed

Tango, Toshiro; Takahashi, Kunihiko

2012-12-30

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

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

USGS Publications Warehouse

Pervez, Md Shahriar; Brown, Jesslyn F.

2010-01-01

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

NASA Astrophysics Data System (ADS)

Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

2013-04-01

Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

Effects of population succession on demographic and genetic processes: predictions and tests in the daylily Hemerocallis thunbergii (Liliaceae).

PubMed

Chung, Mi Yoon; Nason, John D; Chung, Myong Gi

2007-07-01

Spatial genetic structure within plant populations is influenced by variation in demographic processes through space and time, including a population's successional status. To determine how demographic structure and fine-scale genetic structure (FSGS) change with stages in a population's successional history, we studied Hemerocallis thunbergii (Liliaceae), a nocturnal flowering and hawkmoth-pollinated herbaceous perennial with rapid population turnover dynamics. We examined nine populations assigned to three successive stages of population succession: expansion, maturation, and senescence. We developed stage-specific expectations for within-population demographic and genetic structure, and then for each population quantified the spatial aggregation of individuals and genotypes using spatial autocorrelation methods (nonaccumulative O-ring and kinship statistics, respectively), and at the landscape level measured inbreeding and genetic structure using Wright's F-statistics. Analyses using the O-ring statistic revealed significant aggregation of individuals at short spatial scales in expanding and senescing populations, in particular, which may reflect restricted seed dispersal around maternal individuals combined with relatively low local population densities at these stages. Significant FSGS was found for three of four expanding, no mature, and only one senescing population, a pattern generally consistent with expectations of successional processes. Although allozyme genetic diversity was high within populations (mean %P = 78.9 and H(E) = 0.281), landscape-level differentiation among sites was also high (F(ST) = 0.166) and all populations exhibited a significant deficit of heterozygotes relative to Hardy-Weinberg expectations (range F = 0.201-0.424, mean F(IS) = 0.321). Within populations, F was not correlated with the degree of FSGS, thus suggesting inbreeding due primarily to selfing as opposed to mating among close relatives in spatially structured populations. Our results demonstrate considerable variation in the spatial distribution of individuals and patterns and magnitude of FSGS in H. thunbergii populations across the landscape. This variation is generally consistent with succession-stage-specific differences in ecological processes operating within these populations.

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.

Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

NASA Astrophysics Data System (ADS)

Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

2014-02-01

Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

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.

Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations

NASA Astrophysics Data System (ADS)

Lorenzi, Marco; Simpson, Ivor J.; Mendelson, Alex F.; Vos, Sjoerd B.; Cardoso, M. Jorge; Modat, Marc; Schott, Jonathan M.; Ourselin, Sebastien

2016-04-01

The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer’s disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas.

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.

Spatial analysis of electricity demand patterns in Greece: Application of a GIS-based methodological framework

NASA Astrophysics Data System (ADS)

Tyralis, Hristos; Mamassis, Nikos; Photis, Yorgos N.

2016-04-01

We investigate various uses of electricity demand in Greece (agricultural, commercial, domestic, industrial use as well as use for public and municipal authorities and street lightning) and we examine their relation with variables such as population, total area, population density and the Gross Domestic Product. The analysis is performed on data which span from 2008 to 2012 and have annual temporal resolution and spatial resolution down to the level of prefecture. We both visualize the results of the analysis and we perform cluster and outlier analysis using the Anselin local Moran's I statistic as well as hot spot analysis using the Getis-Ord Gi* statistic. The definition of the spatial patterns and relationships of the aforementioned variables in a GIS environment provides meaningful insight and better understanding of the regional development model in Greece and justifies the basis for an energy demand forecasting methodology. Acknowledgement: This research has been partly financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARISTEIA II: Reinforcement of the interdisciplinary and/ or inter-institutional research and innovation (CRESSENDO project; grant number 5145).

[Study on ecological suitability regionalization of Eucommia ulmoides in Guizhou].

PubMed

Kang, Chuan-Zhi; Wang, Qing-Qing; Zhou, Tao; Jiang, Wei-Ke; Xiao, Cheng-Hong; Xie, Yu

2014-05-01

To study the ecological suitability regionalization of Eucommia ulmoides, for selecting artificial planting base and high-quality industrial raw material purchase area of the herb in Guizhou. Based on the investigation of 14 Eucommia ulmoides producing areas, pinoresinol diglucoside content and ecological factors were obtained. Using spatial analysis method to carry on ecological suitability regionalization. Meanwhile, combining pinoresinol diglucoside content, the correlation of major active components and environmental factors were analyzed by statistical analysis. The most suitability planting area of Eucommia ulmoides was the northwest of Guizhou. The distribution of Eucommia ulmoides was mainly affected by the type and pH value of soil, and monthly precipitation. The spatial structure of major active components in Eucommia ulmoides were randomly distributed in global space, but had only one aggregation point which had a high positive correlation in local space. The major active components of Eucommia ulmoides had no correlation with altitude, longitude or latitude. Using the spatial analysis method and statistical analysis method, based on environmental factor and pinoresinol diglucoside content, the ecological suitability regionalization of Eucommia ulmoides can provide reference for the selection of suitable planting area, artificial planting base and directing production layout.

The color of sea level: Importance of spatial variations in spectral shape for assessing the significance of trends

NASA Astrophysics Data System (ADS)

Hughes, Chris W.; Williams, Simon D. P.

2010-10-01

We investigate spatial variations in the shape of the spectrum of sea level variability based on a homogeneously sampled 12 year gridded altimeter data set. We present a method of plotting spectral information as color, focusing on periods between 2 and 24 weeks, which shows that significant spatial variations in the spectral shape exist and contain useful dynamical information. Using the Bayesian Information Criterion, we determine that, typically, a fifth-order autoregressive model is needed to capture the structure in the spectrum. Using this model, we show that statistical errors in fitted local trends range between less than 1 and more than 5 times of what would be calculated assuming "white" noise and that the time needed to detect a 1 mm/yr trend ranges between about 5 years and many decades. For global mean sea level, the statistical error reduces to 0.1 mm/yr over 12 years, with only 2 years needed to detect a 1 mm/yr trend. We find significant regional differences in trend from the global mean. The patterns of these regional differences are indicative of a sea level trend dominated by dynamical ocean processes over this period.

A Space–Time Permutation Scan Statistic for Disease Outbreak Detection

PubMed Central

Kulldorff, Martin; Heffernan, Richard; Hartman, Jessica; Assunção, Renato; Mostashari, Farzad

2005-01-01

Background The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant. Methods and Findings We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest. Conclusion If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems. PMID:15719066

Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2012-01-01

This product "Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area" is a 1:250,000-scale point spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)

PubMed Central

2017-01-01

When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above threshold—when contrast pedestals are clearly visible—vision's spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications. PMID:28989735

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 localization in heterogeneous systems

NASA Astrophysics Data System (ADS)

Kao, Hsien-Ching; Beaume, Cédric; Knobloch, Edgar

2014-01-01

We study spatial localization in the generalized Swift-Hohenberg equation with either quadratic-cubic or cubic-quintic nonlinearity subject to spatially heterogeneous forcing. Different types of forcing (sinusoidal or Gaussian) with different spatial scales are considered and the corresponding localized snaking structures are computed. The results indicate that spatial heterogeneity exerts a significant influence on the location of spatially localized structures in both parameter space and physical space, and on their stability properties. The results are expected to assist in the interpretation of experiments on localized structures where departures from spatial homogeneity are generally unavoidable.

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.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Spatial Distribution of Trends and Seasonality in the Hemispheric Sea Ice Covers

NASA Technical Reports Server (NTRS)

Gloersen, P.; Parkinson, C. L.; Cavalieri, D. J.; Cosmiso, J. C.; Zwally, H. J.

1998-01-01

We extend earlier analyses of a 9-year sea ice data set that described the local seasonal and trend variations in each of the hemispheric sea ice covers to the recently merged 18.2-year sea ice record from four satellite instruments. The seasonal cycle characteristics remain essentially the same as for the shorter time series, but the local trends are markedly different, in some cases reversing sign. The sign reversal reflects the lack of a consistent long-term trend and could be the result of localized long-term oscillations in the hemispheric sea ice covers. By combining the separate hemispheric sea ice records into a global one, we have shown that there are statistically significant net decreases in the sea ice coverage on a global scale. The change in the global sea ice extent, is -0.01 +/- 0.003 x 10(exp 6) sq km per decade. The decrease in the areal coverage of the sea ice is only slightly smaller, so that the difference in the two, the open water within the packs, has no statistically significant change.

Field warming experiments shed light on the wheat yield response to temperature in China

PubMed Central

Zhao, Chuang; Piao, Shilong; Huang, Yao; Wang, Xuhui; Ciais, Philippe; Huang, Mengtian; Zeng, Zhenzhong; Peng, Shushi

2016-01-01

Wheat growth is sensitive to temperature, but the effect of future warming on yield is uncertain. Here, focusing on China, we compiled 46 observations of the sensitivity of wheat yield to temperature change (SY,T, yield change per °C) from field warming experiments and 102 SY,T estimates from local process-based and statistical models. The average SY,T from field warming experiments, local process-based models and statistical models is −0.7±7.8(±s.d.)% per °C, −5.7±6.5% per °C and 0.4±4.4% per °C, respectively. Moreover, SY,T is different across regions and warming experiments indicate positive SY,T values in regions where growing-season mean temperature is low, and water supply is not limiting, and negative values elsewhere. Gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project appear to capture the spatial pattern of SY,T deduced from warming observations. These results from local manipulative experiments could be used to improve crop models in the future. PMID:27853151

An Analysis of San Diego's Housing Market Using a Geographically Weighted Regression Approach

NASA Astrophysics Data System (ADS)

Grant, Christina P.

San Diego County real estate transaction data was evaluated with a set of linear models calibrated by ordinary least squares and geographically weighted regression (GWR). The goal of the analysis was to determine whether the spatial effects assumed to be in the data are best studied globally with no spatial terms, globally with a fixed effects submarket variable, or locally with GWR. 18,050 single-family residential sales which closed in the six months between April 2014 and September 2014 were used in the analysis. Diagnostic statistics including AICc, R2, Global Moran's I, and visual inspection of diagnostic plots and maps indicate superior model performance by GWR as compared to both global regressions.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

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.

Application of statistical classification methods for predicting the acceptability of well-water quality

NASA Astrophysics Data System (ADS)

Cameron, Enrico; Pilla, Giorgio; Stella, Fabio A.

2018-06-01

The application of statistical classification methods is investigated—in comparison also to spatial interpolation methods—for predicting the acceptability of well-water quality in a situation where an effective quantitative model of the hydrogeological system under consideration cannot be developed. In the example area in northern Italy, in particular, the aquifer is locally affected by saline water and the concentration of chloride is the main indicator of both saltwater occurrence and groundwater quality. The goal is to predict if the chloride concentration in a water well will exceed the allowable concentration so that the water is unfit for the intended use. A statistical classification algorithm achieved the best predictive performances and the results of the study show that statistical classification methods provide further tools for dealing with groundwater quality problems concerning hydrogeological systems that are too difficult to describe analytically or to simulate effectively.

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tak, Sungho; Jang, Kwang Eun; Jung, Jinwook; Jang, Jaeduck; Jeong, Yong; Ye, Jong Chul

2008-02-01

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

A generalized regression model of arsenic variations in the shallow groundwater of Bangladesh

PubMed Central

Taylor, Richard G.; Chandler, Richard E.

2015-01-01

Abstract Localized studies of arsenic (As) in Bangladesh have reached disparate conclusions regarding the impact of irrigation‐induced recharge on As concentrations in shallow (≤50 m below ground level) groundwater. We construct generalized regression models (GRMs) to describe observed spatial variations in As concentrations in shallow groundwater both (i) nationally, and (ii) regionally within Holocene deposits where As concentrations in groundwater are generally high (>10 μg L−1). At these scales, the GRMs reveal statistically significant inverse associations between observed As concentrations and two covariates: (1) hydraulic conductivity of the shallow aquifer and (2) net increase in mean recharge between predeveloped and developed groundwater‐fed irrigation periods. Further, the GRMs show that the spatial variation of groundwater As concentrations is well explained by not only surface geology but also statistical interactions (i.e., combined effects) between surface geology and mean groundwater recharge, thickness of surficial silt and clay, and well depth. Net increases in recharge result from intensive groundwater abstraction for irrigation, which induces additional recharge where it is enabled by a permeable surface geology. Collectively, these statistical associations indicate that irrigation‐induced recharge serves to flush mobile As from shallow groundwater. PMID:27524841

How big should a mammal be? A macroecological look at mammalian body size over space and time

PubMed Central

Smith, Felisa A.; Lyons, S. Kathleen

2011-01-01

Macroecology was developed as a big picture statistical approach to the study of ecology and evolution. By focusing on broadly occurring patterns and processes operating at large spatial and temporal scales rather than on localized and/or fine-scaled details, macroecology aims to uncover general mechanisms operating at organism, population, and ecosystem levels of organization. Macroecological studies typically involve the statistical analysis of fundamental species-level traits, such as body size, area of geographical range, and average density and/or abundance. Here, we briefly review the history of macroecology and use the body size of mammals as a case study to highlight current developments in the field, including the increasing linkage with biogeography and other disciplines. Characterizing the factors underlying the spatial and temporal patterns of body size variation in mammals is a daunting task and moreover, one not readily amenable to traditional statistical analyses. Our results clearly illustrate remarkable regularities in the distribution and variation of mammalian body size across both geographical space and evolutionary time that are related to ecology and trophic dynamics and that would not be apparent without a broader perspective. PMID:21768152

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

Statistical Maps of Ground Magnetic Disturbance Derived from Global Geospace Models

NASA Astrophysics Data System (ADS)

Rigler, E. J.; Wiltberger, M. J.; Love, J. J.

2017-12-01

Electric currents in space are the principal driver of magnetic variations measured at Earth's surface. These in turn induce geoelectric fields that present a natural hazard for technological systems like high-voltage power distribution networks. Modern global geospace models can reasonably simulate large-scale geomagnetic response to solar wind variations, but they are less successful at deterministic predictions of intense localized geomagnetic activity that most impacts technological systems on the ground. Still, recent studies have shown that these models can accurately reproduce the spatial statistical distributions of geomagnetic activity, suggesting that their physics are largely correct. Since the magnetosphere is a largely externally driven system, most model-measurement discrepancies probably arise from uncertain boundary conditions. So, with realistic distributions of solar wind parameters to establish its boundary conditions, we use the Lyon-Fedder-Mobarry (LFM) geospace model to build a synthetic multivariate statistical model of gridded ground magnetic disturbance. From this, we analyze the spatial modes of geomagnetic response, regress on available measurements to fill in unsampled locations on the grid, and estimate the global probability distribution of extreme magnetic disturbance. The latter offers a prototype geomagnetic "hazard map", similar to those used to characterize better-known geophysical hazards like earthquakes and floods.

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.

Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City.

PubMed

Little, Eliza; Bajwa, Waheed; Shaman, Jeffrey

2017-08-01

Ae. albopictus, an invasive mosquito vector now endemic to much of the northeastern US, is a significant public health threat both as a nuisance biter and vector of disease (e.g. chikungunya virus). Here, we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae. albopictus mosquitoes in New York City. Using statistical modeling, we create a fine-scale spatially explicit risk map of Ae. albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016. We find that the spatial variability of annual Ae. albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae. albopictus numbers. Specifically, key land use characteristics, including open spaces, residential areas, and vacant lots, and spring and early summer meteorological conditions are associated with annual Ae. albopictus abundance. In addition, we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation. We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus, to date, has provided a check on local arboviral transmission in New York City. We do, however, find concordant areas where high Ae. albopictus abundance and chikungunya importation co-occur. Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission. The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases.

Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City

PubMed Central

Bajwa, Waheed; Shaman, Jeffrey

2017-01-01

Ae. albopictus, an invasive mosquito vector now endemic to much of the northeastern US, is a significant public health threat both as a nuisance biter and vector of disease (e.g. chikungunya virus). Here, we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae. albopictus mosquitoes in New York City. Using statistical modeling, we create a fine-scale spatially explicit risk map of Ae. albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016. We find that the spatial variability of annual Ae. albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae. albopictus numbers. Specifically, key land use characteristics, including open spaces, residential areas, and vacant lots, and spring and early summer meteorological conditions are associated with annual Ae. albopictus abundance. In addition, we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation. We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus, to date, has provided a check on local arboviral transmission in New York City. We do, however, find concordant areas where high Ae. albopictus abundance and chikungunya importation co-occur. Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission. The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases. PMID:28832586

Advanced dynamic statistical parametric mapping with MEG in localizing epileptogenicity of the bottom of sulcus dysplasia.

PubMed

Nakajima, Midori; Wong, Simeon; Widjaja, Elysa; Baba, Shiro; Okanishi, Tohru; Takada, Lynne; Sato, Yosuke; Iwata, Hiroki; Sogabe, Maya; Morooka, Hikaru; Whitney, Robyn; Ueda, Yuki; Ito, Tomoshiro; Yagyu, Kazuyori; Ochi, Ayako; Carter Snead, O; Rutka, James T; Drake, James M; Doesburg, Sam; Takeuchi, Fumiya; Shiraishi, Hideaki; Otsubo, Hiroshi

2018-06-01

To investigate whether advanced dynamic statistical parametric mapping (AdSPM) using magnetoencephalography (MEG) can better localize focal cortical dysplasia at bottom of sulcus (FCDB). We analyzed 15 children with diagnosis of FCDB in surgical specimen and 3 T MRI by using MEG. Using AdSPM, we analyzed a ±50 ms epoch relative to each single moving dipole (SMD) and applied summation technique to estimate the source activity. The most active area in AdSPM was defined as the location of AdSPM spike source. We compared spatial congruence between MRI-visible FCDB and (1) dipole cluster in SMD method; and (2) AdSPM spike source. AdSPM localized FCDB in 12 (80%) of 15 children whereas dipole cluster localized six (40%). AdSPM spike source was concordant within seizure onset zone in nine (82%) of 11 children with intracranial video EEG. Eleven children with resective surgery achieved seizure freedom with follow-up period of 1.9 ± 1.5 years. Ten (91%) of them had an AdSPM spike source in the resection area. AdSPM can noninvasively and neurophysiologically localize epileptogenic FCDB, whether it overlaps with the dipole cluster or not. This is the first study to localize epileptogenic FCDB using MEG. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Image processing techniques revealing the relationship between the field-measured ambient gamma dose equivalent rate and geological conditions at a granitic area, Velence Mountains, Hungary

NASA Astrophysics Data System (ADS)

Beltran Torres, Silvana; Petrik, Attila; Zsuzsanna Szabó, Katalin; Jordan, Gyozo; Szabó, Csaba

2017-04-01

In order to estimate the annual dose that the public receive from natural radioactivity, the identification of the potential risk areas is required which, in turn, necessitates understanding the relationship between the spatial distribution of natural radioactivity and the geogenic risk factors (e.g., rock types, dykes, faults, soil conditions, etc.). A detailed spatial analysis of ambient gamma dose equivalent rate was performed in the western side of Velence Mountains, the largest outcropped granitic area in Hungary. In order to assess the role of local geology in the spatial distribution of ambient gamma dose rates, field measurements were carried out at ground level at 300 sites along a 250 m x 250 m regular grid in a total surface of 14.7 km2. Digital image processing methods were applied to identify anomalies, heterogeneities and spatial patterns in the measured gamma dose rates, including local maxima and minima determination, digital cross sections, gradient magnitude and gradient direction, second derivative profile curvature, local variability, lineament density, 2D autocorrelation and directional variogram analyses. Statistical inference showed that different gamma dose rate levels are associated with the rock types (i.e., Carboniferous granite, Pleistocene colluvial, proluvial, deluvial sediments and talus, and Pannonian sand and pebble), with the highest level on the Carboniferous granite including outlying values. Moreover, digital image processing revealed that linear gamma dose rate spatial features are parallel to the SW-NE dyke system and possibly to the NW-SE main fractures. The results of this study underline the importance of understanding the role of geogenic risk factors influencing the ambient gamma dose rate received by public. The study also demonstrates the power of the image processing techniques for the identification of spatial pattern in field-measured geogenic radiation.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Introducing local property tax for fiscal decentralization and local authority autonomy

NASA Astrophysics Data System (ADS)

Dimopoulos, Thomas; Labropoulos, Tassos; Hadjimitsis, Diafantos G.

2015-06-01

Charles Tiebout (1956), in his work "A Pure Theory of Local Expenditures", provides a vision of the workings of the local public sector, acknowledging many similarities to the features of a competitive market, however omitting any references to local taxation. Contrary to other researchers' claim that the Tiebout model and the theory of fiscal decentralization are by no means synonymous, this paper aims to expand Tiebout's theory, by adding the local property tax in the context, introducing a fair, ad valorem property taxation system based on the automated assessment of the value of real estate properties within the boundaries of local authorities. Computer Assisted Mass Appraisal methodology integrated with Remote Sensing technology and GIS analysis is applied to local authorities' property registries and cadastral data, building a spatial relational database and providing data to be statistically processed through Multiple Regression Analysis modeling. The proposed scheme accomplishes economy of scale using CAMA procedures on one hand, but also succeeds in making local authorities self-sufficient through a decentralized, fair, locally calibrated property taxation model, providing rational income administration.

[Genetic aspects of species structure of the compost worm Eisenia foetida (Sav.) (Oligochaeta, Lumbricidae)].

PubMed

Bolotetskiĭ, N M; Kodolova, O P

2002-01-01

Distribution of frequencies alleles of polymorphous loci of peroxidase (Pox), leucineaminopeptidase (Lap), phosphoglucomutase (Pgm) and octanoldehydrogenase (Odh) were studied by electrophoresis in polyacrylamide gel in 22 local samples of Esenia foetida in Russia (European part), Ukraine, Kazakhstan and Kirghizia. The samples form two spatial groups--"northern" and "southern", distinguished by set of alleles in every studied locus. The "northern" groups is formed by local populations of European Russia from Murmansk region on the north to Smolensk region on the south, and also by cultivated population of selection line "red California hybrid". The "southern" group is formed by local populations on the territory of Russia from middle Volga to the North Caucasus, Ukraine, Kazakhstan, Kirghizia, cultivated populations from Kirghizia and Portugal. High degree of genetic difference between samples and independence of alleles frequencies distribution from geographical location and habitat allows to consider almost all studied groups as separate populations. Statistical processing of Nei genetic distances (Nei, 1972) revealed reliable differences between averages of within- and intergroup distances. Besides, discrete differences between intervals of significance of genetic distances were revealed. The results indicate that on the studied territory E. foetida has hierarchical two level structure. The first level is formed by local populations differed by frequency of the same alleles. The second level is formed by local populations, united into spatial groups, that are qualitatively distinguished by the set of alleles in the same loci.

Spatially variable risk factors for malaria in a geographically heterogeneous landscape, western Kenya: an explorative study.

PubMed

Homan, Tobias; Maire, Nicolas; Hiscox, Alexandra; Di Pasquale, Aurelio; Kiche, Ibrahim; Onoka, Kelvin; Mweresa, Collins; Mukabana, Wolfgang R; Ross, Amanda; Smith, Thomas A; Takken, Willem

2016-01-04

Large reductions in malaria transmission and mortality have been achieved over the last decade, and this has mainly been attributed to the scale-up of long-lasting insecticidal bed nets and indoor residual spraying with insecticides. Despite these gains considerable residual, spatially heterogeneous, transmission remains. To reduce transmission in these foci, researchers need to consider the local demographical, environmental and social context, and design an appropriate set of interventions. Exploring spatially variable risk factors for malaria can give insight into which human and environmental characteristics play important roles in sustaining malaria transmission. On Rusinga Island, western Kenya, malaria infection was tested by rapid diagnostic tests during two cross-sectional surveys conducted 3 months apart in 3632 individuals from 790 households. For all households demographic data were collected by means of questionnaires. Environmental variables were derived using Quickbird satellite images. Analyses were performed on 81 project clusters constructed by a traveling salesman algorithm, each containing 50-51 households. A standard linear regression model was fitted containing multiple variables to determine how much of the spatial variation in malaria prevalence could be explained by the demographic and environmental data. Subsequently, a geographically-weighted regression (GWR) was performed assuming non-stationarity of risk factors. Special attention was taken to investigate the effect of residual spatial autocorrelation and local multicollinearity. Combining the data from both surveys, overall malaria prevalence was 24%. Scan statistics revealed two clusters which had significantly elevated numbers of malaria cases compared to the background prevalence across the rest of the study area. A multivariable linear model including environmental and household factors revealed that higher socioeconomic status, outdoor occupation and population density were associated with increased malaria risk. The local GWR model improved the model fit considerably and the relationship of malaria with risk factors was found to vary spatially over the island; in different areas of the island socio-economic status, outdoor occupation and population density were found to be positively or negatively associated with malaria prevalence. Identification of risk factors for malaria that vary geographically can provide insight into the local epidemiology of malaria. Examining spatially variable relationships can be a helpful tool in exploring which set of targeted interventions could locally be implemented. Supplementary malaria control may be directed at areas, which are identified as at risk. For instance, areas with many people that work outdoors at night may need more focus in terms of vector control. Trialregister.nl NTR3496-SolarMal, registered on 20 June 2012.

Subband Approach to Bandlimited Crosstalk Cancellation System in Spatial Sound Reproduction

NASA Astrophysics Data System (ADS)

Bai, Mingsian R.; Lee, Chih-Chung

2006-12-01

Crosstalk cancellation system (CCS) plays a vital role in spatial sound reproduction using multichannel loudspeakers. However, this technique is still not of full-blown use in practical applications due to heavy computation loading. To reduce the computation loading, a bandlimited CCS is presented in this paper on the basis of subband filtering approach. A pseudoquadrature mirror filter (QMF) bank is employed in the implementation of CCS filters which are bandlimited to 6 kHz, where human's localization is the most sensitive. In addition, a frequency-dependent regularization scheme is adopted in designing the CCS inverse filters. To justify the proposed system, subjective listening experiments were undertaken in an anechoic room. The experiments include two parts: the source localization test and the sound quality test. Analysis of variance (ANOVA) is applied to process the data and assess statistical significance of subjective experiments. The results indicate that the bandlimited CCS performed comparably well as the fullband CCS, whereas the computation loading was reduced by approximately eighty percent.

Autonomous spatially adaptive sampling in experiments based on curvature, statistical error and sample spacing with applications in LDA measurements

NASA Astrophysics Data System (ADS)

Theunissen, Raf; Kadosh, Jesse S.; Allen, Christian B.

2015-06-01

Spatially varying signals are typically sampled by collecting uniformly spaced samples irrespective of the signal content. For signals with inhomogeneous information content, this leads to unnecessarily dense sampling in regions of low interest or insufficient sample density at important features, or both. A new adaptive sampling technique is presented directing sample collection in proportion to local information content, capturing adequately the short-period features while sparsely sampling less dynamic regions. The proposed method incorporates a data-adapted sampling strategy on the basis of signal curvature, sample space-filling, variable experimental uncertainty and iterative improvement. Numerical assessment has indicated a reduction in the number of samples required to achieve a predefined uncertainty level overall while improving local accuracy for important features. The potential of the proposed method has been further demonstrated on the basis of Laser Doppler Anemometry experiments examining the wake behind a NACA0012 airfoil and the boundary layer characterisation of a flat plate.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

NASA Astrophysics Data System (ADS)

Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

2014-01-01

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Alcohol outlet density and violence: A geographically weighted regression approach.

PubMed

Cameron, Michael P; Cochrane, William; Gordon, Craig; Livingston, Michael

2016-05-01

We investigate the relationship between outlet density (of different types) and violence (as measured by police activity) across the North Island of New Zealand, specifically looking at whether the relationships vary spatially. We use New Zealand data at the census area unit (approximately suburb) level, on police-attended violent incidents and outlet density (by type of outlet), controlling for population density and local social deprivation. We employed geographically weighted regression to obtain both global average and locally specific estimates of the relationships between alcohol outlet density and violence. We find that bar and night club density, and licensed club density (e.g. sports clubs) have statistically significant and positive relationships with violence, with an additional bar or night club is associated with nearly 5.3 additional violent events per year, and an additional licensed club associated with 0.8 additional violent events per year. These relationships do not show significant spatial variation. In contrast, the effects of off-licence density and restaurant/café density do exhibit significant spatial variation. However, the non-varying effects of bar and night club density are larger than the locally specific effects of other outlet types. The relationships between outlet density and violence vary significantly across space for off-licences and restaurants/cafés. These results suggest that in order to minimise alcohol-related harms, such as violence, locally specific policy interventions are likely to be necessary. [Cameron MP, Cochrane W, Gordon C, Livingston M. Alcohol outlet density and violence: A geographically weighted regression approach. Drug Alcohol Rev 2016;35:280-288]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

The Great Recession and America’s Geography of Unemployment

PubMed Central

Thiede, Brian C.; Monnat, Shannon M.

2017-01-01

Background The Great Recession of 2007–2009 was the most severe and lengthy economic crisis in the U.S. since the Great Depression. The impacts on the population were multi-dimensional, but operated largely through local labor markets. Objective To examine differences in recession-related changes in county unemployment rates and assess how population and place characteristics shaped these patterns. Methods We calculate and decompose Theil Indexes to describe recession-related changes in the distribution of unemployment rates between counties and states. We use exploratory spatial statistics to identify geographic clusters of counties that experienced similar changes in unemployment. We use spatial regression to evaluate associations between county-level recession impacts on unemployment and demographic composition, industrial structure, and state context. Results The recession was associated with increased inequality between county labor markets within states, but declining between-state differences. Counties that experienced disproportionate recession-related increases in unemployment were spatially clustered and characterized by large shares of historically disadvantaged racial and ethnic minority populations, low educational attainment, and heavy reliance on pro-cyclical industries. Associations between these sources of vulnerability were partially explained by unobserved state-level factors. Conclusions The local consequences of macroeconomic trends are associated with county population characteristics, as well as the structural contexts and policy environments in which they are embedded. The recession placed upward pressure on within-state inequality between local labor market conditions. Contribution To present new estimates of the recession’s impact on local labor markets, quantify how heterogeneous impacts affected the distribution of unemployment prevalence, and identify county characteristics associated with disproportionately large recession-related increases in unemployment. PMID:28663712

Spatial study of homicide rates in the state of Bahia, Brazil, 1996-2010

PubMed Central

de Souza, Tiago Oliveira; Pinto, Liana Wernersbach; de Souza, Edinilsa Ramos

2014-01-01

OBJECTIVE To analyze the spatial distribution of homicide mortality in the state of Bahia, Northeastern Brazil. METHODS Ecological study of the 15 to 39-year old male population in the state of Bahia in the period 1996-2010. Data from the Mortality Information System, relating to homicide (X85-Y09) and population estimates from the Brazilian Institute of Geography and Statistics were used. The existence of spatial correlation, the presence of clusters and critical areas of the event studied were analyzed using Moran’s I Global and Local indices. RESULTS A non-random spatial pattern was observed in the distribution of rates, as was the presence of three clusters, the first in the north health district, the second in the eastern region, and the third cluster included townships in the south and the far south of Bahia. CONCLUSIONS The homicide mortality in the three different critical areas requires further studies that consider the socioeconomic, cultural and environmental characteristics in order to guide specific preventive and interventionist practices. PMID:25119942

Spatial adaptive sampling in multiscale simulation

NASA Astrophysics Data System (ADS)

Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel; Elango, Venmugil; Junghans, Christoph; Lookman, Turab; Mohd-Yusof, Jamaludin; Pavel, Robert S.; Rivera, Axel Y.; Roehm, Dominic; McPherson, Allen L.; Germann, Timothy C.

