Separate but correlated: The latent structure of space and mathematics across development.

PubMed

Mix, Kelly S; Levine, Susan C; Cheng, Yi-Ling; Young, Chris; Hambrick, D Zachary; Ping, Raedy; Konstantopoulos, Spyros

2016-09-01

The relations among various spatial and mathematics skills were assessed in a cross-sectional study of 854 children from kindergarten, third, and sixth grades (i.e., 5 to 13 years of age). Children completed a battery of spatial mathematics tests and their scores were submitted to exploratory factor analyses both within and across domains. In the within domain analyses, all of the measures formed single factors at each age, suggesting consistent, unitary structures across this age range. Yet, as in previous work, the 2 domains were highly correlated, both in terms of overall composite score and pairwise comparisons of individual tasks. When both spatial and mathematics scores were submitted to the same factor analysis, the 2 domain specific factors again emerged, but there also were significant cross-domain factor loadings that varied with age. Multivariate regressions replicated the factor analysis and further revealed that mental rotation was the best predictor of mathematical performance in kindergarten, and visual-spatial working memory was the best predictor of mathematical performance in sixth grade. The mathematical tasks that predicted the most variance in spatial skill were place value (K, 3rd, 6th), word problems (3rd, 6th), calculation (K), fraction concepts (3rd), and algebra (6th). Thus, although spatial skill and mathematics each have strong internal structures, they also share significant overlap, and have particularly strong cross-domain relations for certain tasks. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Urban-Rural Differentials: A Spatial Analysis of Alabama Students’ Recent Alcohol Use and Marijuana Use

PubMed Central

Lo, Celia C.; Weber, Joe; Cheng, Tyrone C.

2013-01-01

Background and Objectives This study of Alabama public school students sought urban-rural differences in social and spatial mechanisms connecting structural factors to recent use of alcohol and marijuana. Methods Its dataset comprised a state-sponsored 2002 need-assessment survey of Alabama students; Alabama education department data; U. S. Census data; and alcohol-outlet locations listed by Alabama’s Alcoholic Beverage Control Board. It measured structural-disadvantage factors (population disadvantages, community instability, alcohol-outlet density), social-organization factors (protective role of community, protective role of school), and recent-use factors. Using Geographic Information Systems (GIS), it generated maps of school catchment areas (SCAs)—the units of analysis for the study—that outline spatial patterns (across areas deemed urban or rural) of students’ recent use of alcohol and marijuana. Results In the final sample of 370 SCAs, significant urban-versus-rural differences were observed for certain structural factors and in how these factors were associated with substance use. These differences aside, spatial analysis weighing the SCAs’ particular geographic characteristics suggested location’s importance, showing that a school playing a strong protective role significantly reduced not just its own students’ recent substance use, but that of students in neighboring SCAs as well. Conclusions and Scientific Significance The findings show students’ recent use of alcohol and marijuana are associated with characteristics of the environment. PMID:23617858

Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

PubMed

Monestiez, P; Goulard, M; Charmet, G

1994-04-01

Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

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).

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.

The influence of the interactions between anthropogenic activities and multiple ecological factors on land surface temperatures of urban forests

NASA Astrophysics Data System (ADS)

Ren, Y.

2017-12-01

Context Land surface temperatures (LSTs) spatio-temporal distribution pattern of urban forests are influenced by many ecological factors; the identification of interaction between these factors can improve simulations and predictions of spatial patterns of urban cold islands. This quantitative research requires an integrated method that combines multiple sources data with spatial statistical analysis. Objectives The purpose of this study was to clarify urban forest LST influence interaction between anthropogenic activities and multiple ecological factors using cluster analysis of hot and cold spots and Geogdetector model. We introduced the hypothesis that anthropogenic activity interacts with certain ecological factors, and their combination influences urban forests LST. We also assumed that spatio-temporal distributions of urban forest LST should be similar to those of ecological factors and can be represented quantitatively. Methods We used Jinjiang as a representative city in China as a case study. Population density was employed to represent anthropogenic activity. We built up a multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) on a unified urban scale to support urban forest LST influence interaction research. Through a combination of spatial statistical analysis results, multi-source spatial data, and Geogdetector model, the interaction mechanisms of urban forest LST were revealed. Results Although different ecological factors have different influences on forest LST, in two periods with different hot spots and cold spots, the patch area and dominant tree species were the main factors contributing to LST clustering in urban forests. The interaction between anthropogenic activity and multiple ecological factors increased LST in urban forest stands, linearly and nonlinearly. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. Conclusions In conclusion, a combination of spatial statistics and GeogDetector models should be effective for quantitatively evaluating interactive relationships among ecological factors, anthropogenic activity and LST.

Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain

PubMed Central

Tunno, Brett J; Dalton, Rebecca; Michanowicz, Drew R; Shmool, Jessie L C; Kinnee, Ellen; Tripathy, Sheila; Cambal, Leah; Clougherty, Jane E

2016-01-01

Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during “frequent inversion” hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling. PMID:26507005

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

USGS Publications Warehouse

Saman, D.M.; Cole, H.P.; Odoi, A.; Myers, M.L.; Carey, D.I.; Westneat, S.C.

2012-01-01

Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001). Conclusions: This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky. ?? 2012 Saman et al.

Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

NASA Astrophysics Data System (ADS)

Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin

2015-04-01

The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination

PubMed Central

Ha, Hoehun; Rogerson, Peter A.; Olson, James R.; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-01-01

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations. PMID:27649221

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

PubMed

Ha, Hoehun; Rogerson, Peter A; Olson, James R; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-09-14

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Development of the Spatial Ability Test for Middle School Students

ERIC Educational Resources Information Center

Yildiz, Sevda Göktepe; Özdemir, Ahmet Sükrü

2017-01-01

The purpose of this study was to develop a test to determine spatial ability of middle school students. The participants were 704 middle school students (6th, 7th and 8th grade) who were studying at different schools from Istanbul. Item analysis, exploratory and confirmatory factor analysis, reliability analysis were used to analyse the data.…

Associations between residence at birth and mental health disorders: a spatial analysis of retrospective cohort data.

PubMed

Hoffman, Kate; Aschengrau, Ann; Webster, Thomas F; Bartell, Scott M; Vieira, Verónica M

2015-07-21

Mental health disorders impact approximately one in four US adults. While their causes are likely multifactorial, prior research has linked the risk of certain mental health disorders to prenatal and early childhood environmental exposures, motivating a spatial analysis to determine whether risk varies by birth location. We investigated the spatial associations between residence at birth and odds of depression, bipolar disorder, and post-traumatic stress disorder (PTSD) in a retrospective cohort (Cape Cod, Massachusetts, 1969-1983) using generalized additive models to simultaneously smooth location and adjust for confounders. Birth location served as a surrogate for prenatal exposure to the combination of social and environmental factors related to the development of mental illness. We predicted crude and adjusted odds ratios (aOR) for each outcome across the study area. The results were mapped to identify areas of increased risk. We observed spatial variation in the crude odds ratios of depression that was still present even after accounting for spatial confounding due to geographic differences in the distribution of known risk factors (aOR range: 0.61-3.07, P = 0.03). Similar geographic patterns were seen for the crude odds of PTSD; however, these patterns were no longer present in the adjusted analysis (aOR range: 0.49-1.36, P = 0.79), with family history of mental illness most notably influencing the geographic patterns. Analyses of the odds of bipolar disorder did not show any meaningful spatial variation (aOR range: 0.58-1.17, P = 0.82). Spatial associations exist between residence at birth and odds of PTSD and depression, but much of this variation can be explained by the geographic distributions of available risk factors. However, these risk factors did not account for all the variation observed with depression, suggesting that other social and environmental factors within our study area need further investigation.

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

PubMed

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

2014-09-25

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

Multiscale Spatial Assessment of Determinant Factors of Land Use Change: Study at Urban Area of Yogyakarta

NASA Astrophysics Data System (ADS)

Susilo, Bowo

2017-12-01

Studies of land use change have been undertaken by different researchers using various methods. Among those methods, modelling is widely utilized. Modelling land use change required several components remarked as model variables. Those represent any conditions or factors which considered relevant or have some degree of correlation to the changes of land use. Variables which have significant correlation to land use change are referred as determinant factors or driving forces. Those factors as well as changes of land use are distributed across space and therefore referred as spatial determinant factors. The main objective of the research was to examine land use change and its determinant factors. Area and location of land use change were analysed based on three different years of land use maps, which are 1993, 2000 and 2007. Spatial and temporal analysis were performed which emphasize to the influence of scale to both of analysis’s. Urban area of Yogyakarta was selected as study area. Study area covered three different districts (kabupaten), involving 20 sub districts and totally consists of 74 villages. Result of this study shows that during 14 years periods (1993 to 2007), there were about 1,460 hectares of land use change had been taken place. Dominant type of land use change is agricultural to residential. The uses of different spatial and temporal scale in analysis were able to reveal different factors related to land use change. In general, factors influencing the quantities of land use change in the study area were population growth and the availability of land. The use of data with different spatial resolution can reveal the presence of various factors associated with the location of the change. Locations of land use change were influenced or determined by accessibility factors.

Variability of Soil Temperature: A Spatial and Temporal Analysis.

ERIC Educational Resources Information Center

Walsh, Stephen J.; And Others

1991-01-01

Discusses an analysis of the relationship of soil temperatures at 3 depths to various climatic variables along a 200-kilometer transect in west-central Oklahoma. Reports that temperature readings increased from east to west. Concludes that temperature variations were explained by a combination of spatial, temporal, and biophysical factors. (SG)

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures.

PubMed

Shmool, Jessie L C; Kubzansky, Laura D; Newman, Ogonnaya Dotson; Spengler, John; Shepard, Peggy; Clougherty, Jane E

2014-11-06

Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health. We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results. Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits. This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.

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

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

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

PubMed

Heino, Jani; Melo, Adriano S; Bini, Luis Mauricio; Altermatt, Florian; Al-Shami, Salman A; Angeler, David G; Bonada, Núria; Brand, Cecilia; Callisto, Marcos; Cottenie, Karl; Dangles, Olivier; Dudgeon, David; Encalada, Andrea; Göthe, Emma; Grönroos, Mira; Hamada, Neusa; Jacobsen, Dean; Landeiro, Victor L; Ligeiro, Raphael; Martins, Renato T; Miserendino, María Laura; Md Rawi, Che Salmah; Rodrigues, Marciel E; Roque, Fabio de Oliveira; Sandin, Leonard; Schmera, Denes; Sgarbi, Luciano F; Simaika, John P; Siqueira, Tadeu; Thompson, Ross M; Townsend, Colin R

2015-03-01

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.

Science in the Sun: How Science is Performed as a Spatial Practice

NASA Astrophysics Data System (ADS)

Kass, Natalie

This study analyzes how spatial organization impacts science communication at the St. Petersburg Science Festival in Florida. Through map analysis, qualitative interviews, and a close reading of evaluation reports, the author determines that sponsorship, logistics, exhibitor ambience, and map usability and design are the factors most affecting the spatial performance of science. To mitigate their effects, technical communicators can identify these factors and provide the necessary revisions when considering how science is communicated to the public.

Quantifying the influences of various ecological factors on land surface temperature of urban forests.

PubMed

Ren, Yin; Deng, Lu-Ying; Zuo, Shu-Di; Song, Xiao-Dong; Liao, Yi-Lan; Xu, Cheng-Dong; Chen, Qi; Hua, Li-Zhong; Li, Zheng-Wei

2016-09-01

Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment.

PubMed

Luan, Hui; Law, Jane; Lysy, Martin

2018-02-01

Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Insights from Modelling the Spatial Dependence Structure of Hydraulic Conductivity at the MADE Site Using Spatial Copulas

NASA Astrophysics Data System (ADS)

Haslauer, Claus; Bohling, Geoff

2013-04-01

Hydraulic conductivity (K) is a fundamental parameter that influences groundwater flow and solute transport. Measurements of K are limited and uncertain. Moreover, the spatial structure of K, which impacts the groundwater velocity field and hence directly influences the advective spreading of a solute migrating in the subsurface, is commonly described by approaches using second order moments. Spatial copulas have in the recent past been applied successfully to model the spatial dependence structure of heterogeneous subsurface datasets. At the MADE site, hydraulic conductivity (K) has been measured in exceptional detail. Two independently collected data-sets were used for this study: (1) ~2000 flowmeter based K measurements, and (2) ~20,000 direct-push based K measurements. These datasets exhibit a very heterogeneous (Var[ln(K)]>2) spatially distributed K field. A copula analysis reveals that the spatial dependence structure of the flowmeter and direct-push datasets are essentially the same. A spatial copula analysis factors out the influence of the marginal distribution of the property under investigation. This independence from the marginal distributions allows the copula analysis to reveal the underlying similarity between the spatial dependence structures of the flowmeter and direct-push datasets despite two complicating factors: 1) an overall offset between the datasets, with direct-push K values being, on average, roughly a factor of five lower than flowmeter K values, due at least in part to opposite biases between the two measurement techniques, and 2) the presence of some anomalously high K values in the direct-push dataset due to a lower limit on accurately measureable pressure responses in high-K zones. In addition, the vertical resolution of the direct-push dataset is ten times finer than that of the flowmeter dataset. Upscaling the direct-push data to compensate for this difference resulted in little change to the spatial structure. The objective of the presented work is to use multidimensional spatial copulas to describe and model the spatial dependence of the spatial structure of K at the heterogeneous MADE site, and evaluate the effects of this multidimensional description on solute transport.

Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya.