2014-07-01

In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈ 50 ×N0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.

Statistics of spatial derivatives of velocity and pressure in turbulent channel flow

NASA Astrophysics Data System (ADS)

Vreman, A. W.; Kuerten, J. G. M.

2014-08-01

Statistical profiles of the first- and second-order spatial derivatives of velocity and pressure are reported for turbulent channel flow at Reτ = 590. The statistics were extracted from a high-resolution direct numerical simulation. To quantify the anisotropic behavior of fine-scale structures, the variances of the derivatives are compared with the theoretical values for isotropic turbulence. It is shown that appropriate combinations of first- and second-order velocity derivatives lead to (directional) viscous length scales without explicit occurrence of the viscosity in the definitions. To quantify the non-Gaussian and intermittent behavior of fine-scale structures, higher-order moments and probability density functions of spatial derivatives are reported. Absolute skewnesses and flatnesses of several spatial derivatives display high peaks in the near wall region. In the logarithmic and central regions of the channel flow, all first-order derivatives appear to be significantly more intermittent than in isotropic turbulence at the same Taylor Reynolds number. Since the nine variances of first-order velocity derivatives are the distinct elements of the turbulence dissipation, the budgets of these nine variances are shown, together with the budget of the turbulence dissipation. The comparison of the budgets in the near-wall region indicates that the normal derivative of the fluctuating streamwise velocity (∂u'/∂y) plays a more important role than other components of the fluctuating velocity gradient. The small-scale generation term formed by triple correlations of fluctuations of first-order velocity derivatives is analyzed. A typical mechanism of small-scale generation near the wall (around y+ = 1), the intensification of positive ∂u'/∂y by local strain fluctuation (compression in normal and stretching in spanwise direction), is illustrated and discussed.

Selected annotated bibliographies for adaptive filtering of digital image data

USGS Publications Warehouse

Mayers, Margaret; Wood, Lynnette

1988-01-01

Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity.Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze.Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography is organized in subsections based on application areas. Contrast enhancement, edge enhancement, noise suppression, and smoothing are typically performed in order imaging process, (for example, degradations due to the optics and electronics of the sensor, or to blurring caused by the intervening atmosphere, uniform motion, or defocused optics). Some of the papers listed may apply to more than one of the above categories; when this happens the paper is listed under the category for which the paper's emphasis is greatest. A list of survey articles is also supplied. These articles are general discussions on adaptive filters and reviews of work done. Finally, a short list of miscellaneous articles are listed which were felt to be sufficiently important to be included, but do not fit into any of the above categories. This bibliography, listing items published from 1970 through 1987, is extensive, but by no means complete. It is intended as a guide for scientists and image analysts, listing references for background information as well as areas of significant development in adaptive filtering.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield

PubMed Central

Ringard, Justine; Seyler, Frederique; Linguet, Laurent

2017-01-01

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale. PMID:28621723

A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield.

PubMed

Ringard, Justine; Seyler, Frederique; Linguet, Laurent

2017-06-16

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

Comparison of Texture Analysis Techniques in Both Frequency and Spatial Domains for Cloud Feature Extraction

DTIC Science & Technology

1992-01-01

entropy , energy. variance, skewness, and object. It can also be applied to an image of a phenomenon. It kurtosis. These parameters are then used as...statistic. The co-occurrence matrix method is used in this study to derive texture values of entropy . Limogeneity. energy (similar to the GLDV angular...from working with the co-occurrence matrix method. Seven convolution sizes were chosen to derive the texture values of entropy , local homogeneity, and

Adaptive Locally Optimum Processing for Interference Suppression from Communication and Undersea Surveillance Signals

DTIC Science & Technology

1994-07-01

1993. "Analysis of the 1730-1732. Track - Before - Detect Approach to Target Detection using Pixel Statistics", to appear in IEEE Transactions Scholz, J...large surveillance arrays. One approach to combining energy in different spatial cells is track - before - detect . References to examples appear in the next... track - before - detect problem. The results obtained are not expected to depend strongly on model details. In particular, the structure of the tracking

StatisticAl Characteristics of Cloud over Beijing, China Obtained FRom Ka band Doppler Radar Observation

NASA Astrophysics Data System (ADS)

LIU, J.; Bi, Y.; Duan, S.; Lu, D.

2017-12-01

It is well-known that cloud characteristics, such as top and base heights and their layering structure of micro-physical parameters, spatial coverage and temporal duration are very important factors influencing both radiation budget and its vertical partitioning as well as hydrological cycle through precipitation data. Also, cloud structure and their statistical distribution and typical values will have respective characteristics with geographical and seasonal variation. Ka band radar is a powerful tool to obtain above parameters around the world, such as ARM cloud radar at the Oklahoma US, Since 2006, Cloudsat is one of NASA's A-Train satellite constellation, continuously observe the cloud structure with global coverage, but only twice a day it monitor clouds over same local site at same local time.By using IAP Ka band Doppler radar which has been operating continuously since early 2013 over the roof of IAP building in Beijing, we obtained the statistical characteristic of clouds, including cloud layering, cloud top and base heights, as well as the thickness of each cloud layer and their distribution, and were analyzed monthly and seasonal and diurnal variation, statistical analysis of cloud reflectivity profiles is also made. The analysis covers both non-precipitating clouds and precipitating clouds. Also, some preliminary comparison of the results with Cloudsat/Calipso products for same period and same area are made.

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

The role of transnational mobility in the local spread of mosquito-borne disease: Measuring the determinants of spatial-temporal lags of imported dengue cases initiating indigenous epidemics in Taiwan

NASA Astrophysics Data System (ADS)

Wen, Tzai-Hung

2014-05-01

Dengue fever is one of the world's most widely spread mosquito-borne diseases. International travelers who acquire dengue infection are important routes for virus transmission from one country to another one. Previous studies have shown that imported dengue cases are able to initiate indigenous epidemics when appropriate weather conditions are present. However, the spatial-temporal associations between imported cases and indigenous epidemics in areas with different social-economic conditions are still unclear. This study investigated determinants of spatial-temporal lags of imported dengue cases who initiated indigenous epidemics from 2003 to 2012 in Taiwan. The quantile regression is used to explore the associations between spatial-temporal lags of imported cases and social-economic indicators with geographic heterogeneity. Our results indicated that imported cases in April and May have statistically significant contribution to initiate indigenous epidemics. Areas with high population density and low average income have significant risk of being imported virus from other areas. However, the areas with imported cases are not significant transmission risk. The results imply that imported cases reported in early summer may be an early-warning indicator of indigenous epidemics. Local demographic and economic conditions, rather than imported cases, may determine the areas with the risk of indigenous epidemics.

The Influence of Spatial Configuration of Residential Area and Vector Populations on Dengue Incidence Patterns in an Individual-Level Transmission Model.

PubMed

Kang, Jeon-Young; Aldstadt, Jared

2017-07-15

Dengue is a mosquito-borne infectious disease that is endemic in tropical and subtropical countries. Many individual-level simulation models have been developed to test hypotheses about dengue virus transmission. Often these efforts assume that human host and mosquito vector populations are randomly or uniformly distributed in the environment. Although, the movement of mosquitoes is affected by spatial configuration of buildings and mosquito populations are highly clustered in key buildings, little research has focused on the influence of the local built environment in dengue transmission models. We developed an agent-based model of dengue transmission in a village setting to test the importance of using realistic environments in individual-level models of dengue transmission. The results from one-way ANOVA analysis of simulations indicated that the differences between scenarios in terms of infection rates as well as serotype-specific dominance are statistically significant. Specifically, the infection rates in scenarios of a realistic environment are more variable than those of a synthetic spatial configuration. With respect to dengue serotype-specific cases, we found that a single dengue serotype is more often dominant in realistic environments than in synthetic environments. An agent-based approach allows a fine-scaled analysis of simulated dengue incidence patterns. The results provide a better understanding of the influence of spatial heterogeneity on dengue transmission at a local scale.

Exceedance probability map: a tool helping the definition of arsenic Natural Background Level (NBL) within the Drainage Basin to the Venice Lagoon (NE Italy)

NASA Astrophysics Data System (ADS)

Dalla Libera, Nico; Fabbri, Paolo; Mason, Leonardo; Piccinini, Leonardo; Pola, Marco

2017-04-01

Arsenic groundwater contamination affects worldwide shallower groundwater bodies. Starting from the actual knowledges around arsenic origin into groundwater, we know that the major part of dissolved arsenic is naturally occurring through the dissolution of As-bearing minerals and ores. Several studies on the shallow aquifers of both the regional Venetian Plain (NE Italy) and the local Drainage Basin to the Venice Lagoon (DBVL) show local high arsenic concentration related to peculiar geochemical conditions, which drive arsenic mobilization. The uncertainty of arsenic spatial distribution makes difficult both the evaluation of the processes involved in arsenic mobilization and the stakeholders' decision about environmental management. Considering the latter aspect, the present study treats the problem of the Natural Background Level (NBL) definition as the threshold discriminating the natural contamination from the anthropogenic pollution. Actually, the UE's Directive 2006/118/EC suggests the procedures and criteria to set up the water quality standards guaranteeing a healthy status and reversing any contamination trends. In addition, the UE's BRIDGE project proposes some criteria, based on the 90th percentile of the contaminant's concentrations dataset, to estimate the NBL. Nevertheless, these methods provides just a statistical NBL for the whole area without considering the spatial variation of the contaminant's concentration. In this sense, we would reinforce the NBL concept using a geostatistical approach, which is able to give some detailed information about the distribution of arsenic concentrations and unveiling zones with high concentrations referred to the Italian drinking water standard (IDWS = 10 µg/liter). Once obtained the spatial information about arsenic distribution, we can apply the 90th percentile methods to estimate some Local NBL referring to every zones with arsenic higher than IDWS. The indicator kriging method was considered because it estimates the spatial distribution of the exceedance probabilities respect some pre-defined thresholds. This approach is largely mentioned in literature to face similar environmental problems. To test the validity of the procedure, we used the dataset from "A.Li.Na" project (founded by the Regional Environmental Agency) that defined regional NBLs of As, Fe, Mn and NH4+ into DBVL's groundwater. Primarily, we defined two thresholds corresponding respectively to the IDWS and the median of the data over the IDWS. These values were decided basing on the dataset's statistical structure and the quality criteria of the GWD 2006/118/EC. Subsequently, we evaluated the spatial distribution of the probability to exceed the defined thresholds using the Indicator kriging. The results highlight different zones with high exceedance probability ranging from 75% to 95% respect both the IDWS and the median value. Considering the geological setting of the DBVL, these probability values correspond with the occurrence of both organic matter and reducing conditions. In conclusion, the spatial prediction of the exceedance probability could be useful to define the areas in which estimate the local NBLs, enhancing the procedure of NBL definition. In that way, the NBL estimation could be more realistic because it considers the spatial distribution of the studied contaminant, distinguishing areas with high natural concentrations from polluted ones.

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review.

PubMed

Manyangadze, Tawanda; Chimbari, Moses John; Gebreslasie, Michael; Mukaratirwa, Samson

2015-11-04

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

Ecological changes and local knowledge in a giant honey bee (Apis dorsata F.) hunting community in Palawan, Philippines.

PubMed

Matias, Denise Margaret S; Borgemeister, Christian; von Wehrden, Henrik

2018-02-24

One of the traditional livelihood practices of indigenous Tagbanuas in Palawan, Philippines is wild honey hunting and gathering from the giant honey bee (Apis dorsata F.). In order to analyze the linkages of the social and ecological systems involved in this indigenous practice, we conducted spatial, quantitative, and qualitative analyses on field data gathered through mapping of global positioning system coordinates, community surveys, and key informant interviews. We found that only 24% of the 251 local community members surveyed could correctly identify the giant honey bee. Inferential statistics showed that a lower level of formal education strongly correlates with correct identification of the giant honey bee. Spatial analysis revealed that mean NDVI of sampled nesting tree areas has dropped from 0.61 in the year 1988 to 0.41 in 2015. However, those who correctly identified the giant honey bee lived in areas with high vegetation cover. Decreasing vegetation cover limits the presence of wild honey bees and this may also be limiting direct experience of the community with wild honey bees. However, with causality yet to be established, we recommend conducting further studies to concretely model feedbacks between ecological changes and local knowledge.

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

Geography of breast cancer incidence according to age & birth cohorts.

PubMed

Gregorio, David I; Ford, Chandler; Samociuk, Holly

2017-06-01

Geographic variation in breast cancer incidence across Connecticut was examined according to age and birth cohort -specific groups. We assigned each of 60,937 incident breast cancer cases diagnosed in Connecticut, 1986-2009, to one of 828 census tracts around the state. Global and local spatial statistics estimated rate variation across the state according to age and birth cohorts. We found the global distribution of incidence rates across places to be more heterogeneous for younger women and later birth cohorts. Concurrently, the spatial scan identified more locations with significantly high rates that pertained to larger proportions of at-risk women within these groups. Geographic variation by age groups was more pronounced than by birth cohorts. Geographic patterns of cancer incidence exhibit differences within and across age and birth cohorts. With the continued insights from descriptive epidemiology, our capacity to effectively limit spatial disparities in cancer will improve. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using High-Resolution Population Data to Identify Neighborhoods and Establish Their Boundaries

PubMed Central

Spielman, Seth E.; Logan, John R.

2012-01-01

Neighborhoods are about local territory, but what territory? This paper offers one approach to this question through a novel application of “local” spatial statistics. We conceptualize a neighborhood in terms of both space and social composition; it is a contiguous territory defined by a bundle of social attributes that distinguish it from surrounding areas. Our method does not impose either a specific social characteristic or a predetermined spatial scale to define a neighborhood. Rather we infer neighborhoods from detailed information about individual residents and their locations. The analysis is based on geocoded complete-count census data from the late 19th Century in four cities: Albany, NY, Buffalo, NY, Cincinnati, OH, and Newark, NJ. We find striking regularities (and some anomalies) in the spatial structure of the cities studied. Our approach illustrates the “spatialization” of an important social scientific concept. PMID:23279975

Using vadose zone data and spatial statistics to assess the impact of cultivated land and dairy waste lagoons on groundwater contamination

NASA Astrophysics Data System (ADS)

Baram, S.; Ronen, Z.; Kurtzman, D.; Peeters, A.; Dahan, O.

2013-12-01

Land cultivation and dairy waste lagoons are considered to be nonpoint and point sources of groundwater contamination by chloride (Cl-) and nitrate (NO3-). The objective of this work is to introduce a methodology to assess the past and future impacts of such agricultural activities on regional groundwater quality. The method is based on mass balances and on spatial statistical analysis of Cl- and NO3-concentration distributions in the saturated and unsaturated zones. The method enables quantitative analysis of the relation between the locations of pollution point sources and the spatial variability in Cl- and NO3- concentrations in groundwater. The method was applied to the Beer-Tuvia region, Israel, where intensive dairy farming along with land cultivation has been practiced for over 50 years above the local phreatic aquifer. Mass balance calculations accounted for the various groundwater recharge and abstraction sources and sinks in the entire region. The mass balances showed that leachates from lagoons and the cultivated land have contributed 6.0 and 89.4 % of the total mass of Cl- added to the aquifer and 12.6 and 77.4 % of the total mass of NO3-. The chemical composition of the aquifer and vadose zone water suggested that irrigated agricultural activity in the region is the main contributor of Cl- and NO3- to the groundwater. A low spatial correlation between the Cl- and NO3- concentrations in the groundwater and the on-land location of the dairy farms strengthened this assumption, despite the dairy waste lagoon being a point source for groundwater contamination by Cl- and NO3-. Results demonstrate that analyzing vadose zone and groundwater data by spatial statistical analysis methods can significantly contribute to the understanding of the relations between groundwater contaminating sources, and to assessing appropriate remediation steps.

The statistical properties of vortex flows in the solar atmosphere

NASA Astrophysics Data System (ADS)

Wedemeyer, Sven; Kato, Yoshiaki; Steiner, Oskar

2015-08-01

Rotating magnetic field structures associated with vortex flows on the Sun, also known as “magnetic tornadoes”, may serve as waveguides for MHD waves and transport mass and energy upwards through the atmosphere. Magnetic tornadoes may therefore potentially contribute to the heating of the upper atmospheric layers in quiet Sun regions.Magnetic tornadoes are observed over a large range of spatial and temporal scales in different layers in quiet Sun regions. However, their statistical properties such as size, lifetime, and rotation speed are not well understood yet because observations of these small-scale events are technically challenging and limited by the spatial and temporal resolution of current instruments. Better statistics based on a combination of high-resolution observations and state-of-the-art numerical simulations is the key to a reliable estimate of the energy input in the lower layers and of the energy deposition in the upper layers. For this purpose, we have developed a fast and reliable tool for the determination and visualization of the flow field in (observed) image sequences. This technique, which combines local correlation tracking (LCT) and line integral convolution (LIC), facilitates the detection and study of dynamic events on small scales, such as propagating waves. Here, we present statistical properties of vortex flows in different layers of the solar atmosphere and try to give realistic estimates of the energy flux which is potentially available for heating of the upper solar atmosphere

Fractal properties of background noise and target signal enhancement using CSEM data

NASA Astrophysics Data System (ADS)

Benavides, Alfonso; Everett, Mark E.; Pierce, Carl; Nguyen, Cam

2003-09-01

Controlled-source electromagnetic (CSEM) spatial profiles and 2-D conductivity maps were obtained on the Brazos Valley, TX floodplain to study the fractal statistics of geological signals and effects of man-made conductive targets using Geonics EM34, EM31 and EM63. Using target-free areas, a consistent power-law power spectrum (|A(k)| ~ k ^-β) for the profiles was found with β values typical of fractional Brownian motion (fBm). This means that the spatial variation of conductivity does not correspond to Gaussian statistics, where there are spatial correlations at different scales. The presence of targets tends to flatten the power-law power spectrum (PS) at small wavenumbers. Detection and localization of targets can be achieved using short-time Fourier transform (STFT). The presence of targets is enhanced because the signal energy is spread to higher wavenumbers (small scale numbers) in the positions occupied by the targets. In the case of poor spatial sampling or small amount of data, the information available from the power spectrum is not enough to separate spatial correlations from target signatures. Advantages are gained by using the spatial correlations of the fBm in order to reject the background response, and to enhance the signals from highly conductive targets. This approach was tested for the EM31 using a pre-processing step that combines apparent conductivity readings from two perpendicular transmitter-receiver orientations at each station. The response obtained using time-domain CSEM is influence to a lesser degree by geological noise and the target response can be processed to recover target features. The homotopy method is proposed to solve the inverse problem using a set of possible target models and a dynamic library of responses used to optimize the starting model.

Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: Evidence from the Phoenix metropolitan region

NASA Astrophysics Data System (ADS)

Fan, Chao; Myint, Soe W.; Rey, Sergio J.; Li, Wenwen

2017-06-01

Urbanization is a natural and social process involving simultaneous changes to the Earth's land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns.

PubMed

Norred, S Elizabeth; Caveney, Patrick M; Chauhan, Gaurav; Collier, Lauren K; Collier, C Patrick; Abel, Steven M; Simpson, Michael L

2018-05-18

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting-the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increase in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA ("spatial noise") that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. These results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.

Spatial Analysis of PAHs in Soils along an Urban-Suburban-Rural Gradient: scale effect, distribution patterns, diffusion and influencing factors

NASA Astrophysics Data System (ADS)

Peng, Chi; Wang, Meie; Chen, Weiping

2016-11-01

Spatial statistical methods including Cokriging interpolation, Morans I analysis, and geographically weighted regression (GWR) were used for studying the spatial characteristics of polycyclic aromatic hydrocarbon (PAH) accumulation in urban, suburban, and rural soils of Beijing. The concentrations of PAHs decreased spatially as the level of urbanization decreased. Generally, PAHs in soil showed two spatial patterns on the regional scale: (1) regional baseline depositions with a radius of 16.5 km related to the level of urbanization and (2) isolated pockets of soil contaminated with PAHs were found up to around 3.5 km from industrial point sources. In the urban areas, soil PAHs showed high spatial heterogeneity on the block scale, which was probably related to vegetation cover, land use, and physical soil disturbance. The distribution of total PAHs in urban blocks was unrelated to the indicators of the intensity of anthropogenic activity, namely population density, light intensity at night, and road density, but was significantly related to the same indicators in the suburban and rural areas. The moving averages of molecular ratios suggested that PAHs in the suburban and rural soils were a mix of local emissions and diffusion from urban areas.

Scale Dependence of Statistics of Spatially Averaged Rain Rate Seen in TOGA COARE Comparison with Predictions from a Stochastic Model

NASA Technical Reports Server (NTRS)

Kundu, Prasun K.; Bell, T. L.; Lau, William K. M. (Technical Monitor)

2002-01-01

A characteristic feature of rainfall statistics is that they in general depend on the space and time scales over which rain data are averaged. As a part of an earlier effort to determine the sampling error of satellite rain averages, a space-time model of rainfall statistics was developed to describe the statistics of gridded rain observed in GATE. The model allows one to compute the second moment statistics of space- and time-averaged rain rate which can be fitted to satellite or rain gauge data to determine the four model parameters appearing in the precipitation spectrum - an overall strength parameter, a characteristic length separating the long and short wavelength regimes and a characteristic relaxation time for decay of the autocorrelation of the instantaneous local rain rate and a certain 'fractal' power law exponent. For area-averaged instantaneous rain rate, this exponent governs the power law dependence of these statistics on the averaging length scale $L$ predicted by the model in the limit of small $L$. In particular, the variance of rain rate averaged over an $L \\times L$ area exhibits a power law singularity as $L \\rightarrow 0$. In the present work the model is used to investigate how the statistics of area-averaged rain rate over the tropical Western Pacific measured with ship borne radar during TOGA COARE (Tropical Ocean Global Atmosphere Coupled Ocean Atmospheric Response Experiment) and gridded on a 2 km grid depends on the size of the spatial averaging scale. Good agreement is found between the data and predictions from the model over a wide range of averaging length scales.

Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan.

PubMed

Tsai, Pui-Jen; Teng, Hwa-Jen

2016-11-09

Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran's I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran's index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran's index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran's index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes (Ae. aegypti and Ae. albopictus) were significant in 348 townships in Taiwan. The results indicated that local Aedes mosquitoes do not contribute to the dengue incidence of imported cases. The density of Ae. aegypti positively correlated with the density of human population. By contrast, the density of Ae. albopictus negatively correlated with the density of human population in the areas of southern Taiwan. The results indicated that Ae. aegypti has more opportunities for human-mosquito contact in dengue endemic areas in southern Taiwan. Ae. aegypti, but not Ae. albopictus, and human population density in southern Taiwan are closely associated with an increased risk of autochthonous dengue incidence.

Spatial variations in the incidence of breast cancer and potential risks associated with soil dioxin contamination in Midland, Saginaw, and Bay Counties, Michigan, USA

PubMed Central

Dai, Dajun; Oyana, Tonny J

2008-01-01

Background High levels of dioxins in soil and higher-than-average body burdens of dioxins in local residents have been found in the city of Midland and the Tittabawassee River floodplain in Michigan. The objective of this study is threefold: (1) to evaluate dioxin levels in soils; (2) to evaluate the spatial variations in breast cancer incidence in Midland, Saginaw, and Bay Counties in Michigan; (3) to evaluate whether breast cancer rates are spatially associated with the dioxin contamination areas. Methods We acquired 532 published soil dioxin data samples collected from 1995 to 2003 and data pertaining to female breast cancer cases (n = 4,604) at ZIP code level in Midland, Saginaw, and Bay Counties for years 1985 through 2002. Descriptive statistics and self-organizing map algorithm were used to evaluate dioxin levels in soils. Geographic information systems techniques, the Kulldorff's spatial and space-time scan statistics, and genetic algorithms were used to explore the variation in the incidence of breast cancer in space and space-time. Odds ratio and their corresponding 95% confidence intervals, with adjustment for age, were used to investigate a spatial association between breast cancer incidence and soil dioxin contamination. Results High levels of dioxin in soils were observed in the city of Midland and the Tittabawassee River 100-year floodplain. After adjusting for age, we observed high breast cancer incidence rates and detected the presence of spatial clusters in the city of Midland, the confluence area of the Tittabawassee, and Saginaw Rivers. After accounting for spatiotemporal variations, we observed a spatial cluster of breast cancer incidence in Midland between 1985 and 1993. The odds ratio further suggests a statistically significant (α = 0.05) increased breast cancer rate as women get older, and a higher disease burden in Midland and the surrounding areas in close proximity to the dioxin contaminated areas. Conclusion These findings suggest that increased breast cancer incidences are spatially associated with soil dioxin contamination. Aging is a substantial factor in the development of breast cancer. Findings can be used for heightened surveillance and education, as well as formulating new study hypotheses for further research. PMID:18939976

Improving Student Understanding of Spatial Ecology Statistics

ERIC Educational Resources Information Center

Hopkins, Robert, II; Alberts, Halley

2015-01-01

This activity is designed as a primer to teaching population dispersion analysis. The aim is to help improve students' spatial thinking and their understanding of how spatial statistic equations work. Students use simulated data to develop their own statistic and apply that equation to experimental behavioral data for Gambusia affinis (western…

Features of the energy structure of acoustic fields in the ocean with two-dimensional random inhomogeneities

NASA Astrophysics Data System (ADS)

Gulin, O. E.; Yaroshchuk, I. O.

2017-03-01

The paper is devoted to the analytic study and numerical simulation of mid-frequency acoustic signal propagation in a two-dimensional inhomogeneous random shallow-water medium. The study was carried out by the cross section method (local modes). We present original theoretical estimates for the behavior of the average acoustic field intensity and show that at different distances, the features of propagation loss behavior are determined by the intensity of fluctuations and their horizontal scale and depend on the initial regular parameters, such as the emission frequency and size of sound losses in the bottom. We establish analytically that for the considered waveguide and sound frequency parameters, mode coupling effect has a local character and weakly influences the statistics. We establish that the specific form of the spatial spectrum of sound velocity inhomogeneities for the statistical patterns of the field intensity is insignificant during observations in the range of shallow-water distances of practical interest.

Efficient and Flexible Climate Analysis with Python in a Cloud-Based Distributed Computing Framework

NASA Astrophysics Data System (ADS)

Gannon, C.

2017-12-01

As climate models become progressively more advanced, and spatial resolution further improved through various downscaling projects, climate projections at a local level are increasingly insightful and valuable. However, the raw size of climate datasets presents numerous hurdles for analysts wishing to develop customized climate risk metrics or perform site-specific statistical analysis. Four Twenty Seven, a climate risk consultancy, has implemented a Python-based distributed framework to analyze large climate datasets in the cloud. With the freedom afforded by efficiently processing these datasets, we are able to customize and continually develop new climate risk metrics using the most up-to-date data. Here we outline our process for using Python packages such as XArray and Dask to evaluate netCDF files in a distributed framework, StarCluster to operate in a cluster-computing environment, cloud computing services to access publicly hosted datasets, and how this setup is particularly valuable for generating climate change indicators and performing localized statistical analysis.

Differentiating induced and natural seismicity using space-time-magnitude statistics applied to the Coso Geothermal field

USGS Publications Warehouse

Schoenball, Martin; Davatzes, Nicholas C.; Glen, Jonathan M. G.

2015-01-01

A remarkable characteristic of earthquakes is their clustering in time and space, displaying their self-similarity. It remains to be tested if natural and induced earthquakes share the same behavior. We study natural and induced earthquakes comparatively in the same tectonic setting at the Coso Geothermal Field. Covering the preproduction and coproduction periods from 1981 to 2013, we analyze interevent times, spatial dimension, and frequency-size distributions for natural and induced earthquakes. Individually, these distributions are statistically indistinguishable. Determining the distribution of nearest neighbor distances in a combined space-time-magnitude metric, lets us identify clear differences between both kinds of seismicity. Compared to natural earthquakes, induced earthquakes feature a larger population of background seismicity and nearest neighbors at large magnitude rescaled times and small magnitude rescaled distances. Local stress perturbations induced by field operations appear to be strong enough to drive local faults through several seismic cycles and reactivate them after time periods on the order of a year.

Parasol cell mosaics are unlikely to drive the formation of structured orientation maps in primary visual cortex.

PubMed

Hore, Victoria R A; Troy, John B; Eglen, Stephen J

2012-11-01

The receptive fields of on- and off-center parasol cell mosaics independently tile the retina to ensure efficient sampling of visual space. A recent theoretical model represented the on- and off-center mosaics by noisy hexagonal lattices of slightly different density. When the two lattices are overlaid, long-range Moiré interference patterns are generated. These Moiré interference patterns have been suggested to drive the formation of highly structured orientation maps in visual cortex. Here, we show that noisy hexagonal lattices do not capture the spatial statistics of parasol cell mosaics. An alternative model based upon local exclusion zones, termed as the pairwise interaction point process (PIPP) model, generates patterns that are statistically indistinguishable from parasol cell mosaics. A key difference between the PIPP model and the hexagonal lattice model is that the PIPP model does not generate Moiré interference patterns, and hence stimulated orientation maps do not show any hexagonal structure. Finally, we estimate the spatial extent of spatial correlations in parasol cell mosaics to be only 200-350 μm, far less than that required to generate Moiré interference. We conclude that parasol cell mosaics are too disordered to drive the formation of highly structured orientation maps in visual cortex.

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

R and Spatial Data

EPA Science Inventory

R is an open source language and environment for statistical computing and graphics that can also be used for both spatial analysis (i.e. geoprocessing and mapping of different types of spatial data) and spatial data analysis (i.e. the application of statistical descriptions and ...

Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System

PubMed Central

Fischer, Brian J.; Peña, Jose L.

2016-01-01

Optimal use of sensory information requires that the brain estimates the reliability of sensory cues, but the neural correlate of cue reliability relevant for behavior is not well defined. Here, we addressed this issue by examining how the reliability of spatial cue influences neuronal responses and behavior in the owl's auditory system. We show that the firing rate and spatial selectivity changed with cue reliability due to the mechanisms generating the tuning to the sound localization cue. We found that the correlated variability among neurons strongly depended on the shape of the tuning curves. Finally, we demonstrated that the change in the neurons' selectivity was necessary and sufficient for a network of stochastic neurons to predict behavior when sensory cues were corrupted with noise. This study demonstrates that the shape of tuning curves can stand alone as a coding dimension of environmental statistics. SIGNIFICANCE STATEMENT In natural environments, sensory cues are often corrupted by noise and are therefore unreliable. To make the best decisions, the brain must estimate the degree to which a cue can be trusted. The behaviorally relevant neural correlates of cue reliability are debated. In this study, we used the barn owl's sound localization system to address this question. We demonstrated that the mechanisms that account for spatial selectivity also explained how neural responses changed with degraded signals. This allowed for the neurons' selectivity to capture cue reliability, influencing the population readout commanding the owl's sound-orienting behavior. PMID:26888922

Understanding the scatter in the spatially resolved star formation main sequence of local massive spiral galaxies

NASA Astrophysics Data System (ADS)

Abdurro'uf; Akiyama, Masayuki

2017-08-01

We investigate the relation between star formation rate (SFR) and stellar mass (M*) at the sub-galactic scale (˜1 kpc) of 93 local (0.01 < z < 0.02) massive (M* > 1010.5 M⊙) spiral galaxies. To derive a spatially resolved SFR and stellar mass, we perform the so-called pixel-to-pixel spectral energy distribution (SED) fitting, which fits an observed spatially resolved multiband SED with a library of model SEDs using Bayesian statistics. We use two bands (far-ultraviolet or FUV and near-ultraviolet or NUV) and five bands (u, g, r, I and z) of imaging data from Galaxy Evolution Explorer (GALEX) and Sloan Digital Sky Survey (SDSS), respectively. We find a tight nearly linear relation between the local surface density of SFR (ΣSFR) and stellar mass (Σ*), which is flattened at high Σ*. The near linear relation between Σ* and ΣSFR suggests a constant specific SFR (sSFR) throughout the galaxies, and the scatter of the relation is directly related to that of the sSFR. Therefore, we analyse the variation of the sSFR in various scales. More massive galaxies on average have lower sSFR throughout them than less massive galaxies. We also find that barred galaxies have a lower sSFR in the core region than non-barred galaxies. However, in the outer region, the sSFRs of barred and non-barred galaxies are similar and lead to a similar total sSFR.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Detection and Attribution of Temperature Trends in the Presence of Natural Variability

NASA Astrophysics Data System (ADS)

Wallace, J. M.

2014-12-01

The fingerprint of human-induced global warming stands out clearly above the noise In the time series of global-mean temperature, but not local temperature. At extratropical latitudes over land the standard error of 50-year linear temperature trends at a fixed point is as large as the cumulative rise in global-mean temperature over the past century. Much of the samping variability in local temperature trends is "dynamically-induced", i.e., attributable to the fact that the seasonally-varying mean circulation varies substantially from one year to the next and anomalous circulation patterns are generally accompanied by anomalous temperature patterns. In the presence of such large sampling variability it is virtually impossible to identify the spatial signature of greenhouse warming based on observational data or to partition observed local temperature trends into natural and human-induced components. It follows that previous IPCC assessments, which have focused on the deterministic signature of human-induced climate change, are inherently limited as to what they can tell us about the attribution of the past record of local temperature change or about how much the temperature at a particular place is likely to rise in the next few decades in response to global warming. To obtain more informative assessments of regional and local climate variability and change it will be necessary to take a probabilistic approach. Just as the use of the ensembles has contributed to more informative extended range weather predictions, large ensembles of climate model simulations can provide a statistical context for interpreting observed climate change and for framing projections of future climate. For some purposes, statistics relating to the interannual variability in the historical record can serve as a surrogate for statistics relating to the diversity of climate change scenarios in large ensembles.

Statistical analysis of catalogs of extragalactic objects. II - The Abell catalog of rich clusters

NASA Technical Reports Server (NTRS)

Hauser, M. G.; Peebles, P. J. E.

1973-01-01

The results of a power-spectrum analysis are presented for the distribution of clusters in the Abell catalog. Clear and direct evidence is found for superclusters with small angular scale, in agreement with the recent study of Bogart and Wagoner (1973). It is also found that the degree and angular scale of the apparent superclustering varies with distance in the manner expected if the clustering is intrinsic to the spatial distribution rather than a consequence of patchy local obscuration.

Investigating of spatial variations of PM2.5 concentration in Suzhou using remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhang, Shanzheng; Li, Bailiang

2017-04-01

Suzhou is located at the center of Yangtze Delta, suffering the air pollution from construction of mega city, industrial emission and traffic development. Particulate matter not greater than 2.5 micrometers (PM2.5) is now considered as the most important pollutants in the air in East China. For Suzhou city, some studies on PM2.5 temporal variations based on ground measurements have been conducted. However, until now, there is limited remote sensing based research to investigate the spatial pattern of PM2.5 in Suzhou. MODIS is often used to evaluate the spatial variabiilty of air quality, however, due to its low spatial resolution (250m), we have adopted China launched HJ-1 satellite with 30 m resolution of CCD sensor. Following the solar radiation S6 model and dark object atmospheric correction method (Kaufman,et al., 2000), atmospheric optical depth (AOD) was estimated. A statistical relationship has been built up between AOD and PM2.5. We have retrieved the spatial distribution of PM2.5 across Suzhou city in the winter of 2014. Results indicate that PM2.5 has the highest value in Kunshan (East of Suzhou) and Changshu and Taicang (NE of Suzhou) due to the heavy-polluted industry, while in the island of the Taihu Lake, the PM2.5 is significantly lower than other places maybe because of high deposition rate of PM2.5 over water and forest surfaces. The spatial variation also shows that traffic has less contribution to the PM2.5 generation than the industry. We believe this study will be very useful to identify the causes of local PM2.5 pollution. The findings could also benefit local management and policy making.

A simple stochastic rainstorm generator for simulating spatially and temporally varying rainfall

NASA Astrophysics Data System (ADS)

Singer, M. B.; Michaelides, K.; Nichols, M.; Nearing, M. A.

2016-12-01

In semi-arid to arid drainage basins, rainstorms often control both water supply and flood risk to marginal communities of people. They also govern the availability of water to vegetation and other ecological communities, as well as spatial patterns of sediment, nutrient, and contaminant transport and deposition on local to basin scales. All of these landscape responses are sensitive to changes in climate that are projected to occur throughout western North America. Thus, it is important to improve characterization of rainstorms in a manner that enables statistical assessment of rainfall at spatial scales below that of existing gauging networks and the prediction of plausible manifestations of climate change. Here we present a simple, stochastic rainstorm generator that was created using data from a rich and dense network of rain gauges at the Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but which is applicable anywhere. We describe our methods for assembling pdfs of relevant rainstorm characteristics including total annual rainfall, storm area, storm center location, and storm duration. We also generate five fitted intensity-duration curves and apply a spatial rainfall gradient to generate precipitation at spatial scales below gauge spacing. The model then runs by Monte Carlo simulation in which a total annual rainfall is selected before we generate rainstorms until the annual precipitation total is reached. The procedure continues for decadal simulations. Thus, we keep track of the hydrologic impact of individual storms and the integral of precipitation over multiple decades. We first test the model using ensemble predictions until we reach statistical similarity to the input data from WGEW. We then employ the model to assess decadal precipitation under simulations of climate change in which we separately vary the distribution of total annual rainfall (trend in moisture) and the intensity-duration curves used for simulation (trends in storminess). We demonstrate the model output through spatial maps of rainfall and through statistical comparisons of relevant parameters and distributions. Finally, discuss how the model can be used to understand basin-scale hydrology in terms of soil moisture, runoff, and erosion.

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.

Lateral Entorhinal Cortex Lesions Impair Local Spatial Frameworks

PubMed Central

Kuruvilla, Maneesh V.; Ainge, James A.

2017-01-01

A prominent theory in the neurobiology of memory processing is that episodic memory is supported by contextually gated spatial representations in the hippocampus formed by combining spatial information from medial entorhinal cortex (MEC) with non-spatial information from lateral entorhinal cortex (LEC). However, there is a growing body of evidence from lesion and single-unit recording studies in rodents suggesting that LEC might have a role in encoding space, particularly the current and previous locations of objects within the local environment. Landmarks, both local and global, have been shown to control the spatial representations hypothesized to underlie cognitive maps. Consequently, it has recently been suggested that information processing within this network might be organized with reference to spatial scale with LEC and MEC providing information about local and global spatial frameworks respectively. In the present study, we trained animals to search for food using either a local or global spatial framework. Animals were re-tested on both tasks after receiving excitotoxic lesions of either the MEC or LEC. LEC lesioned animals were impaired in their ability to learn a local spatial framework task. LEC lesioned animals were also impaired on an object recognition (OR) task involving multiple local features but unimpaired at recognizing a single familiar object. Together, this suggests that LEC is involved in associating features of the local environment. However, neither LEC nor MEC lesions impaired performance on the global spatial framework task. PMID:28567006

Localized Multi-Model Extremes Metrics for the Fourth National Climate Assessment

NASA Astrophysics Data System (ADS)

Thompson, T. R.; Kunkel, K.; Stevens, L. E.; Easterling, D. R.; Biard, J.; Sun, L.

2017-12-01

We have performed localized analysis of scenario-based datasets for the Fourth National Climate Assessment (NCA4). These datasets include CMIP5-based Localized Constructed Analogs (LOCA) downscaled simulations at daily temporal resolution and 1/16th-degree spatial resolution. Over 45 temperature and precipitation extremes metrics have been processed using LOCA data, including threshold, percentile, and degree-days calculations. The localized analysis calculates trends in the temperature and precipitation extremes metrics for relatively small regions such as counties, metropolitan areas, climate zones, administrative areas, or economic zones. For NCA4, we are currently addressing metropolitan areas as defined by U.S. Census Bureau Metropolitan Statistical Areas. Such localized analysis provides essential information for adaptation planning at scales relevant to local planning agencies and businesses. Nearly 30 such regions have been analyzed to date. Each locale is defined by a closed polygon that is used to extract LOCA-based extremes metrics specific to the area. For each metric, single-model data at each LOCA grid location are first averaged over several 30-year historical and future periods. Then, for each metric, the spatial average across the region is calculated using model weights based on both model independence and reproducibility of current climate conditions. The range of single-model results is also captured on the same localized basis, and then combined with the weighted ensemble average for each region and each metric. For example, Boston-area cooling degree days and maximum daily temperature is shown below for RCP8.5 (red) and RCP4.5 (blue) scenarios. We also discuss inter-regional comparison of these metrics, as well as their relevance to risk analysis for adaptation planning.

Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery.

PubMed

Soti, Valérie; Chevalier, Véronique; Maura, Jonathan; Bégué, Agnès; Lelong, Camille; Lancelot, Renaud; Thiongane, Yaya; Tran, Annelise

2013-03-01

Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (p<0.001), suggesting that the risk of RVF transmission was higher in the vicinity of ponds surrounded by a dense vegetation cover. The final risk map of RVF transmission displays a heterogeneous spatial distribution, corroborating previous findings from the same area. Our results highlight the potential of very high spatial resolution remote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.

Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery

PubMed Central

2013-01-01

Introduction Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. Methods In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Results Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (p<0.001), suggesting that the risk of RVF transmission was higher in the vicinity of ponds surrounded by a dense vegetation cover. The final risk map of RVF transmission displays a heterogeneous spatial distribution, corroborating previous findings from the same area. Conclusions Our results highlight the potential of very high spatial resolution remote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale. PMID:23452759

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs.

PubMed

Ingber, Lester; Nunez, Paul L

2011-02-01

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model. Copyright © 2010 Elsevier Inc. All rights reserved.

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime.

PubMed

Fitterer, Jessica L; Nelson, Trisalyn A

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks).

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime

PubMed Central

Fitterer, Jessica L.; Nelson, Trisalyn A.

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks). PMID:26418016

Regional-scale analysis of extreme precipitation from short and fragmented records

NASA Astrophysics Data System (ADS)

Libertino, Andrea; Allamano, Paola; Laio, Francesco; Claps, Pierluigi

2018-02-01

Rain gauge is the oldest and most accurate instrument for rainfall measurement, able to provide long series of reliable data. However, rain gauge records are often plagued by gaps, spatio-temporal discontinuities and inhomogeneities that could affect their suitability for a statistical assessment of the characteristics of extreme rainfall. Furthermore, the need to discard the shorter series for obtaining robust estimates leads to ignore a significant amount of information which can be essential, especially when large return periods estimates are sought. This work describes a robust statistical framework for dealing with uneven and fragmented rainfall records on a regional spatial domain. The proposed technique, named "patched kriging" allows one to exploit all the information available from the recorded series, independently of their length, to provide extreme rainfall estimates in ungauged areas. The methodology involves the sequential application of the ordinary kriging equations, producing a homogeneous dataset of synthetic series with uniform lengths. In this way, the errors inherent to any regional statistical estimation can be easily represented in the spatial domain and, possibly, corrected. Furthermore, the homogeneity of the obtained series, provides robustness toward local artefacts during the parameter-estimation phase. The application to a case study in the north-western Italy demonstrates the potential of the methodology and provides a significant base for discussing its advantages over previous techniques.

Point process models for localization and interdependence of punctate cellular structures.

PubMed

Li, Ying; Majarian, Timothy D; Naik, Armaghan W; Johnson, Gregory R; Murphy, Robert F

2016-07-01

Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Type-curve estimation of statistical heterogeneity

NASA Astrophysics Data System (ADS)

Neuman, Shlomo P.; Guadagnini, Alberto; Riva, Monica

2004-04-01

The analysis of pumping tests has traditionally relied on analytical solutions of groundwater flow equations in relatively simple domains, consisting of one or at most a few units having uniform hydraulic properties. Recently, attention has been shifting toward methods and solutions that would allow one to characterize subsurface heterogeneities in greater detail. On one hand, geostatistical inverse methods are being used to assess the spatial variability of parameters, such as permeability and porosity, on the basis of multiple cross-hole pressure interference tests. On the other hand, analytical solutions are being developed to describe the mean and variance (first and second statistical moments) of flow to a well in a randomly heterogeneous medium. We explore numerically the feasibility of using a simple graphical approach (without numerical inversion) to estimate the geometric mean, integral scale, and variance of local log transmissivity on the basis of quasi steady state head data when a randomly heterogeneous confined aquifer is pumped at a constant rate. By local log transmissivity we mean a function varying randomly over horizontal distances that are small in comparison with a characteristic spacing between pumping and observation wells during a test. Experimental evidence and hydrogeologic scaling theory suggest that such a function would tend to exhibit an integral scale well below the maximum well spacing. This is in contrast to equivalent transmissivities derived from pumping tests by treating the aquifer as being locally uniform (on the scale of each test), which tend to exhibit regional-scale spatial correlations. We show that whereas the mean and integral scale of local log transmissivity can be estimated reasonably well based on theoretical ensemble mean variations of head and drawdown with radial distance from a pumping well, estimating the log transmissivity variance is more difficult. We obtain reasonable estimates of the latter based on theoretical variation of the standard deviation of circumferentially averaged drawdown about its mean.

Structural white matter asymmetries in relation to functional asymmetries during speech perception and production.

PubMed

Ocklenburg, Sebastian; Hugdahl, Kenneth; Westerhausen, René

2013-12-01

Functional hemispheric asymmetries of speech production and perception are a key feature of the human language system, but their neurophysiological basis is still poorly understood. Using a combined fMRI and tract-based spatial statistics approach, we investigated the relation of microstructural asymmetries in language-relevant white matter pathways and functional activation asymmetries during silent verb generation and passive listening to spoken words. Tract-based spatial statistics revealed several leftward asymmetric clusters in the arcuate fasciculus and uncinate fasciculus that were differentially related to activation asymmetries in the two functional tasks. Frontal and temporal activation asymmetries during silent verb generation were positively related to the strength of specific microstructural white matter asymmetries in the arcuate fasciculus. In contrast, microstructural uncinate fasciculus asymmetries were related to temporal activation asymmetries during passive listening. These findings suggest that white matter asymmetries may indeed be one of the factors underlying functional hemispheric asymmetries. Moreover, they also show that specific localized white matter asymmetries might be of greater relevance for functional activation asymmetries than microstructural features of whole pathways. © 2013.

Spatial analyses for nonoverlapping objects with size variations and their application to coral communities.

PubMed

Muko, Soyoka; Shimatani, Ichiro K; Nozawa, Yoko

2014-07-01

Spatial distributions of individuals are conventionally analysed by representing objects as dimensionless points, in which spatial statistics are based on centre-to-centre distances. However, if organisms expand without overlapping and show size variations, such as is the case for encrusting corals, interobject spacing is crucial for spatial associations where interactions occur. We introduced new pairwise statistics using minimum distances between objects and demonstrated their utility when examining encrusting coral community data. We also calculated the conventional point process statistics and the grid-based statistics to clarify the advantages and limitations of each spatial statistical method. For simplicity, coral colonies were approximated by disks in these demonstrations. Focusing on short-distance effects, the use of minimum distances revealed that almost all coral genera were aggregated at a scale of 1-25 cm. However, when fragmented colonies (ramets) were treated as a genet, a genet-level analysis indicated weak or no aggregation, suggesting that most corals were randomly distributed and that fragmentation was the primary cause of colony aggregations. In contrast, point process statistics showed larger aggregation scales, presumably because centre-to-centre distances included both intercolony spacing and colony sizes (radius). The grid-based statistics were able to quantify the patch (aggregation) scale of colonies, but the scale was strongly affected by the colony size. Our approach quantitatively showed repulsive effects between an aggressive genus and a competitively weak genus, while the grid-based statistics (covariance function) also showed repulsion although the spatial scale indicated from the statistics was not directly interpretable in terms of ecological meaning. The use of minimum distances together with previously proposed spatial statistics helped us to extend our understanding of the spatial patterns of nonoverlapping objects that vary in size and the associated specific scales. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.

PubMed

Li, Ke; Garrett, John; Ge, Yongshuai; Chen, Guang-Hong

2014-07-01

Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDIvol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo(®), GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'. (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

Stable statistical representations facilitate visual search.

PubMed

Corbett, Jennifer E; Melcher, David

2014-10-01

Observers represent the average properties of object ensembles even when they cannot identify individual elements. To investigate the functional role of ensemble statistics, we examined how modulating statistical stability affects visual search. We varied the mean and/or individual sizes of an array of Gabor patches while observers searched for a tilted target. In "stable" blocks, the mean and/or local sizes of the Gabors were constant over successive displays, whereas in "unstable" baseline blocks they changed from trial to trial. Although there was no relationship between the context and the spatial location of the target, observers found targets faster (as indexed by faster correct responses and fewer saccades) as the global mean size became stable over several displays. Building statistical stability also facilitated scanning the scene, as measured by larger saccadic amplitudes, faster saccadic reaction times, and shorter fixation durations. These findings suggest a central role for peripheral visual information, creating context to free resources for detailed processing of salient targets and maintaining the illusion of visual stability.

Does the edge effect impact on the measure of spatial accessibility to healthcare providers?

PubMed

Gao, Fei; Kihal, Wahida; Le Meur, Nolwenn; Souris, Marc; Deguen, Séverine

2017-12-11

Spatial accessibility indices are increasingly applied when investigating inequalities in health. Although most studies are making mentions of potential errors caused by the edge effect, many acknowledge having neglected to consider this concern by establishing spatial analyses within a finite region, settling for hypothesizing that accessibility to facilities will be under-reported. Our study seeks to assess the effect of edge on the accuracy of defining healthcare provider access by comparing healthcare provider accessibility accounting or not for the edge effect, in a real-world application. This study was carried out in the department of Nord, France. The statistical unit we use is the French census block known as 'IRIS' (Ilot Regroupé pour l'Information Statistique), defined by the National Institute of Statistics and Economic Studies. The geographical accessibility indicator used is the "Index of Spatial Accessibility" (ISA), based on the E2SFCA algorithm. We calculated ISA for the pregnant women population by selecting three types of healthcare providers: general practitioners, gynecologists and midwives. We compared ISA variation when accounting or not edge effect in urban and rural zones. The GIS method was then employed to determine global and local autocorrelation. Lastly, we compared the relationship between socioeconomic distress index and ISA, when accounting or not for the edge effect, to fully evaluate its impact. The results revealed that on average ISA when offer and demand beyond the boundary were included is slightly below ISA when not accounting for the edge effect, and we found that the IRIS value was more likely to deteriorate than improve. Moreover, edge effect impact can vary widely by health provider type. There is greater variability within the rural IRIS group than within the urban IRIS group. We found a positive correlation between socioeconomic distress variables and composite ISA. Spatial analysis results (such as Moran's spatial autocorrelation index and local indicators of spatial autocorrelation) are not really impacted. Our research has revealed minor accessibility variation when edge effect has been considered in a French context. No general statement can be set up because intensity of impact varies according to healthcare provider type, territorial organization and methodology used to measure the accessibility to healthcare. Additional researches are required in order to distinguish what findings are specific to a territory and others common to different countries. It constitute a promising direction to determine more precisely healthcare shortage areas and then to fight against social health inequalities.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic. PMID:20642827

Adverse effects of metallic artifacts on voxel-wise analysis and tract-based spatial statistics in diffusion tensor imaging.

PubMed

Goto, Masami; Abe, Osamu; Hata, Junichi; Fukunaga, Issei; Shimoji, Keigo; Kunimatsu, Akira; Gomi, Tsutomu

2017-02-01

Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that reflects the Brownian motion of water molecules constrained within brain tissue. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, and can be applied to quantitative analysis of white matter as tract-based spatial statistics (TBSS) and voxel-wise analysis. Purpose To show an association between metallic implants and the results of statistical analysis (voxel-wise group comparison and TBSS) for fractional anisotropy (FA) mapping, in DTI of healthy adults. Material and Methods Sixteen healthy volunteers were scanned with 3-Tesla MRI. A magnetic keeper type of dental implant was used as the metallic implant. DTI was acquired three times in each participant: (i) without a magnetic keeper (FAnon1); (ii) with a magnetic keeper (FAimp); and (iii) without a magnetic keeper (FAnon2) as reproducibility of FAnon1. Group comparisons with paired t-test were performed as FAnon1 vs. FAnon2, and as FAnon1 vs. FAimp. Results Regions of significantly reduced and increased local FA values were revealed by voxel-wise group comparison analysis (a P value of less than 0.05, corrected with family-wise error), but not by TBSS. Conclusion Metallic implants existing outside the field of view produce artifacts that affect the statistical analysis (voxel-wise group comparisons) for FA mapping. When statistical analysis for FA mapping is conducted by researchers, it is important to pay attention to any dental implants present in the mouths of the participants.

Development of hi-resolution regional climate scenarios in Japan by statistical downscaling

NASA Astrophysics Data System (ADS)

Dairaku, K.

2016-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. To meet with the needs of stakeholders such as local governments, a Japan national project, Social Implementation Program on Climate Change Adaptation Technology (SI-CAT), launched in December 2015. It develops reliable technologies for near-term climate change predictions. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 GCMs and a statistical downscaling method to support various municipal adaptation measures appropriate for possible regional climate changes. A statistical downscaling method, Bias Correction Spatial Disaggregation (BCSD), is employed to develop regional climate scenarios based on CMIP5 RCP8.5 five GCMs (MIROC5, MRI-CGCM3, GFDL-CM3, CSIRO-Mk3-6-0, HadGEM2-ES) for the periods of historical climate (1970-2005) and near future climate (2020-2055). Downscaled variables are monthly/daily precipitation and temperature. File format is NetCDF4 (conforming to CF1.6, HDF5 compression). Developed regional climate scenarios will be expanded to meet with needs of stakeholders and interface applications to access and download the data are under developing. Statistical downscaling method is not necessary to well represent locally forced nonlinear phenomena, extreme events such as heavy rain, heavy snow, etc. To complement the statistical method, dynamical downscaling approach is also combined and applied to some specific regions which have needs of stakeholders. The added values of statistical/dynamical downscaling methods compared with parent GCMs are investigated.

Non-local Second Order Closure Scheme for Boundary Layer Turbulence and Convection

NASA Astrophysics Data System (ADS)

Meyer, Bettina; Schneider, Tapio

2017-04-01

There has been scientific consensus that the uncertainty in the cloud feedback remains the largest source of uncertainty in the prediction of climate parameters like climate sensitivity. To narrow down this uncertainty, not only a better physical understanding of cloud and boundary layer processes is required, but specifically the representation of boundary layer processes in models has to be improved. General climate models use separate parameterisation schemes to model the different boundary layer processes like small-scale turbulence, shallow and deep convection. Small scale turbulence is usually modelled by local diffusive parameterisation schemes, which truncate the hierarchy of moment equations at first order and use second-order equations only to estimate closure parameters. In contrast, the representation of convection requires higher order statistical moments to capture their more complex structure, such as narrow updrafts in a quasi-steady environment. Truncations of moment equations at second order may lead to more accurate parameterizations. At the same time, they offer an opportunity to take spatially correlated structures (e.g., plumes) into account, which are known to be important for convective dynamics. In this project, we study the potential and limits of local and non-local second order closure schemes. A truncation of the momentum equations at second order represents the same dynamics as a quasi-linear version of the equations of motion. We study the three-dimensional quasi-linear dynamics in dry and moist convection by implementing it in a LES model (PyCLES) and compare it to a fully non-linear LES. In the quasi-linear LES, interactions among turbulent eddies are suppressed but nonlinear eddy—mean flow interactions are retained, as they are in the second order closure. In physical terms, suppressing eddy—eddy interactions amounts to suppressing, e.g., interactions among convective plumes, while retaining interactions between plumes and the environment (e.g., entrainment and detrainment). In a second part, we employ the possibility to include non-local statistical correlations in a second-order closure scheme. Such non-local correlations allow to directly incorporate the spatially coherent structures that occur in the form of convective updrafts penetrating the boundary layer. This allows us to extend the work that has been done using assumed-PDF schemes for parameterising boundary layer turbulence and shallow convection in a non-local sense.

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

Graesser, Jordan B; Cheriyadat, Anil M; Vatsavai, Raju

The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be de ned as unplanned, unauthorized, and/or unstructured housing. Techniques for ef ciently mapping these settlement boundaries can bene t various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urbanmore » neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classi cation framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.« less

Statistical characterization of spatial patterns of rainfall cells in extratropical cyclones

NASA Astrophysics Data System (ADS)

Bacchi, Baldassare; Ranzi, Roberto; Borga, Marco

1996-11-01

The assumption of a particular type of distribution of rainfall cells in space is needed for the formulation of several space-time rainfall models. In this study, weather radar-derived rain rate maps are employed to evaluate different types of spatial organization of rainfall cells in storms through the use of distance functions and second-moment measures. In particular the spatial point patterns of the local maxima of rainfall intensity are compared to a completely spatially random (CSR) point process by applying an objective distance measure. For all the analyzed radar maps the CSR assumption is rejected, indicating that at the resolution of the observation considered, rainfall cells are clustered. Therefore a theoretical framework for evaluating and fitting alternative models to the CSR is needed. This paper shows how the "reduced second-moment measure" of the point pattern can be employed to estimate the parameters of a Neyman-Scott model and to evaluate the degree of adequacy to the experimental data. Some limitations of this theoretical framework, and also its effectiveness, in comparison to the use of scaling functions, are discussed.

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support ourmore » construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

Defining surfaces for skewed, highly variable data

USGS Publications Warehouse

Helsel, D.R.; Ryker, S.J.

2002-01-01

Skewness of environmental data is often caused by more than simply a handful of outliers in an otherwise normal distribution. Statistical procedures for such datasets must be sufficiently robust to deal with distributions that are strongly non-normal, containing both a large proportion of outliers and a skewed main body of data. In the field of water quality, skewness is commonly associated with large variation over short distances. Spatial analysis of such data generally requires either considerable effort at modeling or the use of robust procedures not strongly affected by skewness and local variability. Using a skewed dataset of 675 nitrate measurements in ground water, commonly used methods for defining a surface (least-squares regression and kriging) are compared to a more robust method (loess). Three choices are critical in defining a surface: (i) is the surface to be a central mean or median surface? (ii) is either a well-fitting transformation or a robust and scale-independent measure of center used? (iii) does local spatial autocorrelation assist in or detract from addressing objectives? Published in 2002 by John Wiley & Sons, Ltd.

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.

Evaluation of commercial available fusion algorithms for Geoeye data

NASA Astrophysics Data System (ADS)

Vaiopoulos, Aristides D.; Nikolakopoulos, Konstantinos G.

2013-10-01

In this study ten commercial available fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, PCA, HCS (Hyperspherical Color Space) and Wavelet were used for the fusion of Geoeye panchromatic and multispectral data. The panchromatic data have a spatial resolution of 0.5m while the multispectral data have a spatial resolution of 2.0m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q, entropy were examined and the results are presented. The broader area of Pendeli mountain near to the city of Athens Greece and more especially two sub areas with different characteristics were chosen for the comparison. The first sub area is located at the edge of the urban fabric and combines at the same time the characteristics of an urban and a rural area. The second sub area comprises a large open quarry and it is suitable to examine which fused product is more suitable for mine monitoring.

Scale-free correlations in the geographical spreading of obesity

NASA Astrophysics Data System (ADS)

Gallos, Lazaros; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernan

2012-02-01

Obesity levels have been universally increasing. A crucial problem is to determine the influence of global and local drivers behind the obesity epidemic, to properly guide effective policies. Despite the numerous factors that affect the obesity evolution, we show a remarkable regularity expressed in a predictable pattern of spatial long-range correlations in the geographical spreading of obesity. We study the spatial clustering of obesity and a number of related health and economic indicators, and we use statistical physics methods to characterize the growth of the resulting clusters. The resulting scaling exponents allow us to broadly classify these indicators into two separate universality classes, weakly or strongly correlated. Weak correlations are found in generic human activity such as population distribution and the growth of the whole economy. Strong correlations are recovered, among others, for obesity, diabetes, and the food industry sectors associated with food consumption. Obesity turns out to be a global problem where local details are of little importance. The long-range correlations suggest influence that extends to large scales, hinting that the physical model of obesity clustering can be mapped to a long-range correlated percolation process.