PubMed

Brooker, Simon; Clarke, Siân; Njagi, Joseph Kiambo; Polack, Sarah; Mugo, Benbolt; Estambale, Benson; Muchiri, Eric; Magnussen, Pascal; Cox, Jonathan

2004-07-01

The epidemiology of malaria over small areas remains poorly understood, and this is particularly true for malaria during epidemics in highland areas of Africa, where transmission intensity is low and characterized by acute within and between year variations. We report an analysis of the spatial distribution of clinical malaria during an epidemic and investigate putative risk factors. Active case surveillance was undertaken in three schools in Nandi District, Western Kenya for 10 weeks during a malaria outbreak in May-July 2002. Household surveys of cases and age-matched controls were conducted to collect information on household construction, exposure factors and socio-economic status. Household geographical location and altitude were determined using a hand-held geographical positioning system and landcover types were determined using high spatial resolution satellite sensor data. Among 129 cases identified during the surveillance, which were matched to 155 controls, we identified significant spatial clusters of malaria cases as determined using the spatial scan statistic. Conditional multiple logistic regression analysis showed that the risk of malaria was higher in children who were underweight, who lived at lower altitudes, and who lived in households where drugs were not kept at home. Copyright 2004 Blackwell Publishing Ltd

Relationships between hand laterality and verbal and spatial skills in 436 healthy adults balanced for handedness.

PubMed

Mellet, E; Jobard, G; Zago, L; Crivello, F; Petit, L; Joliot, M; Mazoyer, B; Tzourio-Mazoyer, N

2014-01-01

The relationship between manual laterality and cognitive skills remains highly controversial. Some studies have reported that strongly lateralised participants had higher cognitive performance in verbal and visuo-spatial domains compared to non-lateralised participants; however, others found the opposite. Moreover, some have suggested that familial sinistrality and sex might interact with individual laterality factors to alter cognitive skills. The present study addressed these issues in 237 right-handed and 199 left-handed individuals. Performance tests covered various aspects of verbal and spatial cognition. A principal component analysis yielded two verbal and one spatial factor scores. Participant laterality assessments included handedness, manual preference strength, asymmetry of motor performance, and familial sinistrality. Age, sex, education level, and brain volume were also considered. No effect of handedness was found, but the mean factor scores in verbal and spatial domains increased with right asymmetry in motor performance. Performance was reduced in participants with a familial history of left-handedness combined with a non-maximal preference strength in the dominant hand. These results elucidated some discrepancies among previous findings in laterality factors and cognitive skills. Laterality factors had small effects compared to the adverse effects of age for spatial cognition and verbal memory, the positive effects of education for all three domains, and the effect of sex for spatial cognition.

Violence in public transportation: an approach based on spatial analysis.

PubMed

Sousa, Daiane Castro Bittencourt de; Pitombo, Cira Souza; Rocha, Samille Santos; Salgueiro, Ana Rita; Delgado, Juan Pedro Moreno

2017-12-11

To carry out a spatial analysis of the occurrence of acts of violence (specifically robberies) in public transportation, identifying the regions of greater incidence, using geostatistics, and possible causes with the aid of a multicriteria analysis in the Geographic Information System. The unit of analysis is the traffic analysis zone of the survey named Origem-Destino, carried out in Salvador, state of Bahia, in 2013. The robberies recorded by the Department of Public Security of Bahia in 2013 were located and made compatible with the limits of the traffic analysis zones and, later, associated with the respective centroids. After determining the regions with the highest probability of robbery, we carried out a geographic analysis of the possible causes in the region with the highest robbery potential, considering the factors analyzed using a multicriteria analysis in a Geographic Information System environment. The execution of the two steps of this study allowed us to identify areas corresponding to the greater probability of occurrence of robberies in public transportation. In addition, the three most vulnerable road sections (Estrada da Liberdade, Rua Pero Vaz, and Avenida General San Martin) were identified in these areas. In these sections, the factors that most contribute with the potential for robbery in buses are: F1 - proximity to places that facilitate escape, F3 - great movement of persons, and F2 - absence of policing, respectively. Indicator Kriging (geostatistical estimation) can be used to construct a spatial probability surface, which can be a useful tool for the implementation of public policies. The multicriteria analysis in the Geographic Information System environment allowed us to understand the spatial factors related to the phenomenon under analysis.

Violence in public transportation: an approach based on spatial analysis

PubMed Central

de Sousa, Daiane Castro Bittencourt; Pitombo, Cira Souza; Rocha, Samille Santos; Salgueiro, Ana Rita; Delgado, Juan Pedro Moreno

2017-01-01

ABSTRACT OBJECTIVE To carry out a spatial analysis of the occurrence of acts of violence (specifically robberies) in public transportation, identifying the regions of greater incidence, using geostatistics, and possible causes with the aid of a multicriteria analysis in the Geographic Information System. METHODS The unit of analysis is the traffic analysis zone of the survey named Origem-Destino, carried out in Salvador, state of Bahia, in 2013. The robberies recorded by the Department of Public Security of Bahia in 2013 were located and made compatible with the limits of the traffic analysis zones and, later, associated with the respective centroids. After determining the regions with the highest probability of robbery, we carried out a geographic analysis of the possible causes in the region with the highest robbery potential, considering the factors analyzed using a multicriteria analysis in a Geographic Information System environment. RESULTS The execution of the two steps of this study allowed us to identify areas corresponding to the greater probability of occurrence of robberies in public transportation. In addition, the three most vulnerable road sections (Estrada da Liberdade, Rua Pero Vaz, and Avenida General San Martin) were identified in these areas. In these sections, the factors that most contribute with the potential for robbery in buses are: F1 - proximity to places that facilitate escape, F3 - great movement of persons, and F2 - absence of policing, respectively. CONCLUSIONS Indicator Kriging (geostatistical estimation) can be used to construct a spatial probability surface, which can be a useful tool for the implementation of public policies. The multicriteria analysis in the Geographic Information System environment allowed us to understand the spatial factors related to the phenomenon under analysis. PMID:29236883

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

PubMed Central

2012-01-01

Background Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e.g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country. PMID:22410079

Bulk tank milk prevalence and production losses, spatial analysis, and predictive risk mapping of Ostertagia ostertagi infections in Mexican cattle herds.

PubMed

Villa-Mancera, Abel; Pastelín-Rojas, César; Olivares-Pérez, Jaime; Córdova-Izquierdo, Alejandro; Reynoso-Palomar, Alejandro

2018-05-01

This study investigated the prevalence, production losses, spatial clustering, and predictive risk mapping in different climate zones in five states of Mexico. The bulk tank milk samples obtained between January and April 2015 were analyzed for antibodies against Ostertagia ostertagi using the Svanovir ELISA. A total of 1204 farm owners or managers answered the questionnaire. The overall herd prevalence and mean optical density ratio (ODR) of parasite were 61.96% and 0.55, respectively. Overall, the production loss was approximately 0.542 kg of milk per parasited cow per day (mean ODR = 0.92, 142 farms, 11.79%). The spatial disease cluster analysis using SatScan software indicated that two high-risk clusters were observed. In the multivariable analysis, three models were tested for potential association with the ELISA results supported by climatic, environmental, and management factors. The final logistic regression model based on both climatic/environmental and management variables included the factors rainfall, elevation, land surface temperature (LST) day, and parasite control program that were significantly associated with an increased risk of infection. Geostatistical kriging was applied to generate a risk map for the presence of parasite in dairy cattle herds in Mexico. The results indicate that climatic and meteorological factors had a higher potential impact on the spatial distribution of O. ostertagi than the management factors.

Components of Spatial Thinking: Evidence from a Spatial Thinking Ability Test

ERIC Educational Resources Information Center

Lee, Jongwon; Bednarz, Robert

2012-01-01

This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied…

Cognitive Process Modeling of Spatial Ability: The Assembling Objects Task

ERIC Educational Resources Information Center

Ivie, Jennifer L.; Embretson, Susan E.

2010-01-01

Spatial ability tasks appear on many intelligence and aptitude tests. Although the construct validity of spatial ability tests has often been studied through traditional correlational methods, such as factor analysis, less is known about the cognitive processes involved in solving test items. This study examines the cognitive processes involved in…

A Cognitive Component Analysis Approach for Developing Game-Based Spatial Learning Tools

ERIC Educational Resources Information Center

Hung, Pi-Hsia; Hwang, Gwo-Jen; Lee, Yueh-Hsun; Su, I-Hsiang

2012-01-01

Spatial ability has been recognized as one of the most important factors affecting the mathematical performance of students. Previous studies on spatial learning have mainly focused on developing strategies to shorten the problem-solving time of learners for very specific learning tasks. Such an approach usually has limited effects on improving…

Spatial and Temporal Changes of Aerosol Optical Depth and its Driving Factors Based on Modis in Jiangsu Province

NASA Astrophysics Data System (ADS)

Jiang, C.; Xu, Q.; Gu, Y. K.; Qian, X. Y.; He, J. N.