Local breast density assessment using reacquired mammographic images.

PubMed

García, Eloy; Diaz, Oliver; Martí, Robert; Diez, Yago; Gubern-Mérida, Albert; Sentís, Melcior; Martí, Joan; Oliver, Arnau

2017-08-01

The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast. We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures. Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution. This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

PubMed

Hjort, Jan; Hugg, Timo T; Antikainen, Harri; Rusanen, Jarmo; Sofiev, Mikhail; Kukkonen, Jaakko; Jaakkola, Maritta S; Jaakkola, Jouni J K

2016-05-01

Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

Prospects for AGN Science using the ART-XC on the SRG Mission

NASA Technical Reports Server (NTRS)

Swartz, Douglas A.; Elsner, Ronald F.; Gubarev, Mikhail V.; O'Dell, Stephen L.; Ramsey, Brian D.; Bonamente, Massimiliano

2012-01-01

The enhanced hard X-ray sensitivity provided by the Astronomical Roentgen Telescope to the Spectrum Roentgen Gamma mission facilitates the detection of heavily obscured and other hard-spectrum cosmic X-ray sources. The SRG all-sky survey will obtain large, statistically-well-defined samples of active galactic nuclei (AGN) including a significant population of local heavily-obscured AGN. In anticipation of the SRG all-sky survey, we investigate the prospects for refining the bright end of the AGN luminosity function and determination of the local black hole mass function and comparing the spatial distribution of AGN with large-scale structure defined by galaxy clusters and groups. Particular emphasis is placed on studies of the deep survey Ecliptic Pole regions.

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns

DOE PAGES

Norred, Sarah Elizabeth; Caveney, Patrick M.; Chauhan, Gaurav; ...

2018-04-24

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting—the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increasemore » in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA (“spatial noise”) that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. Furthermore, these results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.« less

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns

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

Norred, Sarah Elizabeth; Caveney, Patrick M.; Chauhan, Gaurav

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting—the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increasemore » in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA (“spatial noise”) that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. Furthermore, these results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.« less

Current and future pluvial flood hazard analysis for the city of Antwerp

NASA Astrophysics Data System (ADS)

Willems, Patrick; Tabari, Hossein; De Niel, Jan; Van Uytven, Els; Lambrechts, Griet; Wellens, Geert

2016-04-01

For the city of Antwerp in Belgium, higher rainfall extremes were observed in comparison with surrounding areas. The differences were found statistically significant for some areas and may be the result of the heat island effect in combination with the higher concentrations of aerosols. A network of 19 rain gauges but with varying records length (the longest since the 1960s) and continuous radar data for 10 years were combined to map the spatial variability of rainfall extremes over the city at various durations from 15 minutes to 1 day together with the uncertainty. The improved spatial rainfall information was used as input in the sewer system model of the city to analyze the frequency of urban pluvial floods. Comparison with historical flood observations from various sources (fire brigade and media) confirmed that the improved spatial rainfall information also improved sewer impact results on both the magnitude and frequency of the sewer floods. Next to these improved urban flood impact results for recent and current climatological conditions, the new insights on the local rainfall microclimate were also helpful to enhance future projections on rainfall extremes and pluvial floods in the city. This was done by improved statistical downscaling of all available CMIP5 global climate model runs (160 runs) for the 4 RCP scenarios, as well as the available EURO-CORDEX regional climate model runs. Two types of statistical downscaling methods were applied for that purpose (a weather typing based method, and a quantile perturbation approach), making use of the microclimate results and its dependency on specific weather types. Changes in extreme rainfall intensities were analyzed and mapped as a function of the RCP scenario, together with the uncertainty, decomposed in the uncertainties related to the climate models, the climate model initialization or limited length of the 30-year time series (natural climate variability) and the statistical downscaling (albeit limited to two types of methods). These were finally transferred into future pluvial flash flood hazard maps for the city together with the uncertainties, and are considered as basis for spatial planning and adaptation.

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.

Direct statistical modeling and its implications for predictive mapping in mining exploration

NASA Astrophysics Data System (ADS)

Sterligov, Boris; Gumiaux, Charles; Barbanson, Luc; Chen, Yan; Cassard, Daniel; Cherkasov, Sergey; Zolotaya, Ludmila

2010-05-01

Recent advances in geosciences make more and more multidisciplinary data available for mining exploration. This allowed developing methodologies for computing forecast ore maps from the statistical combination of such different input parameters, all based on an inverse problem theory. Numerous statistical methods (e.g. algebraic method, weight of evidence, Siris method, etc) with varying degrees of complexity in their development and implementation, have been proposed and/or adapted for ore geology purposes. In literature, such approaches are often presented through applications on natural examples and the results obtained can present specificities due to local characteristics. Moreover, though crucial for statistical computations, "minimum requirements" needed for input parameters (number of minimum data points, spatial distribution of objects, etc) are often only poorly expressed. From these, problems often arise when one has to choose between one and the other method for her/his specific question. In this study, a direct statistical modeling approach is developed in order to i) evaluate the constraints on the input parameters and ii) test the validity of different existing inversion methods. The approach particularly focused on the analysis of spatial relationships between location of points and various objects (e.g. polygons and /or polylines) which is particularly well adapted to constrain the influence of intrusive bodies - such as a granite - and faults or ductile shear-zones on spatial location of ore deposits (point objects). The method is designed in a way to insure a-dimensionality with respect to scale. In this approach, both spatial distribution and topology of objects (polygons and polylines) can be parametrized by the user (e.g. density of objects, length, surface, orientation, clustering). Then, the distance of points with respect to a given type of objects (polygons or polylines) is given using a probability distribution. The location of points is computed assuming either independency or different grades of dependency between the two probability distributions. The results show that i)polygons surface mean value, polylines length mean value, the number of objects and their clustering are critical and ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used. In addition, this combined approach of direct and inverse modeling offers an opportunity to test the robustness of the inferred distribution point laws with respect to the quality of the input data set.

Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System.

PubMed

Cazettes, Fanny; Fischer, Brian J; Peña, Jose L

2016-02-17

Optimal use of sensory information requires that the brain estimates the reliability of sensory cues, but the neural correlate of cue reliability relevant for behavior is not well defined. Here, we addressed this issue by examining how the reliability of spatial cue influences neuronal responses and behavior in the owl's auditory system. We show that the firing rate and spatial selectivity changed with cue reliability due to the mechanisms generating the tuning to the sound localization cue. We found that the correlated variability among neurons strongly depended on the shape of the tuning curves. Finally, we demonstrated that the change in the neurons' selectivity was necessary and sufficient for a network of stochastic neurons to predict behavior when sensory cues were corrupted with noise. This study demonstrates that the shape of tuning curves can stand alone as a coding dimension of environmental statistics. In natural environments, sensory cues are often corrupted by noise and are therefore unreliable. To make the best decisions, the brain must estimate the degree to which a cue can be trusted. The behaviorally relevant neural correlates of cue reliability are debated. In this study, we used the barn owl's sound localization system to address this question. We demonstrated that the mechanisms that account for spatial selectivity also explained how neural responses changed with degraded signals. This allowed for the neurons' selectivity to capture cue reliability, influencing the population readout commanding the owl's sound-orienting behavior. Copyright © 2016 the authors 0270-6474/16/362101-10$15.00/0.

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

NASA Astrophysics Data System (ADS)

Balthazar, Vincent; Vanacker, Veerle; Girma, Atkilt; Poesen, Jean; Golla, Semunesh

2013-01-01

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.

Magnetoresistance of an Anderson insulator of bosons.

PubMed

Gangopadhyay, Anirban; Galitski, Victor; Müller, Markus

2013-07-12

We study the magnetoresistance of two-dimensional bosonic Anderson insulators. We describe the change in spatial decay of localized excitations in response to a magnetic field, which is given by an interference sum over alternative tunneling trajectories. The excitations become more localized with increasing field (in sharp contrast to generic fermionic excitations which get weakly delocalized): the localization length ξ(B) is found to change as ξ(-1)(B)-ξ(-1)(0)~B(4/5). The quantum interference problem maps onto the classical statistical mechanics of directed polymers in random media (DPRM). We explain the observed scaling using a simplified droplet model which incorporates the nontrivial DPRM exponents. Our results have implications for a variety of experiments on magnetic-field-tuned superconductor-to-insulator transitions observed in disordered films, granular superconductors, and Josephson junction arrays, as well as for cold atoms in artificial gauge fields.

New quantitative approaches reveal the spatial preference of nuclear compartments in mammalian fibroblasts.

PubMed

Weston, David J; Russell, Richard A; Batty, Elizabeth; Jensen, Kirsten; Stephens, David A; Adams, Niall M; Freemont, Paul S

2015-03-06

The nuclei of higher eukaryotic cells display compartmentalization and certain nuclear compartments have been shown to follow a degree of spatial organization. To date, the study of nuclear organization has often involved simple quantitative procedures that struggle with both the irregularity of the nuclear boundary and the problem of handling replicate images. Such studies typically focus on inter-object distance, rather than spatial location within the nucleus. The concern of this paper is the spatial preference of nuclear compartments, for which we have developed statistical tools to quantitatively study and explore nuclear organization. These tools combine replicate images to generate 'aggregate maps' which represent the spatial preferences of nuclear compartments. We present two examples of different compartments in mammalian fibroblasts (WI-38 and MRC-5) that demonstrate new knowledge of spatial preference within the cell nucleus. Specifically, the spatial preference of RNA polymerase II is preserved across normal and immortalized cells, whereas PML nuclear bodies exhibit a change in spatial preference from avoiding the centre in normal cells to exhibiting a preference for the centre in immortalized cells. In addition, we show that SC35 splicing speckles are excluded from the nuclear boundary and localize throughout the nucleoplasm and in the interchromatin space in non-transformed WI-38 cells. This new methodology is thus able to reveal the effect of large-scale perturbation on spatial architecture and preferences that would not be obvious from single cell imaging.

LETTER TO THE EDITOR: Evidence for global mixing in real influenza epidemics

NASA Astrophysics Data System (ADS)

Bonabeau, Eric; Toubiana, Laurent; Flahault, Antoine

1998-05-01

The spatiotemporal behaviour of the spread of influenza in France has been studied, and algebraic spatial correlations (with exponent 0305-4470/31/19/001/img5) spanning the whole territory have been found to be present as soon as the number of reported cases begins to increase, about 15 - 25 weeks before the peak of the epidemic. This result is surprising, as one would expect long-range correlations, if any, only in the vicinity of the maximum incidence, whereas our observations suggest that there exists an underlying non-trivial spatial structure at the very beginning of the observed epidemic. The observed long-range correlations are in fact present in the spatial distribution of the population. Correlations in the number of cases normalized by local population density are characterized by 0305-4470/31/19/001/img6. This suggests that the spread of the epidemic is statistically uniform in space over a complex substrate that already contains the observed long-range correlations.

Detection of the relationship between peak temperature and extreme precipitation

NASA Astrophysics Data System (ADS)

Yu, Y.; Liu, J.; Zhiyong, Y.

2017-12-01

Under the background of climate change and human activities, the characteristics and pattern of precipitation have changed significantly in many regions. As the political and cultural center of China, the structure and character of precipitation in Jingjinji District has varied dramatically in recent years. In this paper, the daily precipitation data throughout the period 1960-2013 are selected for analyzing the spatial-temporal variability of precipitation. The results indicate that the frequency and intensity of precipitation presents an increasing trend. Based on the precipitation data, the maximum, minimum and mean precipitation in different temporal and spatial scales is calculated respectively. The temporal and spatial variation of temperature is obtained by using statistical methods. The relationship between temperature and precipitation in different range is analyzed. The curve relates daily precipitation extremes with local temperatures has a peak structure, increasing at the low-medium range of temperature variations but decreasing at high temperatures. The relationship between extreme precipitation is stronger in downtown than that in suburbs.

Visual Perception-Based Statistical Modeling of Complex Grain Image for Product Quality Monitoring and Supervision on Assembly Production Line

PubMed Central

Chen, Qing; Xu, Pengfei; Liu, Wenzhong

2016-01-01

Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel–least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method. PMID:26986726

Local short-term variability in solar irradiance

NASA Astrophysics Data System (ADS)

Lohmann, Gerald M.; Monahan, Adam H.; Heinemann, Detlev

2016-05-01

Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP)2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k* (i.e., irradiance normalized to clear-sky conditions) and sub-minute k* increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k* statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k* increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k* increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by , this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k* less effectively than variability in k* increments, for a spatial sensor density of 2 km-2.

An LES study on the spatial variability impact of surface sensible heat flux (SHF) on the convective boundary layer (CBL)

NASA Astrophysics Data System (ADS)

Kang, S. L.; Chun, J.; Kumar, A.

2015-12-01

We study the spatial variability impact of surface sensible heat flux (SHF) on the convective boundary layer (CBL), using the Weather Research and Forecasting (WRF) model in large eddy simulation (LES) mode. In order to investigate the response of the CBL to multi-scale feature of the surface SHF field over a local area of several tens of kilometers or smaller, an analytic surface SHF map is crated as a function of the chosen feature. The spatial variation in the SHF map is prescribed with a two-dimensional analytical perturbation field, which is generated by using the inverse transform technique of the Fourier series whose coefficients are controlled, of which spectrum to have a particular slope in the chosen range of wavelength. Then, the CBL responses to various SHF heterogeneities are summarized as a function of the spectral slope, in terms of mean structure, turbulence statistics and cross-scale processes. The range of feasible SHF heterogeneities is obtained from the SHF maps produced by a land surface model (LSM) of the WRF system. The LSM-derived SHF maps are a function of geographical data on various resolutions. Based on the numerical experiment results with the surface heterogeneities in the range, we will discuss the uncertainty in the SHF heterogeneity and its impact on the atmosphere in a numerical model. Also we will present the range of spatial scale of the surface SHF heterogeneity that significantly influence on the whole CBL. Lastly, we will report the test result of the hypothesis that the spatial variability of SHF is more representative of surface thermal heterogeneity than is the latent heat flux over the local area of several tens of kilometers or smaller.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Platnick, Steven; Schaaf, Crystal B.

2004-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which cutails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1'(approximately 10 km) and coarser resolution equal-angle grids, and are computed for the first seven MODIS wavelengths, ranging from 0.47 through 2.1 microns, and for three broadband wavelengths, 0.3-0.7,0.3-5.0 and 0.7-5.0 microns.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

NASA Astrophysics Data System (ADS)

Tsutsumi, Morito; Seya, Hajime

2009-12-01

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

Spatial distribution of limited resources and local density regulation in juvenile Atlantic salmon.

PubMed

Finstad, Anders G; Einum, Sigurd; Ugedal, Ola; Forseth, Torbjørn

2009-01-01

1. Spatial heterogeneity of resources may influence competition among individuals and thus have a fundamental role in shaping population dynamics and carrying capacity. In the present study, we identify shelter opportunities as a limiting resource for juvenile Atlantic salmon (Salmo salar L.). Experimental and field studies are combined in order to demonstrate how the spatial distribution of shelters may influence population dynamics on both within and among population scales. 2. In closed experimental streams, fish performance scaled negatively with decreasing shelter availability and increasing densities. In contrast, the fish in open stream channels dispersed according to shelter availability and performance of fish remaining in the streams did not depend on initial density or shelters. 3. The field study confirmed that spatial variation in densities of 1-year-old juveniles was governed both by initial recruit density and shelter availability. Strength of density-dependent population regulation, measured as carrying capacity, increased with decreasing number of shelters. 4. Nine rivers were surveyed for spatial variation in shelter availability and increased shelter heterogeneity tended to decrease maximum observed population size (measured using catch statistics of adult salmon as a proxy). 5. Our studies highlight the importance of small-scale within-population spatial structure in population dynamics and demonstrate that not only the absolute amount of limiting resources but also their spatial arrangement can be an important factor influencing population carrying capacity.

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

PubMed Central

Kim, Jiyu; Jung, Inkyung

2017-01-01

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

Spatial analysis on future housing markets: economic development and housing implications.

PubMed

Liu, Xin; Wang, Lizhe

2014-01-01

A coupled projection method combining formal modelling and other statistical techniques was developed to delineate the relationship between economic and social drivers for net new housing allocations. Using the example of employment growth in Tyne and Wear, UK, until 2016, the empirical analysis yields housing projections at the macro- and microspatial levels (e.g., region to subregion to elected ward levels). The results have important implications for the strategic planning of locations for housing and employment, demonstrating both intuitively and quantitatively how local economic developments affect housing demand.

Better Than Counting: Density Profiles from Force Sampling

NASA Astrophysics Data System (ADS)

de las Heras, Daniel; Schmidt, Matthias

2018-05-01

Calculating one-body density profiles in equilibrium via particle-based simulation methods involves counting of events of particle occurrences at (histogram-resolved) space points. Here, we investigate an alternative method based on a histogram of the local force density. Via an exact sum rule, the density profile is obtained with a simple spatial integration. The method circumvents the inherent ideal gas fluctuations. We have tested the method in Monte Carlo, Brownian dynamics, and molecular dynamics simulations. The results carry a statistical uncertainty smaller than that of the standard counting method, reducing therefore the computation time.

Spatial Analysis on Future Housing Markets: Economic Development and Housing Implications

PubMed Central

Liu, Xin; Wang, Lizhe

2014-01-01

A coupled projection method combining formal modelling and other statistical techniques was developed to delineate the relationship between economic and social drivers for net new housing allocations. Using the example of employment growth in Tyne and Wear, UK, until 2016, the empirical analysis yields housing projections at the macro- and microspatial levels (e.g., region to subregion to elected ward levels). The results have important implications for the strategic planning of locations for housing and employment, demonstrating both intuitively and quantitatively how local economic developments affect housing demand. PMID:24892097

Linked Micromaps: Statistical Summaries in a Spatial Context

EPA Science Inventory

Communicating summaries of spatial data to decision makers and the public is challenging. We present a graphical method that provides both a geographic context and a statistical summary for such spatial data. Monitoring programs have a need for such geographical summaries. For ...

Fourier decomposition of spatial localization errors reveals an idiotropic dominance of an internal model of gravity.

PubMed

De Sá Teixeira, Nuno Alexandre

2014-12-01

Given its conspicuous nature, gravity has been acknowledged by several research lines as a prime factor in structuring the spatial perception of one's environment. One such line of enquiry has focused on errors in spatial localization aimed at the vanishing location of moving objects - it has been systematically reported that humans mislocalize spatial positions forward, in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, spatial localization errors were found to evolve dynamically with time in a pattern congruent with an anticipated trajectory (representational trajectory). The present study attempts to ascertain the degree to which vestibular information plays a role in these phenomena. Human observers performed a spatial localization task while tilted to varying degrees and referring to the vanishing locations of targets moving along several directions. A Fourier decomposition of the obtained spatial localization errors revealed that although spatial errors were increased "downward" mainly along the body's longitudinal axis (idiotropic dominance), the degree of misalignment between the latter and physical gravity modulated the time course of the localization responses. This pattern is surmised to reflect increased uncertainty about the internal model when faced with conflicting cues regarding the perceived "downward" direction.

Spatial variation in hyperthermia emergency department visits among those with employer-based insurance in the United States - a case-crossover analysis.

PubMed

Saha, Shubhayu; Brock, John W; Vaidyanathan, Ambarish; Easterling, David R; Luber, George

2015-03-04

Predictions of intense heat waves across the United States will lead to localized health impacts, most of which are preventable. There is a need to better understand the spatial variation in the morbidity impacts associated with extreme heat across the country to prevent such adverse health outcomes. Hyperthermia-related emergency department (ED) visits were obtained from the Truven Health MarketScan(®) Research dataset for 2000-2010. Three measures of daily ambient heat were constructed using meteorological observations from the National Climatic Data Center (maximum temperature, heat index) and the Spatial Synoptic Classification. Using a time-stratified case crossover approach, odds ratio of hyperthermia-related ED visit were estimated for the three different heat measures. Random effects meta-analysis was used to combine the odds ratios for 94 Metropolitan Statistical Areas (MSA) to examine the spatial variation by eight latitude categories and nine U.S. climate regions. Examination of lags for all three temperature measures showed that the odds ratio of ED visit was statistically significant and highest on the day of the ED visit. For heat waves lasting two or more days, additional statistically significant association was observed when heat index and synoptic classification was used as the temperature measure. These results were insensitive to the inclusion of air pollution measures. On average, the maximum temperature on the day of an ED visit was 93.4°F in 'South' and 81.9°F in the 'Northwest' climatic regions of United States. The meta-analysis showed higher odds ratios of hyperthermia ED visit in the central and the northern parts of the country compared to the south and southwest. The results showed spatial variation in average temperature on days of ED visit and odds ratio for hyperthermia ED visits associated with extreme heat across United States. This suggests that heat response plans need to be customized for different regions and the potential role of hyperthermia ED visits in syndromic surveillance for extreme heat.

Modeling urbanization patterns at a global scale with generative adversarial networks

NASA Astrophysics Data System (ADS)

Albert, A. T.; Strano, E.; Gonzalez, M.

2017-12-01

Current demographic projections show that, in the next 30 years, global population growth will mostly take place in developing countries. Coupled with a decrease in density, such population growth could potentially double the land occupied by settlements by 2050. The lack of reliable and globally consistent socio-demographic data, coupled with the limited predictive performance underlying traditional urban spatial explicit models, call for developing better predictive methods, calibrated using a globally-consistent dataset. Thus, richer models of the spatial interplay between the urban built-up land, population distribution and energy use are central to the discussion around the expansion and development of cities, and their impact on the environment in the context of a changing climate. In this talk we discuss methods for, and present an analysis of, urban form, defined as the spatial distribution of macroeconomic quantities that characterize a city, using modern machine learning methods and best-available remote-sensing data for the world's largest 25,000 cities. We first show that these cities may be described by a small set of patterns in radial building density, nighttime luminosity, and population density, which highlight, to first order, differences in development and land use across the world. We observe significant, spatially-dependent variance around these typical patterns, which would be difficult to model using traditional statistical methods. We take a first step in addressing this challenge by developing CityGAN, a conditional generative adversarial network model for simulating realistic urban forms. To guide learning and measure the quality of the simulated synthetic cities, we develop a specialized loss function for GAN optimization that incorporates standard spatial statistics used by urban analysis experts. Our framework is a stark departure from both the standard physics-based approaches in the literature (that view urban forms as fractals with a scale-free behavior), and the traditional statistical learning approaches (whereby values of individual pixels are modeled as functions of locally-defined, hand-engineered features). This is a first-of-its-kind analysis of urban forms using data at a planetary scale.

3D source localization of interictal spikes in epilepsy patients with MRI lesions

NASA Astrophysics Data System (ADS)

Ding, Lei; Worrell, Gregory A.; Lagerlund, Terrence D.; He, Bin

2006-08-01

The present study aims to accurately localize epileptogenic regions which are responsible for epileptic activities in epilepsy patients by means of a new subspace source localization approach, i.e. first principle vectors (FINE), using scalp EEG recordings. Computer simulations were first performed to assess source localization accuracy of FINE in the clinical electrode set-up. The source localization results from FINE were compared with the results from a classic subspace source localization approach, i.e. MUSIC, and their differences were tested statistically using the paired t-test. Other factors influencing the source localization accuracy were assessed statistically by ANOVA. The interictal epileptiform spike data from three adult epilepsy patients with medically intractable partial epilepsy and well-defined symptomatic MRI lesions were then studied using both FINE and MUSIC. The comparison between the electrical sources estimated by the subspace source localization approaches and MRI lesions was made through the coregistration between the EEG recordings and MRI scans. The accuracy of estimations made by FINE and MUSIC was also evaluated and compared by R2 statistic, which was used to indicate the goodness-of-fit of the estimated sources to the scalp EEG recordings. The three-concentric-spheres head volume conductor model was built for each patient with three spheres of different radii which takes the individual head size and skull thickness into consideration. The results from computer simulations indicate that the improvement of source spatial resolvability and localization accuracy of FINE as compared with MUSIC is significant when simulated sources are closely spaced, deep, or signal-to-noise ratio is low in a clinical electrode set-up. The interictal electrical generators estimated by FINE and MUSIC are in concordance with the patients' structural abnormality, i.e. MRI lesions, in all three patients. The higher R2 values achieved by FINE than MUSIC indicate that FINE provides a more satisfactory fitting of the scalp potential measurements than MUSIC in all patients. The present results suggest that FINE provides a useful brain source imaging technique, from clinical EEG recordings, for identifying and localizing epileptogenic regions in epilepsy patients with focal partial seizures. The present study may lead to the establishment of a high-resolution source localization technique from scalp-recorded EEGs for aiding presurgical planning in epilepsy patients.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

The minimal local-asperity hypothesis of early retinal lateral inhibition.

PubMed

Balboa, R M; Grzywacz, N M

2000-07-01

Recently we found that the theories related to information theory existent in the literature cannot explain the behavior of the extent of the lateral inhibition mediated by retinal horizontal cells as a function of background light intensity. These theories can explain the fall of the extent from intermediate to high intensities, but not its rise from dim to intermediate intensities. We propose an alternate hypothesis that accounts for the extent's bell-shape behavior. This hypothesis proposes that the lateral-inhibition adaptation in the early retina is part of a system to extract several image attributes, such as occlusion borders and contrast. To do so, this system would use prior probabilistic knowledge about the biological processing and relevant statistics in natural images. A key novel statistic used here is the probability of the presence of an occlusion border as a function of local contrast. Using this probabilistic knowledge, the retina would optimize the spatial profile of lateral inhibition to minimize attribute-extraction error. The two significant errors that this minimization process must reduce are due to the quantal noise in photoreceptors and the straddling of occlusion borders by lateral inhibition.

Spatial reconstruction of single-cell gene expression data.

PubMed

Satija, Rahul; Farrell, Jeffrey A; Gennert, David; Schier, Alexander F; Regev, Aviv

2015-05-01

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Spatial reconstruction of single-cell gene expression

PubMed Central

Satija, Rahul; Farrell, Jeffrey A.; Gennert, David; Schier, Alexander F.; Regev, Aviv

2015-01-01

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. PMID:25867923

Spatial association of public sports facilities with body mass index in Korea.

PubMed

Han, Eun Jin; Kang, Kiyeon; Sohn, So Young

2018-05-07

Governments and also local councils create and enforce their own regional public health care plans for the problem of overweight and obesity in the population. Public sports facilities can help these plans. In this paper, we investigated the contribution of public sports facilities to the reduction of the obesity of local residents. We used the data obtained from the Fifth Korea National Health and Nutrition Examination Surveys; and measured the degree of obesity using body mass index (BMI). We conducted various spatial regression analyses including the global Moran's I test and local indicators of spatial autocorrelation analysis finding that there exists spatial dependence in the error term of spatial regression model for BMI. However, we also observed that the number of local public sports facilities is not significantly related to local BMI. This result can be caused by the low utilization ratio and an unbalanced spatial distribution of local public sports facilities. Based on our findings, we suggest that local councils need to improve the quality of public sports facilities encouraging the establishment of preferred types of pubic sports facilities.

Rainfall-triggered shallow landslides at catchment scale: Threshold mechanics-based modeling for abruptness and localization

NASA Astrophysics Data System (ADS)

von Ruette, J.; Lehmann, P.; Or, D.

2013-10-01

Rainfall-induced shallow landslides may occur abruptly without distinct precursors and could span a wide range of soil mass released during a triggering event. We present a rainfall-induced landslide-triggering model for steep catchments with surfaces represented as an assembly of hydrologically and mechanically interconnected soil columns. The abruptness of failure was captured by defining local strength thresholds for mechanical bonds linking soil and bedrock and adjacent columns, whereby a failure of a single bond may initiate a chain reaction of subsequent failures, culminating in local mass release (a landslide). The catchment-scale hydromechanical landslide-triggering model (CHLT) was applied to results from two event-based landslide inventories triggered by two rainfall events in 2002 and 2005 in two nearby catchments located in the Prealps in Switzerland. Rainfall radar data, surface elevation and vegetation maps, and a soil production model for soil depth distribution were used for hydromechanical modeling of failure patterns for the two rainfall events at spatial and temporal resolutions of 2.5 m and 0.02 h, respectively. The CHLT model enabled systematic evaluation of the effects of soil type, mechanical reinforcement (soil cohesion and lateral root strength), and initial soil water content on landslide characteristics. We compared various landslide metrics and spatial distribution of simulated landslides in subcatchments with observed inventory data. Model parameters were optimized for the short but intense rainfall event in 2002, and the calibrated model was then applied for the 2005 rainfall, yielding reasonable predictions of landslide events and volumes and statistically reproducing localized landslide patterns similar to inventory data. The model provides a means for identifying local hot spots and offers insights into the dynamics of locally resolved landslide hazards in mountainous regions.

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.

White matter alterations in narcolepsy patients with cataplexy: tract-based spatial statistics.

PubMed

Park, Yun K; Kwon, Oh-Hun; Joo, Eun Yeon; Kim, Jae-Hun; Lee, Jong M; Kim, Sung T; Hong, Seung B

2016-04-01

Functional imaging studies and voxel-based morphometry analysis of brain magnetic resonance imaging showed abnormalities in the hypothalamus-thalamus-orbitofrontal pathway, demonstrating altered hypocretin pathway in narcolepsy. Those distinct morphometric changes account for problems in wake-sleep control, attention and memory. It also raised the necessity to evaluate white matter changes. To investigate brain white matter alterations in drug-naïve narcolepsy patients with cataplexy and to explore relationships between white matter changes and patient clinical characteristics, drug-naïve narcolepsy patients with cataplexy (n = 22) and healthy age- and gender-matched controls (n = 26) were studied. Fractional anisotropy and mean diffusivity images were obtained from whole-brain diffusion tensor imaging, and tract-based spatial statistics were used to localize white matter abnormalities. Compared with controls, patients showed significant decreases in fractional anisotropy of white matter of the bilateral anterior cingulate, fronto-orbital area, frontal lobe, anterior limb of the internal capsule and corpus callosum, as well as the left anterior and medial thalamus. Patients and controls showed no differences in mean diffusivity. Among patients, mean diffusivity values of white matter in the bilateral superior frontal gyri, bilateral fronto-orbital gyri and right superior parietal gyrus were positively correlated with depressive mood. This tract-based spatial statistics study demonstrated that drug-naïve patients with narcolepsy had reduced fractional anisotropy of white matter in multiple brain areas and significant relationship between increased mean diffusivity of white matter in frontal/cingulate and depression. It suggests the widespread disruption of white matter integrity and prevalent brain degeneration of frontal lobes according to a depressive symptom in narcolepsy. © 2015 European Sleep Research Society.