2018-04-01

Aerosol Optical Depth (AOD) is of great value for studying air mass and its changes. In this paper, we studied the spatial-temporal changes of AOD and its driving factors based on spatial autocorrelation model, gravity model and multiple regression analysis in Jiangsu Province from 2007 to 2016. The results showed that in terms of spatial distribution, the southern AOD value is higher, and the high-value aggregation areas are significant, while the northern AOD value is lower, but the low-value aggregation areas constantly change. The AOD gravity centers showed a clear point-like aggregation. In terms of temporal changes, the overall AOD in Jiangsu Province increased year by year in fluctuation. In terms of driving factors, the total amount of vehicles, precipitation and temperature are important factors for the growth of AOD.

[Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

PubMed

Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi

2011-12-01

Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

The Effects of Geography and Spatial Behavior on Health Care Utilization among the Residents of a Rural Region

PubMed Central

Arcury, Thomas A; Gesler, Wilbert M; Preisser, John S; Sherman, Jill; Spencer, John; Perin, Jamie

2005-01-01

Objective This analysis determines the importance of geography and spatial behavior as predisposing and enabling factors in rural health care utilization, controlling for demographic, social, cultural, and health status factors. Data Sources A survey of 1,059 adults in 12 rural Appalachian North Carolina counties. Study Design This cross-sectional study used a three-stage sampling design stratified by county and ethnicity. Preliminary analysis of health services utilization compared weighted proportions of number of health care visits in the previous 12 months for regular check-up care, chronic care, and acute care across geographic, sociodemographic, cultural, and health variables. Multivariable logistic models identified independent correlates of health services utilization. Data Collection Methods Respondents answered standard survey questions. They located places in which they engaged health related and normal day-to-day activities; these data were entered into a geographic information system for analysis. Principal Findings Several geographic and spatial behavior factors, including having a driver's license, use of provided rides, and distance for regular care, were significantly related to health care utilization for regular check-up and chronic care in the bivariate analysis. In the multivariate model, having a driver's license and distance for regular care remained significant, as did several predisposing (age, gender, ethnicity), enabling (household income), and need (physical and mental health measures, number of conditions). Geographic measures, as predisposing and enabling factors, were related to regular check-up and chronic care, but not to acute care visits. Conclusions These results show the importance of geographic and spatial behavior factors in rural health care utilization. They also indicate continuing inequity in rural health care utilization that must be addressed in public policy. PMID:15663706

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil

PubMed Central

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J.

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns. PMID:27171522

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil.

PubMed

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns.

Determining the spatial variability of wetland soil bulk density, organic matter, and the conversion factor between organic matter and organic carbon across coastal Louisiana, U.S.A.

USGS Publications Warehouse

Wang, Hongqing; Piazza, Sarai C.; Sharp, Leigh A.; Stagg, Camille L.; Couvillion, Brady R.; Steyer, Gregory D.; McGinnis, Thomas E.

2016-01-01

Soil bulk density (BD), soil organic matter (SOM) content, and a conversion factor between SOM and soil organic carbon (SOC) are often used in estimating SOC sequestration and storage. Spatial variability in BD, SOM, and the SOM–SOC conversion factor affects the ability to accurately estimate SOC sequestration, storage, and the benefits (e.g., land building area and vertical accretion) associated with wetland restoration efforts, such as marsh creation and sediment diversions. There are, however, only a few studies that have examined large-scale spatial variability in BD, SOM, and SOM–SOC conversion factors in coastal wetlands. In this study, soil cores, distributed across the entire coastal Louisiana (approximately 14,667 km2) were used to examine the regional-scale spatial variability in BD, SOM, and the SOM–SOC conversion factor. Soil cores for BD and SOM analyses were collected during 2006–09 from 331 spatially well-distributed sites in the Coastwide Reference Monitoring System network. Soil cores for the SOM–SOC conversion factor analysis were collected from 15 sites across coastal Louisiana during 2006–07. Results of a split-plot analysis of variance with incomplete block design indicated that BD and SOM varied significantly at a landscape level, defined by both hydrologic basins and vegetation types. Vertically, BD and SOM varied significantly among different vegetation types. The SOM–SOC conversion factor also varied significantly at the landscape level. This study provides critical information for the assessment of the role of coastal wetlands in large regional carbon budgets and the estimation of carbon credits from coastal restoration.

Spatio-temporal analysis of small-area intestinal parasites infections in Ghana.

PubMed

Osei, F B; Stein, A

2017-09-22

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran's Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran's Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

Field evaluation of spatial repellency of metofluthrin-impregnated latticework plastic strips against Aedes aegypti (L.) and analysis of environmental factors affecting its efficacy in My Tho City, Tien Giang, Vietnam.

PubMed

Kawada, Hitoshi; Iwasaki, Tomonori; LE Loan, Luu; Tien, Tran Khanh; Mai, Nguyen Thi Nhu; Shono, Yoshinori; Katayama, Yasuyuki; Takagi, Masahiro

2006-12-01

Spatial repellency of metofluthrin-impregnated polyethylene latticework plastic strips against Aedes aegypti mosquitoes was evaluated. Analysis of environmental factors affecting the efficacy of these strips, such as room temperature, humidity, and house structure, was performed in a residential area in My Tho City, Tien Giang Province, Vietnam. Treatment with the strips at the rate of 1 strip per 2.6-5.52 m(2) (approximately 600 mg per 2.6-5.52 m(2)) reduced the collection of Ae. aegypti resting inside the houses for at least eight weeks. Multiple regression analysis indicated that both increase in the average room temperature and decrease in the area of openings in the rooms that were treated with the strips positively affected the spatial repellency of metofluthrin.

[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.

Spatial analysis of alcohol-related motor vehicle crash injuries in southeastern Michigan.

PubMed

Meliker, Jaymie R; Maio, Ronald F; Zimmerman, Marc A; Kim, Hyungjin Myra; Smith, Sarah C; Wilson, Mark L

2004-11-01

Temporal, behavioral and social risk factors that affect injuries resulting from alcohol-related motor vehicle crashes have been characterized in previous research. Much less is known about spatial patterns and environmental associations of alcohol-related motor vehicle crashes. The aim of this study was to evaluate geographic patterns of alcohol-related motor vehicle crashes and to determine if locations of alcohol outlets are associated with those crashes. In addition, we sought to demonstrate the value of integrating spatial and traditional statistical techniques in the analysis of this preventable public health risk. The study design was a cross-sectional analysis of individual-level blood alcohol content, traffic report information, census block group data, and alcohol distribution outlets. Besag and Newell's spatial analysis and traditional logistic regression both indicated that areas of low population density had more alcohol-related motor vehicle crashes than expected (P < 0.05). There was no significant association between alcohol outlets and alcohol-related motor vehicle crashes using distance analyses, logistic regression, and Chi-square. Differences in environmental or behavioral factors characteristic of areas of low population density may be responsible for the higher proportion of alcohol-related crashes occurring in these areas.

Roles of climate, vegetation and soil in regulating the spatial variations in ecosystem carbon dioxide fluxes in the Northern Hemisphere.

PubMed

Chen, Zhi; Yu, Guirui; Ge, Jianping; Wang, Qiufeng; Zhu, Xianjin; Xu, Zhiwei

2015-01-01

Climate, vegetation, and soil characteristics play important roles in regulating the spatial variation in carbon dioxide fluxes, but their relative influence is still uncertain. In this study, we compiled data from 241 eddy covariance flux sites in the Northern Hemisphere and used Classification and Regression Trees and Redundancy Analysis to assess how climate, vegetation, and soil affect the spatial variations in three carbon dioxide fluxes (annual gross primary production (AGPP), annual ecosystem respiration (ARE), and annual net ecosystem production (ANEP)). Our results showed that the spatial variations in AGPP, ARE, and ANEP were significantly related to the climate and vegetation factors (correlation coefficients, R = 0.22 to 0.69, P < 0.01) while they were not related to the soil factors (R = -0.11 to 0.14, P > 0.05) in the Northern Hemisphere. The climate and vegetation together explained 60% and 58% of the spatial variations in AGPP and ARE, respectively. Climate factors (mean annual temperature and precipitation) could account for 45-47% of the spatial variations in AGPP and ARE, but the climate constraint on the vegetation index explained approximately 75%. Our findings suggest that climate factors affect the spatial variations in AGPP and ARE mainly by regulating vegetation properties, while soil factors exert a minor effect. To more accurately assess global carbon balance and predict ecosystem responses to climate change, these discrepant roles of climate, vegetation, and soil are required to be fully considered in the future land surface models. Moreover, our results showed that climate and vegetation factors failed to capture the spatial variation in ANEP and suggest that to reveal the underlying mechanism for variation in ANEP, taking into account the effects of other factors (such as climate change and disturbances) is necessary.