Imaging of Al/Fe ratios in synthetic Al-goethite revealed by nanoscale secondary ion mass spectrometry.

PubMed

Pohl, Lydia; Kölbl, Angelika; Werner, Florian; Mueller, Carsten W; Höschen, Carmen; Häusler, Werner; Kögel-Knabner, Ingrid

2018-04-30

Aluminium (Al)-substituted goethite is ubiquitous in soils and sediments. The extent of Al-substitution affects the physicochemical properties of the mineral and influences its macroscale properties. Bulk analysis only provides total Al/Fe ratios without providing information with respect to the Al-substitution of single minerals. Here, we demonstrate that nanoscale secondary ion mass spectrometry (NanoSIMS) enables the precise determination of Al-content in single minerals, while simultaneously visualising the variation of the Al/Fe ratio. Al-substituted goethite samples were synthesized with increasing Al concentrations of 0.1, 3, and 7 % and analysed by NanoSIMS in combination with established bulk spectroscopic methods (XRD, FTIR, Mössbauer spectroscopy). The high spatial resolution (50-150 nm) of NanoSIMS is accompanied by a high number of single-point measurements. We statistically evaluated the Al/Fe ratios derived from NanoSIMS, while maintaining the spatial information and reassigning it to its original localization. XRD analyses confirmed increasing concentration of incorporated Al within the goethite structure. Mössbauer spectroscopy revealed 11 % of the goethite samples generated at high Al concentrations consisted of hematite. The NanoSIMS data show that the Al/Fe ratios are in agreement with bulk data derived from total digestion and demonstrated small spatial variability between single-point measurements. More advantageously, statistical analysis and reassignment of single-point measurements allowed us to identify distinct spots with significantly higher or lower Al/Fe ratios. NanoSIMS measurements confirmed the capacity to produce images, which indicated the uniform increase in Al-concentrations in goethite. Using a combination of statistical analysis with information from complementary spectroscopic techniques (XRD, FTIR and Mössbauer spectroscopy) we were further able to reveal spots with lower Al/Fe ratios as hematite. Copyright © 2018 John Wiley & Sons, Ltd.

Characteristics of Sudden Commencements Observed by Van Allen Probes in the Inner Magnetosphere

NASA Astrophysics Data System (ADS)

Fathy, A.; Kim, K.-H.; Park, J.-S.; Jin, H.; Kletzing, C.; Wygant, J. R.; Ghamry, E.

2018-02-01

We have statistically studied sudden commencement (SC) by using the data acquired from Van Allen Probes (VAP) in the inner magnetosphere (L = 3.0-6.5) and GOES spacecraft at geosynchronous orbit (L =˜ 6.7) from October 2012 to September 2017. During the time period, we identified 85 SCs in the inner magnetosphere and 90 SCs at geosynchronous orbit. Statistical results of the SC events reveal the following characteristics. (1) There is strong seasonal dependence of the geosynchronous SC amplitude in the radial BV component at all local times. However, BV shows weak seasonal variation on the dayside in the inner magnetosphere. (2) The local time dependence of the SC amplitude in the compressional BH component at geosynchronous orbit is similar to that in the inner magnetosphere. (3) In a nightside region of L = 5.0-6.5, ˜19% of BH events are negative, while ˜58% of BH events are negative at geosynchronous orbit. (4) The amplitude of the SC-associated Ey perturbations varies systematically with local time with a morning-afternoon asymmetry near noon. These observations can be explained by spatial and/or temporal changes in the magnetopause and cross-tail currents, which are caused by changes in the solar wind dynamic pressure, with respect to spacecraft positions.

Improved Bayesian Infrasonic Source Localization for regional infrasound

DOE PAGES

Blom, Philip S.; Marcillo, Omar; Arrowsmith, Stephen J.

2015-10-20

The Bayesian Infrasonic Source Localization (BISL) methodology is examined and simplified providing a generalized method of estimating the source location and time for an infrasonic event and the mathematical framework is used therein. The likelihood function describing an infrasonic detection used in BISL has been redefined to include the von Mises distribution developed in directional statistics and propagation-based, physically derived celerity-range and azimuth deviation models. Frameworks for constructing propagation-based celerity-range and azimuth deviation statistics are presented to demonstrate how stochastic propagation modelling methods can be used to improve the precision and accuracy of the posterior probability density function describing themore » source localization. Infrasonic signals recorded at a number of arrays in the western United States produced by rocket motor detonations at the Utah Test and Training Range are used to demonstrate the application of the new mathematical framework and to quantify the improvement obtained by using the stochastic propagation modelling methods. Moreover, using propagation-based priors, the spatial and temporal confidence bounds of the source decreased by more than 40 per cent in all cases and by as much as 80 per cent in one case. Further, the accuracy of the estimates remained high, keeping the ground truth within the 99 per cent confidence bounds for all cases.« less

Temporal complexity in emission from Anderson localized lasers

NASA Astrophysics Data System (ADS)

Kumar, Randhir; Balasubrahmaniyam, M.; Alee, K. Shadak; Mujumdar, Sushil

2017-12-01

Anderson localization lasers exploit resonant cavities formed due to structural disorder. The inherent randomness in the structure of these cavities realizes a probability distribution in all cavity parameters such as quality factors, mode volumes, mode structures, and so on, implying resultant statistical fluctuations in the temporal behavior. Here we provide direct experimental measurements of temporal width distributions of Anderson localization lasing pulses in intrinsically and extrinsically disordered coupled-microresonator arrays. We first illustrate signature exponential decays in the spatial intensity distributions of the lasing modes that quantify their localized character, and then measure the temporal width distributions of the pulsed emission over several configurations. We observe a dependence of temporal widths on the disorder strength, wherein the widths show a single-peaked, left-skewed distribution in extrinsic disorder and a dual-peaked distribution in intrinsic disorder. We propose a model based on coupled rate equations for an emitter and an Anderson cavity with a random mode structure, which gives excellent quantitative and qualitative agreement with the experimental observations. The experimental and theoretical analyses bring to the fore the temporal complexity in Anderson-localization-based lasing systems.

Household perceptions of coastal hazards and climate change in the Central Philippines.

PubMed

Combest-Friedman, Chelsea; Christie, Patrick; Miles, Edward

2012-12-15

As a tropical archipelagic nation, the Philippines is particularly susceptible to coastal hazards, which are likely to be exacerbated by climate change. To improve coastal hazard management and adaptation planning, it is imperative that climate information be provided at relevant scales and that decision-makers understand the causes and nature of risk in their constituencies. Focusing on a municipality in the Central Philippines, this study examines local meteorological information and explores household perceptions of climate change and coastal hazard risk. First, meteorological data and local perceptions of changing climate conditions are assessed. Perceived changes in climate include an increase in rainfall and rainfall variability, an increase in intensity and frequency of storm events and sea level rise. Second, factors affecting climate change perceptions and perceived risk from coastal hazards are determined through statistical analysis. Factors tested include social status, economic standing, resource dependency and spatial location. Results indicate that perceived risk to coastal hazards is most affected by households' spatial location and resource dependency, rather than socio-economic conditions. However, important differences exist based on the type of hazard and nature of risk being measured. Resource dependency variables are more significant in determining perceived risk from coastal erosion and sea level rise than flood events. Spatial location is most significant in determining households' perceived risk to their household assets, but not perceived risk to their livelihood. Copyright © 2012 Elsevier Ltd. All rights reserved.

Community assembly processes underlying phytoplankton and bacterioplankton across a hydrologic change in a human-impacted river.

PubMed

Isabwe, Alain; Yang, Jun R; Wang, Yongming; Liu, Lemian; Chen, Huihuang; Yang, Jun

2018-07-15

Although the influence of microbial community assembly processes on aquatic ecosystem function and biodiversity is well known, the processes that govern planktonic communities in human-impacted rivers remain largely unstudied. Here, we used multivariate statistics and a null model approach to test the hypothesis that environmental conditions and obstructed dispersal opportunities, dictate a deterministic community assembly for phytoplankton and bacterioplankton across contrasting hydrographic conditions in a subtropical mid-sized river (Jiulong River, southeast China). Variation partitioning analysis showed that the explanatory power of local environmental variables was larger than that of the spatial variables for both plankton communities during the dry season. During the wet season, phytoplankton community variation was mainly explained by local environmental variables, whereas the variance in bacterioplankton was explained by both environmental and spatial predictors. The null model based on Raup-Crick coefficients for both planktonic groups suggested little evidences of the stochastic processes involving dispersal and random distribution. Our results showed that hydrological change and landscape structure act together to cause divergence in communities along the river channel, thereby dictating a deterministic assembly and that selection exceeds dispersal limitation during the dry season. Therefore, to protect the ecological integrity of human-impacted rivers, watershed managers should not only consider local environmental conditions but also dispersal routes to account for the effect of regional species pool on local communities. Copyright © 2018 Elsevier B.V. All rights reserved.

The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

PubMed

Hill, Timothy; Chocholek, Melanie; Clement, Robert

2017-06-01

Eddy covariance (EC) continues to provide invaluable insights into the dynamics of Earth's surface processes. However, despite its many strengths, spatial replication of EC at the ecosystem scale is rare. High equipment costs are likely to be partially responsible. This contributes to the low sampling, and even lower replication, of ecoregions in Africa, Oceania (excluding Australia) and South America. The level of replication matters as it directly affects statistical power. While the ergodicity of turbulence and temporal replication allow an EC tower to provide statistically robust flux estimates for its footprint, these principles do not extend to larger ecosystem scales. Despite the challenge of spatially replicating EC, it is clearly of interest to be able to use EC to provide statistically robust flux estimates for larger areas. We ask: How much spatial replication of EC is required for statistical confidence in our flux estimates of an ecosystem? We provide the reader with tools to estimate the number of EC towers needed to achieve a given statistical power. We show that for a typical ecosystem, around four EC towers are needed to have 95% statistical confidence that the annual flux of an ecosystem is nonzero. Furthermore, if the true flux is small relative to instrument noise and spatial variability, the number of towers needed can rise dramatically. We discuss approaches for improving statistical power and describe one solution: an inexpensive EC system that could help by making spatial replication more affordable. However, we note that diverting limited resources from other key measurements in order to allow spatial replication may not be optimal, and a balance needs to be struck. While individual EC towers are well suited to providing fluxes from the flux footprint, we emphasize that spatial replication is essential for statistically robust fluxes if a wider ecosystem is being studied. © 2016 The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Spatial-Temporal Distribution of Hantavirus Rodent-Borne Infection by Oligoryzomys fulvescens in the Agua Buena Region - Panama

PubMed Central

Gonzalez, Publio; Cumbrera, Alberto; Rivero, Alina; Avila, Mario; Armién, Aníbal G.; Koster, Frederick; Glass, Gregory

2016-01-01

Background Hotspot detection and characterization has played an increasing role in understanding the maintenance and transmission of zoonotic pathogens. Identifying the specific environmental factors (or their correlates) that influence reservoir host abundance help increase understanding of how pathogens are maintained in natural systems and are crucial to identifying disease risk. However, most recent studies are performed at macro-scale and describe broad temporal patterns of population abundances. Few have been conducted at a microscale over short time periods that better capture the dynamical patterns of key populations. These finer resolution studies may better define the likelihood of local pathogen persistence. This study characterizes the landscape distribution and spatio-temporal dynamics of Oligoryzomys fulvescens (O. fulvescens), an important mammalian reservoir in Central America. Methods Information collected in a longitudinal study of rodent populations in the community of Agua Buena in Tonosí, Panama, between April 2006 and December 2009 was analyzed using non-spatial analyses (box plots) and explicit spatial statistical tests (correlograms, SADIE and LISA). A 90 node grid was built (raster format) to design a base map. The area between the nodes was 0.09 km2 and the total study area was 6.43 km2 (2.39 x 2.69 km). The temporal assessment dataset was divided into four periods for each year studied: the dry season, rainy season, and two months-long transitions between seasons (the months of April and December). Results There were heterogeneous patterns in the population densities and degrees of dispersion of O. fulvescens that varied across seasons and among years. The species typically was locally absent during the late transitional months of the season, and re-established locally in subsequent years. These populations re-occurred in the same area during the first three years but subsequently re-established further south in the final year of the study. Spatial autocorrelation analyses indicated local populations encompassed approximately 300–600 m. The borders between suitable and unsuitable habitats were sharply demarcated over short distances. Conclusion Oligoryzomys fulvescens showed a well-defined spatial pattern that evolved over time, and led to a pattern of changing aggregation. Thus, hot spots of abundance showed a general shifting pattern that helps explain the intermittent risk from pathogens transmitted by this species. This variation was associated with seasonality, as well as anthropogenic pressures that occurred with agricultural activities. These factors help define the characteristics of the occurrence, timing, intensity and duration of synanthropic populations affected by human populations and, consequently, possible exposure that local human populations experience. PMID:26894436

Spatial-Temporal Distribution of Hantavirus Rodent-Borne Infection by Oligoryzomys fulvescens in the Agua Buena Region--Panama.

PubMed

Armién, Blas; Ortiz, Paulo Lazaro; Gonzalez, Publio; Cumbrera, Alberto; Rivero, Alina; Avila, Mario; Armién, Aníbal G; Koster, Frederick; Glass, Gregory

2016-02-01

Hotspot detection and characterization has played an increasing role in understanding the maintenance and transmission of zoonotic pathogens. Identifying the specific environmental factors (or their correlates) that influence reservoir host abundance help increase understanding of how pathogens are maintained in natural systems and are crucial to identifying disease risk. However, most recent studies are performed at macro-scale and describe broad temporal patterns of population abundances. Few have been conducted at a microscale over short time periods that better capture the dynamical patterns of key populations. These finer resolution studies may better define the likelihood of local pathogen persistence. This study characterizes the landscape distribution and spatio-temporal dynamics of Oligoryzomys fulvescens (O. fulvescens), an important mammalian reservoir in Central America. Information collected in a longitudinal study of rodent populations in the community of Agua Buena in Tonosí, Panama, between April 2006 and December 2009 was analyzed using non-spatial analyses (box plots) and explicit spatial statistical tests (correlograms, SADIE and LISA). A 90 node grid was built (raster format) to design a base map. The area between the nodes was 0.09 km(2) and the total study area was 6.43 km(2) (2.39 x 2.69 km). The temporal assessment dataset was divided into four periods for each year studied: the dry season, rainy season, and two months-long transitions between seasons (the months of April and December). There were heterogeneous patterns in the population densities and degrees of dispersion of O. fulvescens that varied across seasons and among years. The species typically was locally absent during the late transitional months of the season, and re-established locally in subsequent years. These populations re-occurred in the same area during the first three years but subsequently re-established further south in the final year of the study. Spatial autocorrelation analyses indicated local populations encompassed approximately 300-600 m. The borders between suitable and unsuitable habitats were sharply demarcated over short distances. Oligoryzomys fulvescens showed a well-defined spatial pattern that evolved over time, and led to a pattern of changing aggregation. Thus, hot spots of abundance showed a general shifting pattern that helps explain the intermittent risk from pathogens transmitted by this species. This variation was associated with seasonality, as well as anthropogenic pressures that occurred with agricultural activities. These factors help define the characteristics of the occurrence, timing, intensity and duration of synanthropic populations affected by human populations and, consequently, possible exposure that local human populations experience.

Using neuronal populations to study the mechanisms underlying spatial and feature attention

PubMed Central

Cohen, Marlene R.; Maunsell, John H.R.

2012-01-01

Summary Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and non-spatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can co-modulate local neuronal populations, while feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, while spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons. PMID:21689604

Spatial variability of NDVI at different seasons in the Community of Madrid (Spain)

NASA Astrophysics Data System (ADS)

Sotoca, Juan J. Martin; Saa-Requejo, Antonio; Borondo, Javier; Tarquis, Ana M.

2015-04-01

Agricultural drought quantification is one of the most important tasks in the characterization process of this natural hazard and its implications in crop insurance. Recently, several vegetation indexes based on remote-sensing data (VI) has been applied to quantify it (Dalezios et al, 2012). VIs are obtained combining several frequency bands that represent the relationship between photosynthesis and absorbed/reflected radiation. The most widely used VI is the Normalized Difference Vegetation Index (NDVI). It is based on the principle that healthy vegetation mainly absorbs visible light and reflects the near-infrared frequency band. Drought can be highly localized, and several authors have recognized the critical role of soil moisture and its spatial variability in agricultural losses (Anderson et al., 2011). Therefore, it is important to delimit locations within a homogeneous area that will share main NDVI statistics and in which the same threshold value can be applied to define drought event. In order to do so, we have applied for the first time in this context the method of singularity maps (Cheng and Agterberg, 1996) commonly used in localization of mineral deposits. The NDVI singularity maps calculated in each season through 2011/2012 are showed and discussed (Martín-Sotoca, 2014). References Anderson, M:C:, C. R. Hain, B. Wardlow, J. R. Mecikalski and W. P. Kustas (2011) Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Climate, 24, 2025-2044. Dalezios, N.R., A. Blanta, N.V. Spyropoulos and A.M. Tarquis (2012) Risk identification of agricultural drought for sustainable Agroecosystems. Nat. Hazards Earth Syst. Sci., 14, 2435-2448. Cheng, Q. and F.P. Agterberg (1996) Multifractal modeling and spatial statistics. Math. Geol., 28, 1-16. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish). Acknowledgements First author acknowledges the Research Grant obtained from CEIGRAM in 2014

Monthly AOD maps combining strengths of remote sensing products

NASA Astrophysics Data System (ADS)

Kinne, Stefan

2010-05-01

The mid-visible aerosol optical depth (AOD) is the most prominent property to quantify aerosol amount the atmospheric column. Almost all aerosol retrievals of satellite sensors provide estimates for this property, however, often with limited success. As sensors differ in capabilities individual retrievals have local and regional strengths and weaknesses. Focusing on individual retrieval strengths a satellite based AOD composite has been constructed. Hereby, every retrieval performance has been assessed in statistical comparisons to ground-based sun-photometry, which provide highly accurate references though only at few globally distributed monitoring sites. Based on these comparisons, which consider bias as well as spatial patterns and seasonality, the regionally best performing satellite AOD products are combined. The resulting remote sensing AOD composite provide a general reference for the spatial and temporal AOD distribution on an (almost) global basis - solely tied to sensor data.

Statistical and Economic Techniques for Site-specific Nematode Management.

PubMed

Liu, Zheng; Griffin, Terry; Kirkpatrick, Terrence L

2014-03-01

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.

Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China

PubMed Central

2014-01-01

Background There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale. Methods HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. Conclusions The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially heterogeneous over the Mainland China, which is related to both the climate and socio-economic variables. The combination of socio-economic and climate exposures can explain the HFMD occurrences more comprehensively and objectively than those with only climate exposures. The modeled probability of HFMD occurrence at the county level reveals not only the spatial trends, but also the local details of epidemic risk, even in the regions where there were no HFMD case records. PMID:24731248

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

PubMed

Song, Xiao-Dong; Zhang, Gan-Lin; Liu, Feng; Li, De-Cheng; Zhao, Yu-Guo

2016-11-01

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

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.

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

PubMed

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

2013-01-01

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

[Characteristics of temporal-spatial differentiation in landscape pattern vulnerability in Nansihu Lake wetland, China.

PubMed

Liang, Jia Xin; Li, Xin Ju

2018-02-01

With remote sensing images from 1985, 2000 Lantsat 5 TM and 2015 Lantsat 8 OLI as data sources, we tried to select the suitable research scale and examine the temporal-spatial diffe-rentiation with such scale in the Nansihu Lake wetland by using landscape pattern vulnerability index constructed by sensitivity index and adaptability index, and combined with space statistics such as semivariogram and spatial autocorrelation. The results showed that 1 km × 1 km equidistant grid was the suitable research scale, which could eliminate the influence of spatial heterogeneity induced by random factors. From 1985 to 2015, the landscape pattern vulnerability in the Nansihu Lake wetland deteriorated gradually. The high-risk area of landscape pattern vulnerability dramatically expanded with time. The spatial heterogeneity of landscape pattern vulnerability increased, and the influence of non-structural factors on landscape pattern vulnerability strengthened. Spatial variability affected by spatial autocorrelation slightly weakened. Landscape pattern vulnerability had strong general spatial positive correlation, with the significant form of spatial agglomeration. The positive spatial autocorrelation continued to increase and the phenomenon of spatial concentration was more and more obvious over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighboring space units. The high-high accumulation areas showed the strongest level of significance, and the significant level of low-low accumulation zone increased with time. Natural factors, such as temperature and precipitation, affected water-level and landscape distribution, and thus changed the landscape patterns vulnerability of Nansihu Lake wetland. The dominant driver for the deterioration of landscape patterns vulnerability was human activities, including social economy activity and policy system.

On the use of multiple-point statistics to improve groundwater flow modeling in karst aquifers: A case study from the Hydrogeological Experimental Site of Poitiers, France

NASA Astrophysics Data System (ADS)

Le Coz, Mathieu; Bodin, Jacques; Renard, Philippe

2017-02-01

Limestone aquifers often exhibit complex groundwater flow behaviors resulting from depositional heterogeneities and post-lithification fracturing and karstification. In this study, multiple-point statistics (MPS) was applied to reproduce karst features and to improve groundwater flow modeling. For this purpose, MPS realizations were used in a numerical flow model to simulate the responses to pumping test experiments observed at the Hydrogeological Experimental Site of Poitiers, France. The main flow behaviors evident in the field data were simulated, particularly (i) the early-time inflection of the drawdown signal at certain observation wells and (ii) the convex behavior of the drawdown curves at intermediate times. In addition, it was shown that the spatial structure of the karst features at various scales is critical with regard to the propagation of the depletion wave induced by pumping. Indeed, (i) the spatial shape of the cone of depression is significantly affected by the karst proportion in the vicinity of the pumping well, and (ii) early-time inflection of the drawdown signal occurs only at observation wells crossing locally well-developed karst features.

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

Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.

Here we present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scaledependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show largemore » differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Finally, our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.« less

Gridded National Inventory of U.S. Methane Emissions

NASA Technical Reports Server (NTRS)

Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; Turner, Alexander J.; Weitz, Melissa; Wirth, Tom; Hight, Cate; DeFigueiredo, Mark; Desai, Mausami; Schmeltz, Rachel;

Gridded national inventory of U.S. methane emissions

DOE PAGES

Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; ...

2016-11-16

Here we present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scaledependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show largemore » differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Finally, our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.« less

Gridded National Inventory of U.S. Methane Emissions.

PubMed

Maasakkers, Joannes D; Jacob, Daniel J; Sulprizio, Melissa P; Turner, Alexander J; Weitz, Melissa; Wirth, Tom; Hight, Cate; DeFigueiredo, Mark; Desai, Mausami; Schmeltz, Rachel; Hockstad, Leif; Bloom, Anthony A; Bowman, Kevin W; Jeong, Seongeun; Fischer, Marc L

2016-12-06

We present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show large differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.

Enhanced visual statistical learning in adults with autism

PubMed Central

Roser, Matthew E.; Aslin, Richard N.; McKenzie, Rebecca; Zahra, Daniel; Fiser, József

2014-01-01

Individuals with autism spectrum disorder (ASD) are often characterized as having social engagement and language deficiencies, but a sparing of visuo-spatial processing and short-term memory, with some evidence of supra-normal levels of performance in these domains. The present study expanded on this evidence by investigating the observational learning of visuospatial concepts from patterns of covariation across multiple exemplars. Child and adult participants with ASD, and age-matched control participants, viewed multi-shape arrays composed from a random combination of pairs of shapes that were each positioned in a fixed spatial arrangement. After this passive exposure phase, a post-test revealed that all participant groups could discriminate pairs of shapes with high covariation from randomly paired shapes with low covariation. Moreover, learning these shape-pairs with high covariation was superior in adults with ASD than in age-matched controls, while performance in children with ASD was no different than controls. These results extend previous observations of visuospatial enhancement in ASD into the domain of learning, and suggest that enhanced visual statistical learning may have arisen from a sustained bias to attend to local details in complex arrays of visual features. PMID:25151115

High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city

NASA Astrophysics Data System (ADS)

Zhang, Shaojun; Wu, Ye; Huang, Ruikun; Wang, Jiandong; Yan, Han; Zheng, Yali; Hao, Jiming

2016-08-01

Vehicle emissions containing air pollutants created substantial environmental impacts on air quality for many traffic-populated cities in eastern Asia. A high-resolution emission inventory is a useful tool compared with traditional tools (e.g. registration data-based approach) to accurately evaluate real-world traffic dynamics and their environmental burden. In this study, Macau, one of the most populated cities in the world, is selected to demonstrate a high-resolution simulation of vehicular emissions and their contribution to air pollutant concentrations by coupling multimodels. First, traffic volumes by vehicle category on 47 typical roads were investigated during weekdays in 2010 and further applied in a networking demand simulation with the TransCAD model to establish hourly profiles of link-level vehicle counts. Local vehicle driving speed and vehicle age distribution data were also collected in Macau. Second, based on a localized vehicle emission model (e.g. the emission factor model for the Beijing vehicle fleet - Macau, EMBEV-Macau), this study established a link-based vehicle emission inventory in Macau with high resolution meshed in a temporal and spatial framework. Furthermore, we employed the AERMOD (AMS/EPA Regulatory Model) model to map concentrations of CO and primary PM2.5 contributed by local vehicle emissions during weekdays in November 2010. This study has discerned the strong impact of traffic flow dynamics on the temporal and spatial patterns of vehicle emissions, such as a geographic discrepancy of spatial allocation up to 26 % between THC and PM2.5 emissions owing to spatially heterogeneous vehicle-use intensity between motorcycles and diesel fleets. We also identified that the estimated CO2 emissions from gasoline vehicles agreed well with the statistical fuel consumption in Macau. Therefore, this paper provides a case study and a solid framework for developing high-resolution environment assessment tools for other vehicle-populated cities in eastern Asia.

Communicating Climate Uncertainties: Challenges and Opportunities Related to Spatial Scales, Extreme Events, and the Warming 'Hiatus'

NASA Astrophysics Data System (ADS)

Casola, J. H.; Huber, D.

2013-12-01

Many media, academic, government, and advocacy organizations have achieved sophistication in developing effective messages based on scientific information, and can quickly translate salient aspects of emerging climate research and evolving observations. However, there are several ways in which valid messages can be misconstrued by decision makers, leading them to inaccurate conclusions about the risks associated with climate impacts. Three cases will be discussed: 1) Issues of spatial scale in interpreting climate observations: Local climate observations may contradict summary statements about the effects of climate change on larger regional or global spatial scales. Effectively addressing these differences often requires communicators to understand local and regional climate drivers, and the distinction between a 'signal' associated with climate change and local climate 'noise.' Hydrological statistics in Missouri and California are shown to illustrate this case. 2) Issues of complexity related to extreme events: Climate change is typically invoked following a wide range of damaging meteorological events (e.g., heat waves, landfalling hurricanes, tornadoes), regardless of the strength of the relationship between anthropogenic climate change and the frequency or severity of that type of event. Examples are drawn from media coverage of several recent events, contrasting useful and potentially confusing word choices and frames. 3) Issues revolving around climate sensitivity: The so-called 'pause' or 'hiatus' in global warming has reverberated strongly through political and business discussions of climate change. Addressing the recent slowdown in warming yields an important opportunity to raise climate literacy in these communities. Attempts to use recent observations as a wedge between climate 'believers' and 'deniers' is likely to be counterproductive. Examples are drawn from Congressional testimony and media stories. All three cases illustrate ways that decision makers can arrive at invalid conclusions from a seemingly valid scientific messages. Honest discussion of uncertainties, and recognition of the spatial and time scales associated with decision making, can work to combat this potential confusion.

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.

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand.

PubMed

Nimbalkar, Prakash Madhav; Tripathi, Nitin Kumar

2016-11-21

Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P>0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

The Correlation Between Poverty and Access to Essential Surgical Care in Ghana: A Geospatial Analysis

PubMed Central

Stewart, Barclay T.; Gyedu, Adam; Boakye, Godfred; Lewis, Daniel; Hoogerboord, Marius; Mock, Charles

2017-01-01

Background Surgical disease burden falls disproportionately on individuals in low- and middle-income countries. These populations are also the least likely to have access to surgical care. Understanding the barriers to access in these populations is therefore necessary to meet the global surgical need. Methods Using geospatial methods, this study explores the district-level variation of two access barriers in Ghana: poverty and spatial access to care. National survey data were used to estimate the average total household expenditure (THE) in each district. Estimates of the spatial access to essential surgical care were generated from a cost-distance model based on a recent surgical capacity assessment. Correlations were analyzed using regression and displayed cartographically. Results Both THE and spatial access to surgical care were found to have statistically significant regional variation in Ghana (p < 0.001). An inverse relationship was identified between THE and spatial access to essential surgical care (β −5.15 USD, p < 0.001). Poverty and poor spatial access to surgical care were found to co-localize in the northwest of the country. Conclusions Multiple barriers to accessing surgical care can coexist within populations. A careful understanding of all access barriers is necessary to identify and target strategies to address unmet surgical need within a given population. PMID:27766400

Predictive spatial modeling of narcotic crop growth patterns

USGS Publications Warehouse

Waltz, Frederick A.; Moore, D.G.

1986-01-01

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

Spatial pattern analysis of Cu, Zn and Ni and their interpretation in the Campania region (Italy)

NASA Astrophysics Data System (ADS)

Petrik, Attila; Albanese, Stefano; Jordan, Gyozo; Rolandi, Roberto; De Vivo, Benedetto

2017-04-01

The uniquely abundant Campanian topsoil dataset enabled us to perform a spatial pattern analysis on 3 potentially toxic elements of Cu, Zn and Ni. This study is focusing on revealing the spatial texture and distribution of these elements by spatial point pattern and image processing analysis such as lineament density and spatial variability index calculation. The application of these methods on geochemical data provides a new and efficient tool to understand the spatial variation of concentrations and their background/baseline values. The determination and quantification of spatial variability is crucial to understand how fast the change in concentration is in a certain area and what processes might govern the variation. The spatial variability index calculation and image processing analysis including lineament density enables us to delineate homogenous areas and analyse them with respect to lithology and land use. Identification of spatial outliers and their patterns were also investigated by local spatial autocorrelation and image processing analysis including the determination of local minima and maxima points and singularity index analysis. The spatial variability of Cu and Zn reveals the highest zone (Cu: 0.5 MAD, Zn: 0.8-0.9 MAD, Median Deviation Index) along the coast between Campi Flegrei and the Sorrento Peninsula with the vast majority of statistically identified outliers and high-high spatial clustered points. The background/baseline maps of Cu and Zn reveals a moderate to high variability (Cu: 0.3 MAD, Zn: 0.4-0.5 MAD) NW-SE oriented zone including disrupted patches from Bisaccia to Mignano following the alluvial plains of Appenine's rivers. This zone has high abundance of anomaly concentrations identified using singularity analysis and it also has a high density of lineaments. The spatial variability of Ni shows the highest variability zone (0.6-0.7 MAD) around Campi Flegrei where the majority of low outliers are concentrated. The variability of background/baseline map of Ni reveals a shift to the east in case of highest variability zones coinciding with limestone outcrops. The high segmented area between Mignano and Bisaccia partially follows the alluvial plains of Appenine's rivers which seem to be playing a crucial role in the distribution and redistribution pattern of Cu, Zn and Ni in Campania. The high spatial variability zones of the later elements are located in topsoils on volcanoclastic rocks and are mostly related to cultivation and urbanised areas.

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.

Arsenic contamination of drinking water in Ireland: A spatial analysis of occurrence and potential risk.

PubMed

McGrory, Ellen R; Brown, Colin; Bargary, Norma; Williams, Natalya Hunter; Mannix, Anthony; Zhang, Chaosheng; Henry, Tiernan; Daly, Eve; Nicholas, Sarah; Petrunic, Barbara M; Lee, Monica; Morrison, Liam

2017-02-01

The presence of arsenic in groundwater has become a global concern due to the health risks from drinking water with elevated concentrations. The Water Framework Directive (WFD) of the European Union calls for drinking water risk assessment for member states. The present study amalgamates readily available national and sub-national scale datasets on arsenic in groundwater in the Republic of Ireland. However, due to the presence of high levels of left censoring (i.e. arsenic values below an analytical detection limit) and changes in detection limits over time, the application of conventional statistical methods would inhibit the generation of meaningful results. In order to handle these issues several arsenic databases were integrated and the data modelled using statistical methods appropriate for non-detect data. In addition, geostatistical methods were used to assess principal risk components of elevated arsenic related to lithology, aquifer type and groundwater vulnerability. Geographic statistical methods were used to overcome some of the geographical limitations of the Irish Environmental Protection Agency (EPA) sample database. Nearest-neighbour inverse distance weighting (IDW) and local indicator of spatial association (LISA) methods were used to estimate risk in non-sampled areas. Significant differences were also noted between different aquifer lithologies, indicating that Rhyolite, Sandstone and Shale (Greywackes), and Impure Limestone potentially presented a greater risk of elevated arsenic in groundwaters. Significant differences also occurred among aquifer types with poorly productive aquifers, locally important fractured bedrock aquifers and regionally important fissured bedrock aquifers presenting the highest potential risk of elevated arsenic. No significant differences were detected among different groundwater vulnerability groups as defined by the Geological Survey of Ireland. This research will assist management and future policy directions of groundwater resources at EU level and guide future research focused on understanding arsenic mobilisation processes to facilitate in guiding future development, testing and treatment requirements of groundwater resources. Copyright © 2016 Elsevier B.V. All rights reserved.

Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).

Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport: a land use regression modeling study

PubMed Central

2010-01-01

Background There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Methods Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Results Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches. Conclusion Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal. PMID:21083910

Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport: a land use regression modeling study.

PubMed

Adamkiewicz, Gary; Hsu, Hsiao-Hsien; Vallarino, Jose; Melly, Steven J; Spengler, John D; Levy, Jonathan I

2010-11-17

There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches. Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

Spatial variability in acoustic backscatter as an indicator of tissue homogenate production in pulsed cavitational ultrasound therapy.

PubMed

Parsons, Jessica E; Cain, Charles A; Fowlkes, J Brian

2007-03-01

Spatial variability in acoustic backscatter is investigated as a potential feedback metric for assessment of lesion morphology during cavitation-mediated mechanical tissue disruption ("histotripsy"). A 750-kHz annular array was aligned confocally with a 4.5 MHz passive backscatter receiver during ex vivo insonation of porcine myocardium. Various exposure conditions were used to elicit a range of damage morphologies and backscatter characteristics [pulse duration = 14 micros, pulse repetition frequency (PRF) = 0.07-3.1 kHz, average I(SPPA) = 22-44 kW/cm2]. Variability in backscatter spatial localization was quantified by tracking the lag required to achieve peak correlation between sequential RF A-lines received. Mean spatial variability was observed to be significantly higher when damage morphology consisted of mechanically disrupted tissue homogenate versus mechanically intact coagulation necrosis (2.35 +/- 1.59 mm versus 0.067 +/- 0.054 mm, p < 0.025). Statistics from these variability distributions were used as the basis for selecting a threshold variability level to identify the onset of homogenate formation via an abrupt, sustained increase in spatially dynamic backscatter activity. Specific indices indicative of the state of the homogenization process were quantified as a function of acoustic input conditions. The prevalence of backscatter spatial variability was observed to scale with the amount of homogenate produced for various PRFs and acoustic intensities.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies.

PubMed

Lee, Duncan; Sarran, Christophe

2015-11-01

The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

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.

Annual economic impacts of seasonal influenza on US counties: Spatial heterogeneity and patterns

PubMed Central

2012-01-01

Economic impacts of seasonal influenza vary across US counties, but little estimation has been conducted at the county level. This research computed annual economic costs of seasonal influenza for 3143 US counties based on Census 2010, identified inherent spatial patterns, and investigated cost-benefits of vaccination strategies. The computing model modified existing methods for national level estimation, and further emphasized spatial variations between counties, in terms of population size, age structure, influenza activity, and income level. Upon such a model, four vaccination strategies that prioritize different types of counties were simulated and their net returns were examined. The results indicate that the annual economic costs of influenza varied from $13.9 thousand to $957.5 million across US counties, with a median of $2.47 million. Prioritizing vaccines to counties with high influenza attack rates produces the lowest influenza cases and highest net returns. This research fills the current knowledge gap by downscaling the estimation to a county level, and adds spatial variability into studies of influenza economics and interventions. Compared to the national estimates, the presented statistics and maps will offer detailed guidance for local health agencies to fight against influenza. PMID:22594494

Spatial pattern and temporal trend of mortality due to tuberculosis 10

PubMed Central

de Queiroz, Ana Angélica Rêgo; Berra, Thaís Zamboni; Garcia, Maria Concebida da Cunha; Popolin, Marcela Paschoal; Belchior, Aylana de Souza; Yamamura, Mellina; dos Santos, Danielle Talita; Arroyo, Luiz Henrique; Arcêncio, Ricardo Alexandre

2018-01-01

ABSTRACT Objectives: To describe the epidemiological profile of mortality due to tuberculosis (TB), to analyze the spatial pattern of these deaths and to investigate the temporal trend in mortality due to tuberculosis in Northeast Brazil. Methods: An ecological study based on secondary mortality data. Deaths due to TB were included in the study. Descriptive statistics were calculated and gross mortality rates were estimated and smoothed by the Local Empirical Bayesian Method. Prais-Winsten’s regression was used to analyze the temporal trend in the TB mortality coefficients. The Kernel density technique was used to analyze the spatial distribution of TB mortality. Results: Tuberculosis was implicated in 236 deaths. The burden of tuberculosis deaths was higher amongst males, single people and people of mixed ethnicity, and the mean age at death was 51 years. TB deaths were clustered in the East, West and North health districts, and the tuberculosis mortality coefficient remained stable throughout the study period. Conclusions: Analyses of the spatial pattern and temporal trend in mortality revealed that certain areas have higher TB mortality rates, and should therefore be prioritized in public health interventions targeting the disease. PMID:29742272

Genotyping and spatial analysis of pulmonary tuberculosis and diabetes cases in the state of Veracruz, Mexico.

PubMed

Blanco-Guillot, Francles; Castañeda-Cediel, M Lucía; Cruz-Hervert, Pablo; Ferreyra-Reyes, Leticia; Delgado-Sánchez, Guadalupe; Ferreira-Guerrero, Elizabeth; Montero-Campos, Rogelio; Bobadilla-Del-Valle, Miriam; Martínez-Gamboa, Rosa Areli; Torres-González, Pedro; Téllez-Vazquez, Norma; Canizales-Quintero, Sergio; Yanes-Lane, Mercedes; Mongua-Rodríguez, Norma; Ponce-de-León, Alfredo; Sifuentes-Osornio, José; García-García, Lourdes

2018-01-01

Genotyping and georeferencing in tuberculosis (TB) have been used to characterize the distribution of the disease and occurrence of transmission within specific groups and communities. The objective of this study was to test the hypothesis that diabetes mellitus (DM) and pulmonary TB may occur in spatial and molecular aggregations. Retrospective cohort study of patients with pulmonary TB. The study area included 12 municipalities in the Sanitary Jurisdiction of Orizaba, Veracruz, México. Patients with acid-fast bacilli in sputum smears and/or Mycobacterium tuberculosis in sputum cultures were recruited from 1995 to 2010. Clinical (standardized questionnaire, physical examination, chest X-ray, blood glucose test and HIV test), microbiological, epidemiological, and molecular evaluations were carried out. Patients were considered "genotype-clustered" if two or more isolates from different patients were identified within 12 months of each other and had six or more IS6110 bands in an identical pattern, or < 6 bands with identical IS6110 RFLP patterns and spoligotype with the same spacer oligonucleotides. Residential and health care centers addresses were georeferenced. We used a Jeep hand GPS. The coordinates were transferred from the GPS files to ArcGIS using ArcMap 9.3. We evaluated global spatial aggregation of patients in IS6110-RFLP/ spoligotype clusters using global Moran´s I. Since global distribution was not random, we evaluated "hotspots" using Getis-Ord Gi* statistic. Using bivariate and multivariate analysis we analyzed sociodemographic, behavioral, clinic and bacteriological conditions associated with "hotspots". We used STATA® v13.1 for all statistical analysis. From 1995 to 2010, 1,370 patients >20 years were diagnosed with pulmonary TB; 33% had DM. The proportion of isolates that were genotyped was 80.7% (n = 1105), of which 31% (n = 342) were grouped in 91 genotype clusters with 2 to 23 patients each; 65.9% of total clusters were small (2 members) involving 35.08% of patients. Twenty three (22.7) percent of cases were classified as recent transmission. Moran`s I indicated that distribution of patients in IS6110-RFLP/spoligotype clusters was not random (Moran`s I = 0.035468, Z value = 7.0, p = 0.00). Local spatial analysis showed statistically significant spatial aggregation of patients in IS6110-RFLP/spoligotype clusters identifying "hotspots" and "coldspots". GI* statistic showed that the hotspot for spatial clustering was located in Camerino Z. Mendoza municipality; 14.6% (50/342) of patients in genotype clusters were located in a hotspot; of these, 60% (30/50) lived with DM. Using logistic regression the statistically significant variables associated with hotspots were: DM [adjusted Odds Ratio (aOR) 7.04, 95% Confidence interval (CI) 3.03-16.38] and attending the health center in Camerino Z. Mendoza (aOR18.04, 95% CI 7.35-44.28). The combination of molecular and epidemiological information with geospatial data allowed us to identify the concurrence of molecular clustering and spatial aggregation of patients with DM and TB. This information may be highly useful for TB control programs.

Genotyping and spatial analysis of pulmonary tuberculosis and diabetes cases in the state of Veracruz, Mexico

PubMed Central

Blanco-Guillot, Francles; Ferreyra-Reyes, Leticia; Delgado-Sánchez, Guadalupe; Ferreira-Guerrero, Elizabeth; Montero-Campos, Rogelio; Bobadilla-del-Valle, Miriam; Martínez-Gamboa, Rosa Areli; Torres-González, Pedro; Téllez-Vazquez, Norma; Canizales-Quintero, Sergio; Yanes-Lane, Mercedes; Mongua-Rodríguez, Norma; Ponce-de-León, Alfredo; Sifuentes-Osornio, José

2018-01-01

Background Genotyping and georeferencing in tuberculosis (TB) have been used to characterize the distribution of the disease and occurrence of transmission within specific groups and communities. Objective The objective of this study was to test the hypothesis that diabetes mellitus (DM) and pulmonary TB may occur in spatial and molecular aggregations. Material and methods Retrospective cohort study of patients with pulmonary TB. The study area included 12 municipalities in the Sanitary Jurisdiction of Orizaba, Veracruz, México. Patients with acid-fast bacilli in sputum smears and/or Mycobacterium tuberculosis in sputum cultures were recruited from 1995 to 2010. Clinical (standardized questionnaire, physical examination, chest X-ray, blood glucose test and HIV test), microbiological, epidemiological, and molecular evaluations were carried out. Patients were considered “genotype-clustered” if two or more isolates from different patients were identified within 12 months of each other and had six or more IS6110 bands in an identical pattern, or < 6 bands with identical IS6110 RFLP patterns and spoligotype with the same spacer oligonucleotides. Residential and health care centers addresses were georeferenced. We used a Jeep hand GPS. The coordinates were transferred from the GPS files to ArcGIS using ArcMap 9.3. We evaluated global spatial aggregation of patients in IS6110-RFLP/ spoligotype clusters using global Moran´s I. Since global distribution was not random, we evaluated “hotspots” using Getis-Ord Gi* statistic. Using bivariate and multivariate analysis we analyzed sociodemographic, behavioral, clinic and bacteriological conditions associated with “hotspots”. We used STATA® v13.1 for all statistical analysis. Results From 1995 to 2010, 1,370 patients >20 years were diagnosed with pulmonary TB; 33% had DM. The proportion of isolates that were genotyped was 80.7% (n = 1105), of which 31% (n = 342) were grouped in 91 genotype clusters with 2 to 23 patients each; 65.9% of total clusters were small (2 members) involving 35.08% of patients. Twenty three (22.7) percent of cases were classified as recent transmission. Moran`s I indicated that distribution of patients in IS6110-RFLP/spoligotype clusters was not random (Moran`s I = 0.035468, Z value = 7.0, p = 0.00). Local spatial analysis showed statistically significant spatial aggregation of patients in IS6110-RFLP/spoligotype clusters identifying “hotspots” and “coldspots”. GI* statistic showed that the hotspot for spatial clustering was located in Camerino Z. Mendoza municipality; 14.6% (50/342) of patients in genotype clusters were located in a hotspot; of these, 60% (30/50) lived with DM. Using logistic regression the statistically significant variables associated with hotspots were: DM [adjusted Odds Ratio (aOR) 7.04, 95% Confidence interval (CI) 3.03–16.38] and attending the health center in Camerino Z. Mendoza (aOR18.04, 95% CI 7.35–44.28). Conclusions The combination of molecular and epidemiological information with geospatial data allowed us to identify the concurrence of molecular clustering and spatial aggregation of patients with DM and TB. This information may be highly useful for TB control programs. PMID:29534104

Rethinking the polar cap: Eccentric dipole structuring of ULF power at the highest corrected geomagnetic latitudes

NASA Astrophysics Data System (ADS)

Urban, Kevin D.; Gerrard, Andrew J.; Lanzerotti, Louis J.; Weatherwax, Allan T.

2016-09-01

The day-to-day evolution and statistical features of Pc3-Pc7 band ultralow frequency (ULF) power throughout the southern polar cap suggest that the corrected geomagnetic (CGM) coordinates do not adequately organize the observed hydromagnetic spatial structure. It is shown that that the local-time distribution of ULF power at sites along CGM latitudinal parallels exhibit fundamental differences and that the CGM latitude of a site in general is not indicative of the site's projection into the magnetosphere. Thus, ULF characteristics observed at a single site in the polar cap cannot be freely generalized to other sites of similar CGM latitude but separated in magnetic local time, and the inadequacy of CGM coordinates in the polar cap has implications for conjugacy/mapping studies in general. In seeking alternative, observationally motivated systems of "polar cap latitudes," it is found that eccentric dipole (ED) coordinates have several strengths in organizing the hydromagnetic spatial structure in the polar cap region. ED latitudes appear to better classify the local-time ULF power in both magnitude and morphology and better differentiate the "deep polar cap" (where the ULF power is largely UT dependent and nearly free of local-time structure) from the "peripheral polar cap" (where near-magnetic noon pulsations dominate at lower and lower frequencies as one increases in ED latitude). Eccentric local time is shown to better align the local-time profiles in the magnetic east component over several PcX bands but worsen in the magnetic north component. It is suggested that a hybrid ED-CGM coordinate system might capture the strengths of both CGM and ED coordinates. It is shown that the local-time morphology of median ULF power at high-latitude sites is dominantly driven by where they project into the magnetosphere, which is best quantified by their proximity to the low-altitude cusp on the dayside (which is not necessarily quantified by a site's CGM latitude), and that variations in the local-time morphology at sites similar in ED latitude are due to both geographic local-time control (relative amplification or dampening by the diurnal variation in the local ionospheric conductivity) and geomagnetic coastal effects (enhanced power in a coastally mediated direction). Regardless of cause, it is emphasized that the application of CGM latitudes in the polar cap region is not entirely meaningful and likely should be dispensed with in favor of a scheme that is in better accord with the observed hydromagnetic spatial structure.

Tract-Based Spatial Statistics in Preterm-Born Neonates Predicts Cognitive and Motor Outcomes at 18 Months.

PubMed

Duerden, E G; Foong, J; Chau, V; Branson, H; Poskitt, K J; Grunau, R E; Synnes, A; Zwicker, J G; Miller, S P

2015-08-01

Adverse neurodevelopmental outcome is common in children born preterm. Early sensitive predictors of neurodevelopmental outcome such as MR imaging are needed. Tract-based spatial statistics, a diffusion MR imaging analysis method, performed at term-equivalent age (40 weeks) is a promising predictor of neurodevelopmental outcomes in children born very preterm. We sought to determine the association of tract-based spatial statistics findings before term-equivalent age with neurodevelopmental outcome at 18-months corrected age. Of 180 neonates (born at 24-32-weeks' gestation) enrolled, 153 had DTI acquired early at 32 weeks' postmenstrual age and 105 had DTI acquired later at 39.6 weeks' postmenstrual age. Voxelwise statistics were calculated by performing tract-based spatial statistics on DTI that was aligned to age-appropriate templates. At 18-month corrected age, 166 neonates underwent neurodevelopmental assessment by using the Bayley Scales of Infant Development, 3rd ed, and the Peabody Developmental Motor Scales, 2nd ed. Tract-based spatial statistics analysis applied to early-acquired scans (postmenstrual age of 30-33 weeks) indicated a limited significant positive association between motor skills and axial diffusivity and radial diffusivity values in the corpus callosum, internal and external/extreme capsules, and midbrain (P < .05, corrected). In contrast, for term scans (postmenstrual age of 37-41 weeks), tract-based spatial statistics analysis showed a significant relationship between both motor and cognitive scores with fractional anisotropy in the corpus callosum and corticospinal tracts (P < .05, corrected). Tract-based spatial statistics in a limited subset of neonates (n = 22) scanned at <30 weeks did not significantly predict neurodevelopmental outcomes. The strength of the association between fractional anisotropy values and neurodevelopmental outcome scores increased from early-to-late-acquired scans in preterm-born neonates, consistent with brain dysmaturation in this population. © 2015 by American Journal of Neuroradiology.

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

Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness

NASA Astrophysics Data System (ADS)

Aalto, J.; Karjalainen, O.; Hjort, J.; Luoto, M.

2018-05-01

Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedback.

Star formation in M 33: the radial and local relations with the gas

NASA Astrophysics Data System (ADS)

Verley, S.; Corbelli, E.; Giovanardi, C.; Hunt, L. K.

2010-02-01

Aims: In the Local Group spiral galaxy M 33, we investigate the correlation between the star formation rate (SFR) surface density, Σ_SFR, and the gas density Σ_gas (molecular, atomic, and total). We also explore whether there are other physical quantities, such as the hydrostatic pressure and dust optical depth, which establish a good correlation with Σ_SFR. Methods: We use the Hα, far-ultraviolet (FUV), and bolometric emission maps to infer the SFR locally at different spatial scales, and in radial bins using azimuthally averaged values. Most of the local analysis is done using the highest spatial resolution allowed by gas surveys, 180 pc. The Kennicutt-Schmidt (KS) law, Σ_SFR ∝ Σ_gas^n is analyzed by three statistical methods. Results: At all spatial scales, with Hα emission as a SFR tracer, the KS indices n are always steeper than those derived with the FUV and bolometric emissions. We attribute this to the lack of Hα emission in low luminosity regions where most stars form in small clusters with an incomplete initial mass function at their high mass end. For azimuthally averaged values the depletion timescale for the molecular gas is constant, and the KS index is n_H_2=1.1 ±0.1. Locally, at a spatial resolution of 180 pc, the correlation between Σ_SFR and Σ_gas is generally poor, even though it is tighter with the molecular and total gas than with the atomic gas alone. Considering only positions where the CO J=1-0 line is above the 2-σ detection threshold and taking into account uncertainties in Σ_H_2 and Σ_SFR, we obtain a steeper KS index than obtained with radial averages: n_H_2=2.22 ±0.07 (for FUV and bolometric SFR tracers), flatter than that relative to the total gas (n_Htot=2.59 ±0.05). The gas depletion timescale is therefore larger in regions of lower Σ_SFR. Lower KS indices (n_H_2=1.46 ±0.34 and n_H_2=1.12) are found using different fitting techniques, which do not account for individual position uncertainties. At coarser spatial resolutions these indices get slightly steeper, and the correlation improves. We find an almost linear relation and a better correlation coefficient between the local Σ_SFR and the ISM hydrostatic pressure or the gas volume density. This suggests that the stellar disk, gravitationally dominant with respect to the gaseous disk in M 33, has a non-marginal role in driving the SFR. However, the tight local correlation that exists between the dust optical depth and the SFR sheds light on the alternative hypothesis that the dust column density is a good tracer of the gas that is prone to star formation.

Analysis of the dependence of extreme rainfalls

NASA Astrophysics Data System (ADS)

Padoan, Simone; Ancey, Christophe; Parlange, Marc

2010-05-01

The aim of spatial analysis is to quantitatively describe the behavior of environmental phenomena such as precipitation levels, wind speed or daily temperatures. A number of generic approaches to spatial modeling have been developed[1], but these are not necessarily ideal for handling extremal aspects given their focus on mean process levels. The areal modelling of the extremes of a natural process observed at points in space is important in environmental statistics; for example, understanding extremal spatial rainfall is crucial in flood protection. In light of recent concerns over climate change, the use of robust mathematical and statistical methods for such analyses has grown in importance. Multivariate extreme value models and the class of maxstable processes [2] have a similar asymptotic motivation to the univariate Generalized Extreme Value (GEV) distribution , but providing a general approach to modeling extreme processes incorporating temporal or spatial dependence. Statistical methods for max-stable processes and data analyses of practical problems are discussed by [3] and [4]. This work illustrates methods to the statistical modelling of spatial extremes and gives examples of their use by means of a real extremal data analysis of Switzerland precipitation levels. [1] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. [2] de Haan, L and Ferreria A. (2006). Extreme Value Theory An Introduction. Springer, USA. [3] Padoan, S. A., Ribatet, M and Sisson, S. A. (2009). Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Theory & Methods. In press. [4] Davison, A. C. and Gholamrezaee, M. (2009), Geostatistics of extremes. Journal of the Royal Statistical Society, Series B. To appear.

Evaluation of Fuzzy-Logic Framework for Spatial Statistics Preserving Methods for Estimation of Missing Precipitation Data

NASA Astrophysics Data System (ADS)

El Sharif, H.; Teegavarapu, R. S.

2012-12-01

Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of a fuzzy-logic methodology for infilling missing historical daily precipitation data in preserving site and regional statistics. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of this newly proposed method in comparison to traditional data infilling techniques. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.

Evaluation and Analysis of Regional Best Management Practices in San Diego, California (USA)

NASA Astrophysics Data System (ADS)

Flint, K.; Kinoshita, A. M.

2017-12-01

In urban areas, surface water quality is often impaired due to pollutants transported by stormwater runoff. To maintain and improve surface water quality, the United States Clean Water Act (CWA) requires an evaluation of available water quality information to develop a list of impaired water bodies and establish contaminant restrictions. Structural Best Management Practices (BMPs) are designed to reduce runoff volume and/or pollutant concentrations to comply with CWA requirements. Local level policy makers and managers require an improved understanding of the costs and benefits associated with BMP installation, performance, and maintenance. The International Stormwater BMP Database (Database) is an online platform for submittal of information about existing BMPs, such as cost, design details, and statistical analysis of influent and effluent pollutant concentrations. While the Database provides an aggregation of data which supports analysis of overall BMP performance at international and national scales, the sparse spatial distribution of the data is not suitable for regional and local analysis. This research conducts an extensive review of local inventory and spatial analysis of existing permanent BMPs throughout the San Diego River watershed in California, USA. Information collected from cities within the San Diego River watershed will include BMP types, locations, dates of installation, costs, expected removal efficiencies, monitoring data, and records of maintenance. Aggregating and mapping this information will facilitate BMP evaluation. Specifically, the identification of spatial trends, inconsistencies in BMP performances, and gaps in current records. Regression analysis will provide insight into the nature and significance of correlations between BMP performance and physical characteristics such as land use, soil type, and proximity to impaired waters. This analysis will also result in a metric of relative BMP performance and will provide a basis for future predictions of BMP effectiveness. Ultimately, results from this work will provide information to local governments and agencies for prioritizing, maintaining and monitoring BMPs, and improvement of hydrologic and water quality modeling in urban systems subject to compliance.

Using NASA Earth Observing Satellites and Statistical Model Analysis to Monitor Vegetation and Habitat Rehabilitation in Southwest Virginia's Reclaimed Mine Lands

NASA Astrophysics Data System (ADS)

Tate, Z.; Dusenge, D.; Elliot, T. S.; Hafashimana, P.; Medley, S.; Porter, R. P.; Rajappan, R.; Rodriguez, P.; Spangler, J.; Swaminathan, R. S.; VanGundy, R. D.

2014-12-01

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

3D PATTERN OF BRAIN ABNORMALITIES IN FRAGILE X SYNDROME VISUALIZED USING TENSOR-BASED MORPHOMETRY

PubMed Central

Lee, Agatha D.; Leow, Alex D.; Lu, Allen; Reiss, Allan L.; Hall, Scott; Chiang, Ming-Chang; Toga, Arthur W.; Thompson, Paul M.

2007-01-01

Fragile X syndrome (FraX), a genetic neurodevelopmental disorder, results in impaired cognition with particular deficits in executive function and visuo-spatial skills. Here we report the first detailed 3D maps of the effects of the Fragile X mutation on brain structure, using tensor-based morphometry. TBM visualizes structural brain deficits automatically, without time-consuming specification of regions-of-interest. We compared 36 subjects with FraX (age: 14.66+/−1.58SD, 18 females/18 males), and 33 age-matched healthy controls (age: 14.67+/−2.2SD, 17 females/16 males), using high-dimensional elastic image registration. All 69 subjects' 3D T1-weighted brain MRIs were spatially deformed to match a high-resolution single-subject average MRI scan in ICBM space, whose geometry was optimized to produce a minimal deformation target. Maps of the local Jacobian determinant (expansion factor) were computed from the deformation fields. Statistical maps showed increased caudate (10% higher; p=0.001) and lateral ventricle volumes (19% higher; p=0.003), and trend-level parietal and temporal white matter excesses (10% higher locally; p=0.04). In affected females, volume abnormalities correlated with reduction in systemically measured levels of the fragile X mental retardation protein (FMRP; Spearman's r<−0.5 locally). Decreased FMRP correlated with ventricular expansion (p=0.042; permutation test), and anterior cingulate tissue reductions (p=0.0026; permutation test) supporting theories that FMRP is required for normal dendritic pruning in fronto-striatal-limbic pathways. No sex differences were found; findings were confirmed using traditional volumetric measures in regions of interest. Deficit patterns were replicated using Lie group statistics optimized for tensor-valued data. Investigation of how these anomalies emerge over time will accelerate our understanding of FraX and its treatment. PMID:17161622

Latent spatial models and sampling design for landscape genetics

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

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.

Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies forest: analyses using spatial autocorrelation and the mark correlation function.

PubMed

Suzuki, Satoshi N; Kachi, Naoki; Suzuki, Jun-Ichirou

2008-09-01

During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. In 1980 all trees in an 8 x 8 m plot were mapped and their height growth after the disturbance was estimated. Their mortality and growth were then recorded at 1- to 4-year intervals between 1980 and 2006. Spatial distribution patterns of trees were analysed by the pair correlation function. Spatial correlations between tree heights were analysed with a spatial autocorrelation function and the mark correlation function. The mark correlation function was able to detect a local size hierarchy that could not be detected by the spatial autocorrelation function alone. The small-scale spatial distribution pattern of trees changed from clumped to slightly regular during the 47 years. Mortality occurred in a density-dependent manner, which resulted in regular spacing between trees after 1980. The spatial autocorrelation and mark correlation functions revealed the existence of tree patches consisting of large trees at the initial stage. Development of a local size hierarchy was detected within the first decade after the disturbance, although the spatial autocorrelation was not negative. Local size hierarchies that developed persisted until 2006, and the spatial autocorrelation became negative at later stages (after about 40 years). This is the first study to detect local size hierarchies as a prelude to regular spacing using the mark correlation function. The results confirm that use of the mark correlation function together with the spatial autocorrelation function is an effective tool to analyse the development of a local size hierarchy of trees in a forest.

Response of flame thickness and propagation speed under intense turbulence in spatially developing lean premixed methane–air jet flames

DOE PAGES

Sankaran, Ramanan; Hawkes, Evatt R.; Yoo, Chun Sang; ...

2015-06-22

Direct numerical simulations of three-dimensional spatially-developing turbulent Bunsen flames were performed at three different turbulence intensities. We performed these simulations using a reduced methane–air chemical mechanism which was specifically tailored for the lean premixed conditions simulated here. A planar-jet turbulent Bunsen flame configuration was used in which turbulent preheated methane–air mixture at 0.7 equivalence ratio issued through a central jet and was surrounded by a hot laminar coflow of burned products. The turbulence characteristics at the jet inflow were selected such that combustion occured in the thin reaction zones (TRZ) regime. At the lowest turbulence intensity, the conditions fall onmore » the boundary between the TRZ regime and the corrugated flamelet regime, and progressively moved further into the TRZ regime by increasing the turbulent intensity. The data from the three simulations was analyzed to understand the effect of turbulent stirring on the flame structure and thickness. Furthermore, statistical analysis of the data showed that the thermal preheat layer of the flame was thickened due to the action of turbulence, but the reaction zone was not significantly affected. A global and local analysis of the burning velocity of the flame was performed to compare the different flames. Detailed statistical averages of the flame speed were also obtained to study the spatial dependence of displacement speed and its correlation to strain rate and curvature.« less

Predictions of the spontaneous symmetry-breaking theory for visual code completeness and spatial scaling in single-cell learning rules.

PubMed

Webber, C J

2001-05-01

This article shows analytically that single-cell learning rules that give rise to oriented and localized receptive fields, when their synaptic weights are randomly and independently initialized according to a plausible assumption of zero prior information, will generate visual codes that are invariant under two-dimensional translations, rotations, and scale magnifications, provided that the statistics of their training images are sufficiently invariant under these transformations. Such codes span different image locations, orientations, and size scales with equal economy. Thus, single-cell rules could account for the spatial scaling property of the cortical simple-cell code. This prediction is tested computationally by training with natural scenes; it is demonstrated that a single-cell learning rule can give rise to simple-cell receptive fields spanning the full range of orientations, image locations, and spatial frequencies (except at the extreme high and low frequencies at which the scale invariance of the statistics of digitally sampled images must ultimately break down, because of the image boundary and the finite pixel resolution). Thus, no constraint on completeness, or any other coupling between cells, is necessary to induce the visual code to span wide ranges of locations, orientations, and size scales. This prediction is made using the theory of spontaneous symmetry breaking, which we have previously shown can also explain the data-driven self-organization of a wide variety of transformation invariances in neurons' responses, such as the translation invariance of complex cell response.

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

PubMed

Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

2016-01-01

Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

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

PubMed

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

2018-05-09

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

Novel Image Processing Interface to Relate DSB Spatial Distribution from Immunofluorescence Foci Experiments to the State-of-the-Art Models of DNA Breakage

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Cucinotta, F. A.

2004-01-01

A recently developed software (NASARadiationTrackImage) allows a quick and automatic segmentation of foci that indicate spatial localization of specific proteins that are visualized by immunofluorescence. Of interest are the spatial and temporal distribution of foci such as gammaH2AX, a signal of the phosphorylation of a variant of the histone H2A that has been shown to correspond to DSBs, or proteins involved in DSB processing, such as ATM, Rad51, and p53, following exposures of human cells to high charge and energy (HZE) ion irradiation. Experimental data are recorded as sets of two-dimensional images in color with cells and foci of gammaH2AX, ATM, Rad51 or others shown. Different cells, levels of radiation and timing after radiation were recorded. The software allows us to calculate the number of foci per cell, overall intensity of light in foci and their spatial organization. A simple statistical model allows for testing of foci overlap (eclipse). A more complex statistical model previously known as DNAbreak simulates track structure and random chromosome geometry. It has one adjustable parameter corresponding to an average intensity of DSB creation in cubic micrometers of DNA volume per particle track or unit dose. Its limitation is the low-resolution limit both in physical space and DSB's along DNA. It works adequately on the scale of a cell and provides further insights on how the geometry of tracks and DNA affects genomic damage of the cell and subsequent repair. Future developments of the model for the description of the time evolution of DNA damage response proteins, and more robust track structure models will be discussed.

The Effect of Velocity Correlation on the Spatial Evolution of Breakthrough Curves in Heterogeneous Media

NASA Astrophysics Data System (ADS)

Massoudieh, A.; Dentz, M.; Le Borgne, T.

2017-12-01

In heterogeneous media, the velocity distribution and the spatial correlation structure of velocity for solute particles determine the breakthrough curves and how they evolve as one moves away from the solute source. The ability to predict such evolution can help relating the spatio-statistical hydraulic properties of the media to the transport behavior and travel time distributions. While commonly used non-local transport models such as anomalous dispersion and classical continuous time random walk (CTRW) can reproduce breakthrough curve successfully by adjusting the model parameter values, they lack the ability to relate model parameters to the spatio-statistical properties of the media. This in turns limits the transferability of these models. In the research to be presented, we express concentration or flux of solutes as a distribution over their velocity. We then derive an integrodifferential equation that governs the evolution of the particle distribution over velocity at given times and locations for a particle ensemble, based on a presumed velocity correlation structure and an ergodic cross-sectional velocity distribution. This way, the spatial evolution of breakthrough curves away from the source is predicted based on cross-sectional velocity distribution and the connectivity, which is expressed by the velocity transition probability density. The transition probability is specified via a copula function that can help construct a joint distribution with a given correlation and given marginal velocities. Using this approach, we analyze the breakthrough curves depending on the velocity distribution and correlation properties. The model shows how the solute transport behavior evolves from ballistic transport at small spatial scales to Fickian dispersion at large length scales relative to the velocity correlation length.

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.

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

NASA Technical Reports Server (NTRS)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

Demarcation of local neighborhoods to study relations between contextual factors and health

PubMed Central

2010-01-01

Background Several studies have highlighted the importance of collective social factors for population health. One of the major challenges is an adequate definition of the spatial units of analysis which present properties potentially related to the target outcomes. Political and administrative divisions of urban areas are the most commonly used definition, although they suffer limitations in their ability to fully express the neighborhoods as social and spatial units. Objective This study presents a proposal for defining the boundaries of local neighborhoods in Rio de Janeiro city. Local neighborhoods are constructed by means of aggregation of contiguous census tracts which are homogeneous regarding socioeconomic indicators. Methodology Local neighborhoods were created using the SKATER method (TerraView software). Criteria used for socioeconomic homogeneity were based on four census tract indicators (income, education, persons per household, and percentage of population in the 0-4-year age bracket) considering a minimum population of 5,000 people living in each local neighborhood. The process took into account the geographic boundaries between administrative neighborhoods (a political-administrative division larger than a local neighborhood, but smaller than a borough) and natural geographic barriers. Results The original 8,145 census tracts were collapsed into 794 local neighborhoods, distributed along 158 administrative neighborhoods. Local neighborhoods contained a mean of 10 census tracts, and there were an average of five local neighborhoods per administrative neighborhood. The local neighborhood units demarcated in this study are less socioeconomically heterogeneous than the administrative neighborhoods and provide a means for decreasing the well-known statistical variability of indicators based on census tracts. The local neighborhoods were able to distinguish between different areas within administrative neighborhoods, particularly in relation to squatter settlements. Conclusion Although the literature on neighborhood and health is increasing, little attention has been paid to criteria for demarcating neighborhoods. The proposed method is well-structured, available in open-access software, and easily reproducible, so we expect that new experiments will be conducted to evaluate its potential use in other settings. The method is thus a potentially important contribution to research on intra-urban differentials, particularly concerning contextual factors and their implications for different health outcomes. PMID:20587046

Demarcation of local neighborhoods to study relations between contextual factors and health.

PubMed

Santos, Simone M; Chor, Dora; Werneck, Guilherme Loureiro

2010-06-29

Several studies have highlighted the importance of collective social factors for population health. One of the major challenges is an adequate definition of the spatial units of analysis which present properties potentially related to the target outcomes. Political and administrative divisions of urban areas are the most commonly used definition, although they suffer limitations in their ability to fully express the neighborhoods as social and spatial units. This study presents a proposal for defining the boundaries of local neighborhoods in Rio de Janeiro city. Local neighborhoods are constructed by means of aggregation of contiguous census tracts which are homogeneous regarding socioeconomic indicators. Local neighborhoods were created using the SKATER method (TerraView software). Criteria used for socioeconomic homogeneity were based on four census tract indicators (income, education, persons per household, and percentage of population in the 0-4-year age bracket) considering a minimum population of 5,000 people living in each local neighborhood. The process took into account the geographic boundaries between administrative neighborhoods (a political-administrative division larger than a local neighborhood, but smaller than a borough) and natural geographic barriers. The original 8,145 census tracts were collapsed into 794 local neighborhoods, distributed along 158 administrative neighborhoods. Local neighborhoods contained a mean of 10 census tracts, and there were an average of five local neighborhoods per administrative neighborhood.The local neighborhood units demarcated in this study are less socioeconomically heterogeneous than the administrative neighborhoods and provide a means for decreasing the well-known statistical variability of indicators based on census tracts. The local neighborhoods were able to distinguish between different areas within administrative neighborhoods, particularly in relation to squatter settlements. Although the literature on neighborhood and health is increasing, little attention has been paid to criteria for demarcating neighborhoods. The proposed method is well-structured, available in open-access software, and easily reproducible, so we expect that new experiments will be conducted to evaluate its potential use in other settings. The method is thus a potentially important contribution to research on intra-urban differentials, particularly concerning contextual factors and their implications for different health outcomes.

Referring to the social performance promotes cooperation in spatial prisoner's dilemma games

NASA Astrophysics Data System (ADS)

Shigaki, Keizo; Tanimoto, Jun; Wang, Zhen; Kokubo, Satoshi; Hagishima, Aya; Ikegaya, Naoki

2012-09-01

We propose a new pairwise Fermi updating rule by considering a social average payoff when an agent copies a neighbor's strategy. In the update rule, a focal agent compares her payoff with the social average payoff of the same strategy that her pairwise opponent has. This concept might be justified by the fact that people reference global and, somehow, statistical information, not local information when imitating social behaviors. We presume several possible ways for the social average. Simulation results prove that the social average of some limited agents realizes more significant cooperation than that of the entire population.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

PubMed Central

Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

2015-01-01

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

Global-local visual biases correspond with visual-spatial orientation.

PubMed

Basso, Michael R; Lowery, Natasha

2004-02-01

Within the past decade, numerous investigations have demonstrated reliable associations of global-local visual processing biases with right and left hemisphere function, respectively (cf. Van Kleeck, 1989). Yet the relevance of these biases to other cognitive functions is not well understood. Towards this end, the present research examined the relationship between global-local visual biases and perception of visual-spatial orientation. Twenty-six women and 23 men completed a global-local judgment task (Kimchi and Palmer, 1982) and the Judgment of Line Orientation Test (JLO; Benton, Sivan, Hamsher, Varney, and Spreen, 1994), a measure of visual-spatial orientation. As expected, men had better performance on JLO. Extending previous findings, global biases were related to better visual-spatial acuity on JLO. The findings suggest that global-local biases and visual-spatial orientation may share underlying cerebral mechanisms. Implications of these findings for other visually mediated cognitive outcomes are discussed.

The Differential Role of Verbal and Spatial Working Memory in the Neural Basis of Arithmetic

PubMed Central

Demir, Özlem Ece; Prado, Jérôme; Booth, James R.

2014-01-01

We examine the relations of verbal and spatial WM ability to the neural bases of arithmetic in school-age children. We independently localize brain regions subserving verbal versus spatial representations. For multiplication, higher verbal WM ability is associated with greater recruitment of the left temporal cortex, identified by the verbal localizer. For multiplication and subtraction, higher spatial WM ability is associated with greater recruitment of right parietal cortex, identified by the spatial localizer. Depending on their WM ability, children engage different neural systems that manipulate different representations to solve arithmetic problems. PMID:25144257

Social dataset analysis and mapping tools for Risk Perception: resilience, people preparation and communication tools

NASA Astrophysics Data System (ADS)

Peters-Guarin, Graciela; Garcia, Carolina; Frigerio, Simone

2010-05-01

Perception has been identified as resource and part of the resilience of a community to disasters. Risk perception, if present, may determine the potential damage a household or community experience. Different levels of risk perception and preparedness can influence directly people's susceptibility and the way they might react in case of an emergency caused by natural hazards. In spite of the profuse literature about risk perception, works to spatially portray this feature are really scarce. The spatial relationship to danger or hazard is being recognised as an important factor of the risk equation; it can be used as a powerful tool either for better knowledge or for operational reasons (e.g. management of preventive information). Risk perception and people's awareness when displayed in a spatial format can be useful for several actors in the risk management arena. Local authorities and civil protection can better address educational activities to increase the preparation of particularly vulnerable groups of clusters of households within a community. It can also be useful for the emergency personal in order to optimally direct the actions in case of an emergency. In the framework of the Marie Curie Research Project, a Community Based Early Warning System (CBEWS) it's been developed in the Mountain Community Valtellina of Tirano, northern Italy. This community has been continuously exposed to different mass movements and floods, in particular, a large event in 1987 which affected a large portion of the valley and left 58 dead. The actual emergency plan for the study area is composed by a real time, highly detailed, decision support system. This emergency plan contains detailed instructions for the rapid deployment of civil protection and other emergency personal in case of emergency, for risk scenarios previously defined. Especially in case of a large event, where timely reaction is crucial for reducing casualties, it is important for those in charge of emergency management, to know in advance the different levels of risk perception and preparedness existing among several sectors of the population. Knowing where the most vulnerable population is located may optimize the use of resources, better direct the initial efforts and organize the evacuation and attention procedures. As part of the CBEWS, a comprehensive survey was applied in the study area to measure, among others features, the levels of risk perception, preparation and information received about natural hazards. After a statistical and direct analysis on a complete social dataset recorded, a spatial information distribution is actually in progress. Based on boundaries features (municipalities and sub-districts) of Italian Institute of Statistics (ISTAT), a local scale background has been granted (a private address level is not accessible for privacy rules so the local districts-ID inside municipality has been the detail level performed) and a spatial location of the surveyed population has been completed. The geometric component has been defined and actually it is possible to create a local distribution of social parameters derived from perception questionnaries results. A lot of raw information and social-statistical analysis offer different mirror and "visual concept" of risk perception. For this reason a concrete complete GeoDB is under working for the complete organization of the dataset. By a technical point of view the environment for data sharing is based on a complete open source web-service environment, to offer manually-made and user-friendly interface to this kind of information. Final aim is to offer different switches of dataset, using the same scale prototype and data hierarchical structure, to provide and compare social location of risk perception in the most detailed level.

Laser-diagnostic mapping of temperature and soot statistics in a 2-m diameter turbulent pool fire

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

Kearney, Sean P.; Grasser, Thomas W.

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting 2-m base diameter turbulent pool fire, burning a 10%-toluene / 90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized to obtain radial profiles of temperature and soot probability density functions (pdf) as well as estimates of temperature/soot joint statistics at three vertical heights above the surface of the methanol/toluene fuel pool. Results are presented both in the fuel vapor-dome region at ¼ base diameter and in the actively burning region at ½ and ¾ diameters above the fuel surface. The spatial evolution of themore » soot and temperature pdfs is discussed and profiles of the temperature and soot mean and rms statistics are provided. Joint temperature/soot statistics are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations.« less

Laser-diagnostic mapping of temperature and soot statistics in a 2-m diameter turbulent pool fire

DOE PAGES

Kearney, Sean P.; Grasser, Thomas W.

2017-08-10

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting 2-m base diameter turbulent pool fire, burning a 10%-toluene / 90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized to obtain radial profiles of temperature and soot probability density functions (pdf) as well as estimates of temperature/soot joint statistics at three vertical heights above the surface of the methanol/toluene fuel pool. Results are presented both in the fuel vapor-dome region at ¼ base diameter and in the actively burning region at ½ and ¾ diameters above the fuel surface. The spatial evolution of themore » soot and temperature pdfs is discussed and profiles of the temperature and soot mean and rms statistics are provided. Joint temperature/soot statistics are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations.« less

Basis adaptation and domain decomposition for steady partial differential equations with random coefficients

DOE PAGES

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

2017-09-04

In this paper, we present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support ourmore » construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Lastly, our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Estimation of the vortex length scale and intensity from two-dimensional samples

NASA Technical Reports Server (NTRS)

Reuss, D. L.; Cheng, W. P.

1992-01-01

A method is proposed for estimating flow features that influence flame wrinkling in reciprocating internal combustion engines, where traditional statistical measures of turbulence are suspect. Candidate methods were tested in a computed channel flow where traditional turbulence measures are valid and performance can be rationally evaluated. Two concepts are tested. First, spatial filtering is applied to the two-dimensional velocity distribution and found to reveal structures corresponding to the vorticity field. Decreasing the spatial-frequency cutoff of the filter locally changes the character and size of the flow structures that are revealed by the filter. Second, vortex length scale and intensity is estimated by computing the ensemble-average velocity distribution conditionally sampled on the vorticity peaks. The resulting conditionally sampled 'average vortex' has a peak velocity less than half the rms velocity and a size approximately equal to the two-point-correlation integral-length scale.

Tooth enamel oxygen "isoscapes" show a high degree of human mobility in prehistoric Britain.

PubMed

Pellegrini, Maura; Pouncett, John; Jay, Mandy; Pearson, Mike Parker; Richards, Michael P

2016-10-07

A geostatistical model to predict human skeletal oxygen isotope values (δ 18 O p ) in Britain is presented here based on a new dataset of Chalcolithic and Early Bronze Age human teeth. The spatial statistics which underpin this model allow the identification of individuals interpreted as 'non-local' to the areas where they were buried (spatial outliers). A marked variation in δ 18 O p is observed in several areas, including the Stonehenge region, the Peak District, and the Yorkshire Wolds, suggesting a high degree of human mobility. These areas, rich in funerary and ceremonial monuments, may have formed focal points for people, some of whom would have travelled long distances, ultimately being buried there. The dataset and model represent a baseline for future archaeological studies, avoiding the complex conversions from skeletal to water δ 18 O values-a process known to be problematic.

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numericalmore » experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Memory effects in active particles with exponentially correlated propulsion

NASA Astrophysics Data System (ADS)

Sandford, Cato; Grosberg, Alexander Y.

2018-01-01

The Ornstein-Uhlenbeck particle (OUP) model imagines a microscopic swimmer propelled by an active force which is correlated with itself on a finite time scale. Here we investigate the influence of external potentials on an ideal suspension of OUPs, in both one and two spatial dimensions, with particular attention paid to the pressure exerted on "confining walls." We employ a mathematical connection between the local density of OUPs and the statistics of their propulsion force to demonstrate the existence of an equation of state in one dimension. In higher dimensions we show that active particles generate a nonconservative force field in the surrounding medium. A simplified far-from-equilibrium model is proposed to account for OUP behavior in the vicinity of potentials. Building on this, we interpret simulations of OUPs in more complicated situations involving asymmetrical and spatially curved potentials, and characterize the resulting inhomogeneous stresses in terms of competing active length scales.

Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

NASA Technical Reports Server (NTRS)

Famiglietti, J. S.; Wood, Eric F.

1993-01-01

A land surface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of land surface-atmosphere interactions.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

A new framework for estimating return levels using regional frequency analysis

NASA Astrophysics Data System (ADS)

Winter, Hugo; Bernardara, Pietro; Clegg, Georgina

2017-04-01

We propose a new framework for incorporating more spatial and temporal information into the estimation of extreme return levels. Currently, most studies use extreme value models applied to data from a single site; an approach which is inefficient statistically and leads to return level estimates that are less physically realistic. We aim to highlight the benefits that could be obtained by using methodology based upon regional frequency analysis as opposed to classic single site extreme value analysis. This motivates a shift in thinking, which permits the evaluation of local and regional effects and makes use of the wide variety of data that are now available on high temporal and spatial resolutions. The recent winter storms over the UK during the winters of 2013-14 and 2015-16, which have caused wide-ranging disruption and damaged important infrastructure, provide the main motivation for the current work. One of the most impactful natural hazards is flooding, which is often initiated by extreme precipitation. In this presentation, we focus on extreme rainfall, but shall discuss other meteorological variables alongside potentially damaging hazard combinations. To understand the risks posed by extreme precipitation, we need reliable statistical models which can be used to estimate quantities such as the T-year return level, i.e. the level which is expected to be exceeded once every T-years. Extreme value theory provides the main collection of statistical models that can be used to estimate the risks posed by extreme precipitation events. Broadly, at a single site, a statistical model is fitted to exceedances of a high threshold and the model is used to extrapolate to levels beyond the range of the observed data. However, when we have data at many sites over a spatial domain, fitting a separate model for each separate site makes little sense and it would be better if we could incorporate all this information to improve the reliability of return level estimates. Here, we use the regional frequency analysis approach to define homogeneous regions which are affected by the same storms. Extreme value models are then fitted to the data pooled from across a region. We find that this approach leads to more spatially consistent return level estimates with reduced uncertainty bounds.

Lost in the Forest, Stuck in the Trees: Dispositional Global/Local Bias Is Resistant to Exposure to High and Low Spatial Frequencies

PubMed Central

Dale, Gillian; Arnell, Karen M.

2014-01-01

Visual stimuli can be perceived at a broad, “global” level, or at a more focused, “local” level. While research has shown that many individuals demonstrate a preference for global information, there are large individual differences in the degree of global/local bias, such that some individuals show a large global bias, some show a large local bias, and others show no bias. The main purpose of the current study was to examine whether these dispositional differences in global/local bias could be altered through various manipulations of high/low spatial frequency. Through 5 experiments, we examined various measures of dispositional global/local bias and whether performance on these measures could be altered by manipulating previous exposure to high or low spatial frequency information (with high/low spatial frequency faces, gratings, and Navon letters). Ultimately, there was little evidence of change from pre-to-post manipulation on the dispositional measures, and dispositional global/local bias was highly reliable pre- to post-manipulation. The results provide evidence that individual differences in global/local bias or preference are relatively resistant to exposure to spatial frequency information, and suggest that the processing mechanisms underlying high/low spatial frequency use and global/local bias may be more independent than previously thought. PMID:24992321

Soil moisture mapping at Bubnow Wetland using L-band radiometer (ELBARA III)

NASA Astrophysics Data System (ADS)

Łukowski, Mateusz; Schwank, Mike; Szlązak, Radosław; Wiesmann, Andreas; Marczewski, Wojciech; Usowicz, Bogusław; Usowicz, Jerzy; Rojek, Edyta; Werner, Charles

2016-04-01

The study of soil moisture is a scientific challenge. Not only because of large diversity of soils and differences in their water content, but also due to the difficulty of measuring, especially in large scale. On this field of interest several methods to determine the content of water in soil exists. The basic and referential is gravimetric method, which is accurate, but suitable only for small spatial scales and time-consuming. Indirect methods are faster, but need to be validated, for example those based on dielectric properties of materials (e.g. time domain reflectometry - TDR) or made from distance (remote), like brightness temperature measurements. Remote sensing of soil moisture can be performed locally (from towers, drones, planes etc.) or globally (satellites). These techniques can complement and help to verify different models and assumptions. In our studies, we applied spatial statistics to local soil moisture mapping using ELBARA III (ESA L-band radiometer, 1.4 GHz) mounted on tower (6.5 meter height). Our measurements were carried out in natural Bubnow Wetland, near Polesie National Park (Eastern Poland), during spring time. This test-site had been selected because it is representative for one of the biggest wetlands in Europe (1400 km2), called "Western Polesie", localized in Ukraine, Poland and Belarus. We have investigated Bubnow for almost decade, using meteorological and soil moisture stations, conducting campaigns of hand-held measurements and collecting soil samples. Now, due to the possibility of rotation at different incidence angles (as in previous ELBARA systems) and the new azimuth tracking capabilities, we obtained brightness temperature data not only at different distances from the tower, but also around it, in footprints containing different vegetation and soil types. During experiment we collected data at area about 450 m2 by rotating ELBARA's antenna 5-175° in horizontal and 30-70° in vertical plane. This type of approach allows combining multiple independent measurements (performed nearly simultaneously) to one consistent soil moisture map. Spatial statistics helps with correcting blind spots or distortions causes by assembly elements, especially on corners of ELBARA's tower. Moreover, using this technique we can observe distribution of soil moisture with time dependency. In order to validate our data, the results were compared with measurements obtained by means of the TDR method. The presented approach enables better understanding the soil moisture spatial distribution over a particular local area of interests, before extending soil water assessments on larger areas. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States) 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp

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

Application of hierarchical clustering method to classify of space-time rainfall patterns

NASA Astrophysics Data System (ADS)

Yu, Hwa-Lung; Chang, Tu-Je

2010-05-01

Understanding the local precipitation patterns is essential to the water resources management and flooding mitigation. The precipitation patterns can vary in space and time depending upon the factors from different spatial scales such as local topological changes and macroscopic atmospheric circulation. The spatiotemporal variation of precipitation in Taiwan is significant due to its complex terrain and its location at west pacific and subtropical area, where is the boundary between the pacific ocean and Asia continent with the complex interactions among the climatic processes. This study characterizes local-scale precipitation patterns by classifying the historical space-time precipitation records. We applied the hierarchical ascending clustering method to analyze the precipitation records from 1960 to 2008 at the six rainfall stations located in Lan-yang catchment at the northeast of the island. Our results identify the four primary space-time precipitation types which may result from distinct driving forces from the changes of atmospheric variables and topology at different space-time scales. This study also presents an important application of the statistical downscaling to combine large-scale upper-air circulation with local space-time precipitation patterns.

Theory of activated glassy relaxation, mobility gradients, surface diffusion, and vitrification in free standing thin films

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

Mirigian, Stephen, E-mail: kschweiz@illinois.edu, E-mail: smirigian@gmail.com; Schweizer, Kenneth S., E-mail: kschweiz@illinois.edu, E-mail: smirigian@gmail.com

2015-12-28

We have constructed a quantitative, force level, statistical mechanical theory for how confinement in free standing thin films introduces a spatial mobility gradient of the alpha relaxation time as a function of temperature, film thickness, and location in the film. The crucial idea is that relaxation speeds up due to the reduction of both near-surface barriers associated with the loss of neighbors in the local cage and the spatial cutoff and dynamical softening near the vapor interface of the spatially longer range collective elasticity cost for large amplitude hopping. These two effects are fundamentally coupled. Quantitative predictions are made formore » how an apparent glass temperature depends on the film thickness and experimental probe technique, the emergence of a two-step decay and mobile layers in time domain measurements, signatures of confinement in frequency-domain dielectric loss experiments, the dependence of film-averaged relaxation times and dynamic fragility on temperature and film thickness, surface diffusion, and the relationship between kinetic experiments and pseudo-thermodynamic measurements such as ellipsometry.« less

Theory of activated glassy relaxation, mobility gradients, surface diffusion, and vitrification in free standing thin films.

PubMed

Mirigian, Stephen; Schweizer, Kenneth S

2015-12-28

We have constructed a quantitative, force level, statistical mechanical theory for how confinement in free standing thin films introduces a spatial mobility gradient of the alpha relaxation time as a function of temperature, film thickness, and location in the film. The crucial idea is that relaxation speeds up due to the reduction of both near-surface barriers associated with the loss of neighbors in the local cage and the spatial cutoff and dynamical softening near the vapor interface of the spatially longer range collective elasticity cost for large amplitude hopping. These two effects are fundamentally coupled. Quantitative predictions are made for how an apparent glass temperature depends on the film thickness and experimental probe technique, the emergence of a two-step decay and mobile layers in time domain measurements, signatures of confinement in frequency-domain dielectric loss experiments, the dependence of film-averaged relaxation times and dynamic fragility on temperature and film thickness, surface diffusion, and the relationship between kinetic experiments and pseudo-thermodynamic measurements such as ellipsometry.

Detecting defective electrical components in heterogeneous infra-red images by spatial control charts

NASA Astrophysics Data System (ADS)

Jamshidieini, Bahman; Fazaee, Reza

2016-05-01

Distribution network components connect machines and other loads to electrical sources. If resistance or current of any component is more than specified range, its temperature may exceed the operational limit which can cause major problems. Therefore, these defects should be found and eliminated according to their severity. Although infra-red cameras have been used for inspection of electrical components, maintenance prioritization of distribution cubicles is mostly based on personal perception and lack of training data prevents engineers from developing image processing methods. New research on the spatial control chart encouraged us to use statistical approaches instead of the pattern recognition for the image processing. In the present study, a new scanning pattern which can tolerate heavy autocorrelation among adjacent pixels within infra-red image was developed and for the first time combination of kernel smoothing, spatial control charts and local robust regression were used for finding defects within heterogeneous infra-red images of old distribution cubicles. This method does not need training data and this advantage is crucially important when the training data is not available.

Multi-Scale Modeling to Improve Single-Molecule, Single-Cell Experiments

NASA Astrophysics Data System (ADS)

Munsky, Brian; Shepherd, Douglas

2014-03-01

Single-cell, single-molecule experiments are producing an unprecedented amount of data to capture the dynamics of biological systems. When integrated with computational models, observations of spatial, temporal and stochastic fluctuations can yield powerful quantitative insight. We concentrate on experiments that localize and count individual molecules of mRNA. These high precision experiments have large imaging and computational processing costs, and we explore how improved computational analyses can dramatically reduce overall data requirements. In particular, we show how analyses of spatial, temporal and stochastic fluctuations can significantly enhance parameter estimation results for small, noisy data sets. We also show how full probability distribution analyses can constrain parameters with far less data than bulk analyses or statistical moment closures. Finally, we discuss how a systematic modeling progression from simple to more complex analyses can reduce total computational costs by orders of magnitude. We illustrate our approach using single-molecule, spatial mRNA measurements of Interleukin 1-alpha mRNA induction in human THP1 cells following stimulation. Our approach could improve the effectiveness of single-molecule gene regulation analyses for many other process.

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis

PubMed Central

Chen, Zhe; Gao, Xiaorong; Li, Yuanqing; Brown, Emery N.; Gao, Shangkai

2015-01-01

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task. PMID:26005228

Towards sub-nanometer real-space observation of spin and orbital magnetism at the Fe/MgO interface

PubMed Central

Thersleff, Thomas; Muto, Shunsuke; Werwiński, Mirosław; Spiegelberg, Jakob; Kvashnin, Yaroslav; Hjӧrvarsson, Björgvin; Eriksson, Olle; Rusz, Ján; Leifer, Klaus

2017-01-01

While the performance of magnetic tunnel junctions based on metal/oxide interfaces is determined by hybridization, charge transfer, and magnetic properties at the interface, there are currently only limited experimental techniques with sufficient spatial resolution to directly observe these effects simultaneously in real-space. In this letter, we demonstrate an experimental method based on Electron Magnetic Circular Dichroism (EMCD) that will allow researchers to simultaneously map magnetic transitions and valency in real-space over interfacial cross-sections with sub-nanometer spatial resolution. We apply this method to an Fe/MgO bilayer system, observing a significant enhancement in the orbital to spin moment ratio that is strongly localized to the interfacial region. Through the use of first-principles calculations, multivariate statistical analysis, and Electron Energy-Loss Spectroscopy (EELS), we explore the extent to which this enhancement can be attributed to emergent magnetism due to structural confinement at the interface. We conclude that this method has the potential to directly visualize spin and orbital moments at buried interfaces in magnetic systems with unprecedented spatial resolution. PMID:28338011

Towards sub-nanometer real-space observation of spin and orbital magnetism at the Fe/MgO interface

NASA Astrophysics Data System (ADS)

Thersleff, Thomas; Muto, Shunsuke; Werwiński, Mirosław; Spiegelberg, Jakob; Kvashnin, Yaroslav; Hjӧrvarsson, Björgvin; Eriksson, Olle; Rusz, Ján; Leifer, Klaus

2017-03-01

While the performance of magnetic tunnel junctions based on metal/oxide interfaces is determined by hybridization, charge transfer, and magnetic properties at the interface, there are currently only limited experimental techniques with sufficient spatial resolution to directly observe these effects simultaneously in real-space. In this letter, we demonstrate an experimental method based on Electron Magnetic Circular Dichroism (EMCD) that will allow researchers to simultaneously map magnetic transitions and valency in real-space over interfacial cross-sections with sub-nanometer spatial resolution. We apply this method to an Fe/MgO bilayer system, observing a significant enhancement in the orbital to spin moment ratio that is strongly localized to the interfacial region. Through the use of first-principles calculations, multivariate statistical analysis, and Electron Energy-Loss Spectroscopy (EELS), we explore the extent to which this enhancement can be attributed to emergent magnetism due to structural confinement at the interface. We conclude that this method has the potential to directly visualize spin and orbital moments at buried interfaces in magnetic systems with unprecedented spatial resolution.

BaTMAn: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2016-12-01

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

Statistical Analysis of Sport Movement Observations: the Case of Orienteering

NASA Astrophysics Data System (ADS)

Amouzandeh, K.; Karimipour, F.

2017-09-01

Study of movement observations is becoming more popular in several applications. Particularly, analyzing sport movement time series has been considered as a demanding area. However, most of the attempts made on analyzing movement sport data have focused on spatial aspects of movement to extract some movement characteristics, such as spatial patterns and similarities. This paper proposes statistical analysis of sport movement observations, which refers to analyzing changes in the spatial movement attributes (e.g. distance, altitude and slope) and non-spatial movement attributes (e.g. speed and heart rate) of athletes. As the case study, an example dataset of movement observations acquired during the "orienteering" sport is presented and statistically analyzed.

Spatial synchrony of local populations has increased in association with the recent Northern Hemisphere climate trend.

PubMed

Post, Eric; Forchhammer, Mads C

2004-06-22

According to ecological theory, populations whose dynamics are entrained by environmental correlation face increased extinction risk as environmental conditions become more synchronized spatially. This prediction is highly relevant to the study of ecological consequences of climate change. Recent empirical studies have indicated, for example, that large-scale climate synchronizes trophic interactions and population dynamics over broad spatial scales in freshwater and terrestrial systems. Here, we present an analysis of century-scale, spatially replicated data on local weather and the population dynamics of caribou in Greenland. Our results indicate that spatial autocorrelation in local weather has increased with large-scale climatic warming. This increase in spatial synchrony of environmental conditions has been matched, in turn, by an increase in the spatial synchrony of local caribou populations toward the end of the 20th century. Our results indicate that spatial synchrony in environmental conditions and the populations influenced by them are highly variable through time and can increase with climatic warming. We suggest that if future warming can increase population synchrony, it may also increase extinction risk.

Effect of spatial averaging on multifractal properties of meteorological time series

NASA Astrophysics Data System (ADS)

Hoffmann, Holger; Baranowski, Piotr; Krzyszczak, Jaromir; Zubik, Monika

2016-04-01

Introduction The process-based models for large-scale simulations require input of agro-meteorological quantities that are often in the form of time series of coarse spatial resolution. Therefore, the knowledge about their scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and non-stationarities. Here we assess how spatially aggregating meteorological data to coarser resolutions affects the data's temporal scaling properties. While it is known that spatial aggregation may affect spatial data properties (Hoffmann et al., 2015), it is unknown how it affects temporal data properties. Therefore, the objective of this study was to characterize the aggregation effect (AE) with regard to both temporal and spatial input data properties considering scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological time series through multifractal detrended fluctuation analysis (MFDFA). Materials and Methods Time series coming from years 1982-2011 were spatially averaged from 1 to 10, 25, 50 and 100 km resolution to assess the impact of spatial aggregation. Daily minimum, mean and maximum air temperature (2 m), precipitation, global radiation, wind speed and relative humidity (Zhao et al., 2015) were used. To reveal the multifractal structure of the time series, we used the procedure described in Baranowski et al. (2015). The diversity of the studied multifractals was evaluated by the parameters of time series spectra. In order to analyse differences in multifractal properties to 1 km resolution grids, data of coarser resolutions was disaggregated to 1 km. Results and Conclusions Analysing the spatial averaging on multifractal properties we observed that spatial patterns of the multifractal spectrum (MS) of all meteorological variables differed from 1 km grids and MS-parameters were biased by -29.1 % (precipitation; width of MS) up to >4 % (min. Temperature, Radiation; asymmetry of MS). Also, the spatial variability of MS parameters was strongly affected at the highest aggregation (100 km). Obtained results confirm that spatial data aggregation may strongly affect temporal scaling properties. This should be taken into account when upscaling for large-scale studies. Acknowledgements The study was conducted within FACCE MACSUR. Please see Baranowski et al. (2015) for details on funding. References Baranowski, P., Krzyszczak, J., Sławiński, C. et al. (2015). Climate Research 65, 39-52. Hoffman, H., G. Zhao, L.G.J. Van Bussel et al. (2015). Climate Research 65, 53-69. Zhao, G., Siebert, S., Rezaei E. et al. (2015). Agricultural and Forest Meteorology 200, 156-171.

Modeling the influence of local environmental factors on malaria transmission in Benin and its implications for cohort study.

PubMed

Cottrell, Gilles; Kouwaye, Bienvenue; Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission--even at a very local scale--is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors.As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages.This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics.

Modeling the Influence of Local Environmental Factors on Malaria Transmission in Benin and Its Implications for Cohort Study

PubMed Central

Pierrat, Charlotte; le Port, Agnès; Bouraïma, Aziz; Fonton, Noël; Hounkonnou, Mahouton Norbert; Massougbodji, Achille; Corbel, Vincent; Garcia, André

2012-01-01

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission—even at a very local scale—is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors. As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages. This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics. PMID:22238582

Statespace geometry of puff formation in pipe flow

NASA Astrophysics Data System (ADS)

Budanur, Nazmi Burak; Hof, Bjoern

2017-11-01

Localized patches of chaotically moving fluid known as puffs play a central role in the transition to turbulence in pipe flow. Puffs coexist with the laminar flow and their large-scale dynamics sets the critical Reynolds number: When the rate of puff splitting exceeds that of decaying, turbulence in a long pipe becomes sustained in a statistical sense. Since puffs appear despite the linear stability of the Hagen-Poiseuille flow, one expects them to emerge from the bifurcations of finite-amplitude solutions of Navier-Stokes equations. In numerical simulations of pipe flow, Avila et al., discovered a pair of streamwise localized relative periodic orbits, which are time-periodic solutions with spatial drifts. We combine symmetry reduction and Poincaré section methods to compute the unstable manifolds of these orbits, revealing statespace structures associated with different stages of puff formation.

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis.

PubMed

Foong, Shaohui; Sun, Zhenglong

2016-08-12

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison.

Self-Replication of Localized Vegetation Patches in Scarce Environments

NASA Astrophysics Data System (ADS)

Bordeu, Ignacio; Clerc, Marcel G.; Couteron, Piere; Lefever, René; Tlidi, Mustapha

2016-09-01

Desertification due to climate change and increasing drought periods is a worldwide problem for both ecology and economy. Our ability to understand how vegetation manages to survive and propagate through arid and semiarid ecosystems may be useful in the development of future strategies to prevent desertification, preserve flora—and fauna within—or even make use of scarce resources soils. In this paper, we study a robust phenomena observed in semi-arid ecosystems, by which localized vegetation patches split in a process called self-replication. Localized patches of vegetation are visible in nature at various spatial scales. Even though they have been described in literature, their growth mechanisms remain largely unexplored. Here, we develop an innovative statistical analysis based on real field observations to show that patches may exhibit deformation and splitting. This growth mechanism is opposite to the desertification since it allows to repopulate territories devoid of vegetation. We investigate these aspects by characterizing quantitatively, with a simple mathematical model, a new class of instabilities that lead to the self-replication phenomenon observed.

Temporal Characteristics of Electron Flux Events at Geosynchronous Orbit

NASA Astrophysics Data System (ADS)

Olson, D. K.; Larsen, B.; Henderson, M. G.

2017-12-01

Geosynchronous satellites such as the LANL-GEO fleet are exposed to hazardous conditions when they encounter regions of hot, intense plasma such as that from the plasma sheet. These conditions can lead to the build-up of charge on the surface of a spacecraft, with undesired, and often dangerous, side effects. Observation of electron flux levels at geosynchronous orbit (GEO) with multiple satellites provides a unique view of plasma sheet access to that region. Flux "events", or periods when fluxes are elevated continuously above the LANL-GEO spacecraft charging threshold, can be characterized by duration in two dimensions: a spatial dimension of local time, describing the duration of an event from the perspective of a single spacecraft, and a temporal dimension describing the duration in time in which high energy plasma sheet particles have access to geosynchronous orbit. We examine the statistical properties of the temporal duration of 8 keV electron flux events at geosynchronous orbit over a twelve-year period. These results, coupled with the spatial duration characteristics, provide the key information needed to formulate a statistical model for forecasting the electron flux conditions at GEO that are correlated with LANL-GEO surface charging. Forecasting models are an essential component to understanding space weather and mitigating the dangers of surface charging on our satellites. We also examine the correlation of flux event durations with solar wind parameters and geomagnetic indices, identifying the data needed to improve upon a statistical forecasting model

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

NASA Astrophysics Data System (ADS)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of discretization levels, and the initial conditions.

Analysis of thrips distribution: application of spatial statistics and Kriging

Treesearch

John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

1991-01-01

Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation.

PubMed

Gething, Peter W; Patil, Anand P; Hay, Simon I

2010-04-01

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty--the fidelity of predictions at each mapped pixel--but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

Quality evaluation of different fusion techniques applied on Worldview-2 data

NASA Astrophysics Data System (ADS)

Vaiopoulos, Aristides; Nikolakopoulos, Konstantinos G.

2015-10-01

In the current study a Worldview-2 image was used for fusion quality assessment. The bundle image was collected on July 2014 over Araxos area in Western Peloponnese. Worldview-2 is the first satellite that collects at the same time a panchromatic (Pan) image and 8 band multispectral (MS) image. The Pan data have a spatial resolution of 0.46m while the MS data have a spatial resolution of 1.84m. In contrary to the respective Pan band of Ikonos and Quickbird that range between 0.45 and 0.90 micrometers the Worldview Pan band is narrower and ranges between 0.45 and 0.8 micrometers. The MS bands include four conventional visible and near-infrared bands common to multispectral satellites like Ikonos Quickbird, Geoeye Landsat-7 etc., and four new bands. Thus, it is quite interesting to investigate the assessment of commonly used fusion algorithms with Worldview-2 data. Twelve fusion techniques and more especially the Ehlers, Gram-Schmidt, Color Normalized, High Pass Filter, Hyperspherical Color Space, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, Pansharp2, PCA and Wavelet were used for the fusion of Worldview-2 panchromatic and multispectral data. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q and entropy difference were examined and the results are presented. The quality control was evaluated both in spectral and spatial domain.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Schaaf, Crystal B.; Platnick, Steven

2006-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. , Over five years of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface s radiative characteristics. However, roughly 30% of the global land surface, on an annual equal-angle basis, is obscured due to persistent and transient cloud cover, while another 207% is obscured due to ephemeral and seasonal snow effects. This precludes the MOD43B3 albedo products from being directly used in some remote sensing and ground-based applications, climate models, and global change research projects. To provide researchers with the requisite spatially complete global snow-free land surface albedo dataset, an ecosystem-dependent temporal interpolation technique was developed to fill missing or lower quality data and snow covered values from the official MOD43B3 dataset with geophysically realistic values. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data.

Vulnerability to the transmission of human visceral leishmaniasis in a Brazilian urban area

PubMed Central

de Toledo, Celina Roma Sánchez; de Almeida, Andréa Sobral; Chaves, Sergio Augusto de Miranda; Sabroza, Paulo Chagastelles; Toledo, Luciano Medeiros; Caldas, Jefferson Pereira

2017-01-01

ABSTRACT OBJECTIVE To analyze the determinants for the occurrence of human visceral leishmaniasis linked to the conditions of vulnerability. METHODS This is an ecological study, whose spatial analysis unit was the Territorial Analysis Unit in Araguaína, State of Tocantins, Brazil, from 2007 to 2012. We have carried out an analysis of the sociodemographic and urban infrastructure situation of the municipality. Normalized primary indicators were calculated and used to construct the indicators of vulnerability of the social structure, household structure, and urban infrastructure. From them, we have composed a vulnerability index. Kernel density estimation was used to evaluate the density of cases of human visceral leishmaniasis, based on the coordinates of the cases. Bivariate global Moran’s I was used to verify the existence of spatial autocorrelation between the incidence of human visceral leishmaniasis and the indicators and index of vulnerability. Bivariate local Moran’s I was used to identify spatial clusters. RESULTS We have observed a pattern of centrifugal spread of human visceral leishmaniasis in the municipality, where outbreaks of the disease have progressively reached central and peri-urban areas. There has been no correlation between higher incidences of human visceral leishmaniasis and worse living conditions. Statistically significant clusters have been observed between the incidences of human visceral leishmaniasis in both periods analyzed (2007 to 2009 and 2010 to 2012) and the indicators and index of vulnerability. CONCLUSIONS The environment in circumscribed areas helps as protection factor or increases the local vulnerability to the occurrence of human visceral leishmaniasis. The use of methodology that analyzes the conditions of life of the population and the spatial distribution of human visceral leishmaniasis is essential to identify the most vulnerable areas to the spread/maintenance of the disease. PMID:28513764

Temporal changes of spatial soil moisture patterns: controlling factors explained with a multidisciplinary approach

NASA Astrophysics Data System (ADS)

Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen

2016-04-01

Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under different hydrologic conditions and the factors controlling the temporal variability of the ECa-soil moisture relationship. The approach provided valuable insight into the time-varying contribution of local and nonlocal factors to the characteristic spatial patterns of soil moisture and the transition mechanisms. The spatial organization of soil moisture was controlled by different processes in different soil horizons, and the topsoil's moisture did not mirror processes that take place within the soil profile. Results show that, for the Schäfertal hillslope site which is presumed to be representative for non-intensively managed soils with moderate clay content, local soil properties (e.g., soil texture and porosity) are the major control on the spatial pattern of ECa. In contrast, the ECa-soil moisture relationship is small and varies over time indicating that ECa is not a good proxy for soil moisture estimation at the investigated site.Occasionally observed stronger correlations between ECa and soil moisture may be explained by background dependencies of ECa to other state variables such as pore water electrical conductivity. The results will help to improve conceptual understanding for hydrological model studies at similar or smaller scales, and to transfer observation concepts and process understanding to larger or less instrumented sites, as well as to constrain the use of EMI-based ECa data for hydrological applications.

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

Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer.

PubMed

van Sloun, Ruud J G; Demi, Libertario; Postema, Arnoud W; Jmch De La Rosette, Jean; Wijkstra, Hessel; Mischi, Massimo

2017-03-01

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.

Models for inference in dynamic metacommunity systems

USGS Publications Warehouse

Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias

2010-01-01

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

Spatial trends in Pearson Type III statistical parameters

USGS Publications Warehouse

Lichty, R.W.; Karlinger, M.R.

1995-01-01

Spatial trends in the statistical parameters (mean, standard deviation, and skewness coefficient) of a Pearson Type III distribution of the logarithms of annual flood peaks for small rural basins (less than 90 km2) are delineated using a climate factor CT, (T=2-, 25-, and 100-yr recurrence intervals), which quantifies the effects of long-term climatic data (rainfall and pan evaporation) on observed T-yr floods. Maps showing trends in average parameter values demonstrate the geographically varying influence of climate on the magnitude of Pearson Type III statistical parameters. The spatial trends in variability of the parameter values characterize the sensitivity of statistical parameters to the interaction of basin-runoff characteristics (hydrology) and climate. -from Authors

Simulation of spatially evolving turbulence and the applicability of Taylor's hypothesis in compressible flow

NASA Technical Reports Server (NTRS)

Lee, Sangsan; Lele, Sanjiva K.; Moin, Parviz

1992-01-01

For the numerical simulation of inhomogeneous turbulent flows, a method is developed for generating stochastic inflow boundary conditions with a prescribed power spectrum. Turbulence statistics from spatial simulations using this method with a low fluctuation Mach number are in excellent agreement with the experimental data, which validates the procedure. Turbulence statistics from spatial simulations are also compared to those from temporal simulations using Taylor's hypothesis. Statistics such as turbulence intensity, vorticity, and velocity derivative skewness compare favorably with the temporal simulation. However, the statistics of dilatation show a significant departure from those obtained in the temporal simulation. To directly check the applicability of Taylor's hypothesis, space-time correlations of fluctuations in velocity, vorticity, and dilatation are investigated. Convection velocities based on vorticity and velocity fluctuations are computed as functions of the spatial and temporal separations. The profile of the space-time correlation of dilatation fluctuations is explained via a wave propagation model.

Fractional power-law spatial dispersion in electrodynamics

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

Tarasov, Vasily E., E-mail: tarasov@theory.sinp.msu.ru; Departamento de Análisis Matemático, Universidad de La Laguna, 38271 La Laguna, Tenerife; Trujillo, Juan J., E-mail: jtrujill@ullmat.es

2013-07-15

Electric fields in non-local media with power-law spatial dispersion are discussed. Equations involving a fractional Laplacian in the Riesz form that describe the electric fields in such non-local media are studied. The generalizations of Coulomb’s law and Debye’s screening for power-law non-local media are characterized. We consider simple models with anomalous behavior of plasma-like media with power-law spatial dispersions. The suggested fractional differential models for these plasma-like media are discussed to describe non-local properties of power-law type. -- Highlights: •Plasma-like non-local media with power-law spatial dispersion. •Fractional differential equations for electric fields in the media. •The generalizations of Coulomb’s lawmore » and Debye’s screening for the media.« less

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

PubMed Central

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

2006-01-01

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

Effects of Heterogeniety on Spatial Pattern Analysis of Wild Pistachio Trees in Zagros Woodlands, Iran

NASA Astrophysics Data System (ADS)

Erfanifard, Y.; Rezayan, F.

2014-10-01

Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0-50 m than actually existed and an aggregation at scales of 150-200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

Asymptotic modal analysis and statistical energy analysis

NASA Technical Reports Server (NTRS)

Dowell, Earl H.

1992-01-01

Asymptotic Modal Analysis (AMA) is a method which is used to model linear dynamical systems with many participating modes. The AMA method was originally developed to show the relationship between statistical energy analysis (SEA) and classical modal analysis (CMA). In the limit of a large number of modes of a vibrating system, the classical modal analysis result can be shown to be equivalent to the statistical energy analysis result. As the CMA result evolves into the SEA result, a number of systematic assumptions are made. Most of these assumptions are based upon the supposition that the number of modes approaches infinity. It is for this reason that the term 'asymptotic' is used. AMA is the asymptotic result of taking the limit of CMA as the number of modes approaches infinity. AMA refers to any of the intermediate results between CMA and SEA, as well as the SEA result which is derived from CMA. The main advantage of the AMA method is that individual modal characteristics are not required in the model or computations. By contrast, CMA requires that each modal parameter be evaluated at each frequency. In the latter, contributions from each mode are computed and the final answer is obtained by summing over all the modes in the particular band of interest. AMA evaluates modal parameters only at their center frequency and does not sum the individual contributions from each mode in order to obtain a final result. The method is similar to SEA in this respect. However, SEA is only capable of obtaining spatial averages or means, as it is a statistical method. Since AMA is systematically derived from CMA, it can obtain local spatial information as well.

Intercepting a sound without vision

PubMed Central

Vercillo, Tiziana; Tonelli, Alessia; Gori, Monica

2017-01-01

Visual information is extremely important to generate internal spatial representations. In the auditory modality, the absence of visual cues during early infancy does not preclude the development of some spatial strategies. However, specific spatial abilities might result impaired. In the current study, we investigated the effect of early visual deprivation on the ability to localize static and moving auditory stimuli by comparing sighted and early blind individuals’ performance in different spatial tasks. We also examined perceptual stability in the two groups of participants by matching localization accuracy in a static and a dynamic head condition that involved rotational head movements. Sighted participants accurately localized static and moving sounds. Their localization ability remained unchanged after rotational movements of the head. Conversely, blind participants showed a leftward bias during the localization of static sounds and a little bias for moving sounds. Moreover, head movements induced a significant bias in the direction of head motion during the localization of moving sounds. These results suggest that internal spatial representations might be body-centered in blind individuals and that in sighted people the availability of visual cues during early infancy may affect sensory-motor interactions. PMID:28481939

Discrete Element Method Modeling of Bedload Transport: Towards a physics-based link between bed surface variability and particle entrainment statistics

NASA Astrophysics Data System (ADS)

Ghasemi, A.; Borhani, S.; Viparelli, E.; Hill, K. M.

2017-12-01

The Exner equation provides a formal mathematical link between sediment transport and bed morphology. It is typically represented in a discrete formulation where there is a sharp geometric interface between the bedload layer and the bed, below which no particles are entrained. For high temporally and spatially resolved models, this is strictly correct, but typically this is applied in such a way that spatial and temporal fluctuations in the bed surface (bedforms and otherwise) are not captured. This limits the extent to which the exchange between particles in transport and the sediment bed are properly represented, particularly problematic for mixed grain size distributions that exhibit segregation. Nearly two decades ago, Parker (2000) provided a framework for a solution to this dilemma in the form of a probabilistic Exner equation, partially experimentally validated by Wong et al. (2007). We present a computational study designed to develop a physics-based framework for understanding the interplay between physical parameters of the bed and flow and parameters in the Parker (2000) probabilistic formulation. To do so we use Discrete Element Method simulations to relate local time-varying parameters to long-term macroscopic parameters. These include relating local grain size distribution and particle entrainment and deposition rates to long- average bed shear stress and the standard deviation of bed height variations. While relatively simple, these simulations reproduce long-accepted empirically determined transport behaviors such as the Meyer-Peter and Muller (1948) relationship. We also find that these simulations reproduce statistical relationships proposed by Wong et al. (2007) such as a Gaussian distribution of bed heights whose standard deviation increases with increasing bed shear stress. We demonstrate how the ensuing probabilistic formulations provide insight into the transport and deposition of both narrow and wide grain size distribution.

Assessing the effects of habitat patches ensuring propagule supply and different costs inclusion in marine spatial planning through multivariate analyses.

PubMed

Appolloni, L; Sandulli, R; Vetrano, G; Russo, G F

2018-05-15

Marine Protected Areas are considered key tools for conservation of coastal ecosystems. However, many reserves are characterized by several problems mainly related to inadequate zonings that often do not protect high biodiversity and propagule supply areas precluding, at the same time, economic important zones for local interests. The Gulf of Naples is here employed as a study area to assess the effects of inclusion of different conservation features and costs in reserve design process. In particular eight scenarios are developed using graph theory to identify propagule source patches and fishing and exploitation activities as costs-in-use for local population. Scenarios elaborated by MARXAN, software commonly used for marine conservation planning, are compared using multivariate analyses (MDS, PERMANOVA and PERMDISP) in order to assess input data having greatest effects on protected areas selection. MARXAN is heuristic software able to give a number of different correct results, all of them near to the best solution. Its outputs show that the most important areas to be protected, in order to ensure long-term habitat life and adequate propagule supply, are mainly located around the Gulf islands. In addition through statistical analyses it allowed us to prove that different choices on conservation features lead to statistically different scenarios. The presence of propagule supply patches forces MARXAN to select almost the same areas to protect decreasingly different MARXAN results and, thus, choices for reserves area selection. The multivariate analyses applied here to marine spatial planning proved to be very helpful allowing to identify i) how different scenario input data affect MARXAN and ii) what features have to be taken into account in study areas characterized by peculiar biological and economic interests. Copyright © 2018 Elsevier Ltd. All rights reserved.

Forecasting of extreme events in Andes mountain basins using CFSv2

NASA Astrophysics Data System (ADS)

Castro, L.

2017-12-01

As has been shown in several recent studies related with climate change, there has been an increase in heavy daily precipitation events, and this is expected to continue in almost all areas of the globe. In central Chile, where the hydrological regime is influenced by snow accumulation, an increase in temperatures is expected due to CC, which in turn may cause an elevation of the freezing level. The impact on the freezing level increase is also significant because a larger area of the basin will be exposed to liquid precipitation rather than snow, and afterwards will have a strong impact on streamflow. The frequency of extreme precipitation events and freezing level increases have recently affected the north and central parts of Chile. In order to predict the severity of an extreme hydrometeorological event in a mountainous basin affected by rainfall and freezing level variations, this paper pose that it is necessary to know in advance the expected meteorology and the way it will affect the hydrological response of the basin. To achieve this purpose, it will be necessary to have meteorological forecasts of a numerical model for short-term prediction, corrected and disaggregated at local scale. The methodological process is as follows. First, we consider the generation of daily forecasts at local scale using statistical downscaling methods for the forecasts obtained from an NWP model. Second, we pose to improve our knowledge the spatial-temporal distribution of the meteorological forcings using a dense network of meteorological stations in a mountain basin. With the above, the statistical methods used to represent the spatial-temporal variability of the meteorological forcings at basin scale will be evaluated.

Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings

NASA Astrophysics Data System (ADS)

Liou, Jyun-you; Smith, Elliot H.; Bateman, Lisa M.; McKhann, Guy M., II; Goodman, Robert R.; Greger, Bradley; Davis, Tyler S.; Kellis, Spencer S.; House, Paul A.; Schevon, Catherine A.

2017-08-01

Objective. Epileptiform discharges, an electrophysiological hallmark of seizures, can propagate across cortical tissue in a manner similar to traveling waves. Recent work has focused attention on the origination and propagation patterns of these discharges, yielding important clues to their source location and mechanism of travel. However, systematic studies of methods for measuring propagation are lacking. Approach. We analyzed epileptiform discharges in microelectrode array recordings of human seizures. The array records multiunit activity and local field potentials at 400 micron spatial resolution, from a small cortical site free of obstructions. We evaluated several computationally efficient statistical methods for calculating traveling wave velocity, benchmarking them to analyses of associated neuronal burst firing. Main results. Over 90% of discharges met statistical criteria for propagation across the sampled cortical territory. Detection rate, direction and speed estimates derived from a multiunit estimator were compared to four field potential-based estimators: negative peak, maximum descent, high gamma power, and cross-correlation. Interestingly, the methods that were computationally simplest and most efficient (negative peak and maximal descent) offer non-inferior results in predicting neuronal traveling wave velocities compared to the other two, more complex methods. Moreover, the negative peak and maximal descent methods proved to be more robust against reduced spatial sampling challenges. Using least absolute deviation in place of least squares error minimized the impact of outliers, and reduced the discrepancies between local field potential-based and multiunit estimators. Significance. Our findings suggest that ictal epileptiform discharges typically take the form of exceptionally strong, rapidly traveling waves, with propagation detectable across millimeter distances. The sequential activation of neurons in space can be inferred from clinically-observable EEG data, with a variety of straightforward computation methods available. This opens possibilities for systematic assessments of ictal discharge propagation in clinical and research settings.

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

PubMed Central

Brown, Mac; Moore, Leslie; McMahon, Benjamin; Powell, Dennis; LaBute, Montiago; Hyman, James M.; Rivas, Ariel; Jankowski, Mark; Berendzen, Joel; Loeppky, Jason; Manore, Carrie; Fair, Jeanne

2015-01-01

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic. PMID:25946164

Dynamics of particulate organic matter composition in coastal systems: Forcing of spatio-temporal variability at multi-systems scale

NASA Astrophysics Data System (ADS)

Liénart, Camilla; Savoye, Nicolas; David, Valérie; Ramond, Pierre; Rodriguez Tress, Paco; Hanquiez, Vincent; Marieu, Vincent; Aubert, Fabien; Aubin, Sébastien; Bichon, Sabrina; Boinet, Christophe; Bourasseau, Line; Bozec, Yann; Bréret, Martine; Breton, Elsa; Caparros, Jocelyne; Cariou, Thierry; Claquin, Pascal; Conan, Pascal; Corre, Anne-Marie; Costes, Laurence; Crouvoisier, Muriel; Del Amo, Yolanda; Derriennic, Hervé; Dindinaud, François; Duran, Robert; Durozier, Maïa; Devesa, Jérémy; Ferreira, Sophie; Feunteun, Eric; Garcia, Nicole; Geslin, Sandrine; Grossteffan, Emilie; Gueux, Aurore; Guillaudeau, Julien; Guillou, Gaël; Jolly, Orianne; Lachaussée, Nicolas; Lafont, Michel; Lagadec, Véronique; Lamoureux, Jézabel; Lauga, Béatrice; Lebreton, Benoît; Lecuyer, Eric; Lehodey, Jean-Paul; Leroux, Cédric; L'Helguen, Stéphane; Macé, Eric; Maria, Eric; Mousseau, Laure; Nowaczyk, Antoine; Pineau, Philippe; Petit, Franck; Pujo-Pay, Mireille; Raimbault, Patrick; Rimmelin-Maury, Peggy; Rouaud, Vanessa; Sauriau, Pierre-Guy; Sultan, Emmanuelle; Susperregui, Nicolas

2018-03-01

In costal systems, particulate organic matter (POM) results from a multiplicity of sources having their respective dynamics in terms of production, decomposition, transport and burial. The POM pool experiences thus considerable spatial and temporal variability. In order to better understand this variability, the present study employs statistical multivariate analyses to investigate links between POM composition and environmental forcings for a panel of twelve coastal systems distributed along the three maritime regions of France and monitored weekly to monthly for 1 to 8 years. At multi-system scale, two main gradients of POC composition have been identified: a 'Continent-Ocean' gradient associated with hydrodynamics, sedimentary dynamics and depth of the water column, and a gradient of trophic status related to nutrient availability. At local scale, seasonality of POC composition appears to be station-specific but still related to part of the above-mentioned forcings. A typology of systems was established by coupling spatial and temporal variability of POC composition. Four groups were highlighted: (1) the estuarine stations where POC composition is dominated by terrestrial POM and driven by hydrodynamics and sedimentary processes, (2) the oligotrophic systems, characterized by the contribution of diazotrophs due to low nutrient availability, and the marine meso/eutroph systems whose POC composition is (3) either deeply dominated by phytoplankton or (4) dominated by phytoplankton but where the contribution of continental and benthic POC is not negligible and is driven by hydrodynamics, sedimentary processes and the height of the water column. Finally, the present study provides several insights into the different forcings to POM composition and dynamics in temperate coastal systems at local and multi-system scales. This work also presents a methodological approach that establishes statistical links between forcings and POM composition, helping to gain more objectively insight of forcings.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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