Roles of Climate, Vegetation and Soil in Regulating the Spatial Variations in Ecosystem Carbon Dioxide Fluxes in the Northern Hemisphere

PubMed Central

Chen, Zhi; Yu, Guirui; Ge, Jianping; Wang, Qiufeng; Zhu, Xianjin; Xu, Zhiwei

2015-01-01

Climate, vegetation, and soil characteristics play important roles in regulating the spatial variation in carbon dioxide fluxes, but their relative influence is still uncertain. In this study, we compiled data from 241 eddy covariance flux sites in the Northern Hemisphere and used Classification and Regression Trees and Redundancy Analysis to assess how climate, vegetation, and soil affect the spatial variations in three carbon dioxide fluxes (annual gross primary production (AGPP), annual ecosystem respiration (ARE), and annual net ecosystem production (ANEP)). Our results showed that the spatial variations in AGPP, ARE, and ANEP were significantly related to the climate and vegetation factors (correlation coefficients, R = 0.22 to 0.69, P < 0.01) while they were not related to the soil factors (R = -0.11 to 0.14, P > 0.05) in the Northern Hemisphere. The climate and vegetation together explained 60 % and 58 % of the spatial variations in AGPP and ARE, respectively. Climate factors (mean annual temperature and precipitation) could account for 45 - 47 % of the spatial variations in AGPP and ARE, but the climate constraint on the vegetation index explained approximately 75 %. Our findings suggest that climate factors affect the spatial variations in AGPP and ARE mainly by regulating vegetation properties, while soil factors exert a minor effect. To more accurately assess global carbon balance and predict ecosystem responses to climate change, these discrepant roles of climate, vegetation, and soil are required to be fully considered in the future land surface models. Moreover, our results showed that climate and vegetation factors failed to capture the spatial variation in ANEP and suggest that to reveal the underlying mechanism for variation in ANEP, taking into account the effects of other factors (such as climate change and disturbances) is necessary. PMID:25928452

Spatial Data Mining for Estimating Cover Management Factor of Universal Soil Loss Equation

NASA Astrophysics Data System (ADS)

Tsai, F.; Lin, T. C.; Chiang, S. H.; Chen, W. W.

2016-12-01

Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes long-term soil erosion processes. Among the six different soil erosion risk factors of USLE, the cover-management factor (C-factor) is related to land-cover/land-use. The value of C-factor ranges from 0.001 to 1, so it alone might cause a thousandfold difference in a soil erosion analysis using USLE. The traditional methods for the estimation of USLE C-factor include in situ experiments, soil physical parameter models, USLE look-up tables with land use maps, and regression models between vegetation indices and C-factors. However, these methods are either difficult or too expensive to implement in large areas. In addition, the values of C-factor obtained using these methods can not be updated frequently, either. To address this issue, this research developed a spatial data mining approach to estimate the values of C-factor with assorted spatial datasets for a multi-temporal (2004 to 2008) annual soil loss analysis of a reservoir watershed in northern Taiwan. The idea is to establish the relationship between the USLE C-factor and spatial data consisting of vegetation indices and texture features extracted from satellite images, soil and geology attributes, digital elevation model, road and river distribution etc. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. Experimental results indicate that the overall accuracy of the data mining model is about 79% with a kappa value of 0.76. The estimated soil erosion amounts in 2004-2008 according to the data mining results are about 50.39 - 74.57 ton/ha-year after applying the sediment delivery ratio and correction coefficient. Comparing with estimations calculated with C-factors from look-up tables, the soil erosion values estimated with C-factors generated from spatial data mining results are more in agreement with the values published by the watershed administration authority.

The genetic and environmental aetiology of spatial, mathematics and general anxiety

PubMed Central

Malanchini, Margherita; Rimfeld, Kaili; Shakeshaft, Nicholas G.; Rodic, Maja; Schofield, Kerry; Selzam, Saskia; Dale, Philip S.; Petrill, Stephen A.; Kovas, Yulia

2017-01-01

Individuals differ in their level of general anxiety as well as in their level of anxiety towards specific activities, such as mathematics and spatial tasks. Both specific anxieties correlate moderately with general anxiety, but the aetiology of their association remains unexplored. Moreover, the factor structure of spatial anxiety is to date unknown. The present study investigated the factor structure of spatial anxiety, its aetiology, and the origins of its association with general and mathematics anxiety in a sample of 1,464 19-21-year-old twin pairs from the UK representative Twins Early Development Study. Participants reported their general, mathematics and spatial anxiety as part of an online battery of tests. We found that spatial anxiety is a multifactorial construct, including two components: navigation anxiety and rotation/visualization anxiety. All anxiety measures were moderately heritable (30% to 41%), and non-shared environmental factors explained the remaining variance. Multivariate genetic analysis showed that, although some genetic and environmental factors contributed to all anxiety measures, a substantial portion of genetic and non-shared environmental influences were specific to each anxiety construct. This suggests that anxiety is a multifactorial construct phenotypically and aetiologically, highlighting the importance of studying anxiety within specific contexts. PMID:28220830

The genetic and environmental aetiology of spatial, mathematics and general anxiety.

PubMed

Malanchini, Margherita; Rimfeld, Kaili; Shakeshaft, Nicholas G; Rodic, Maja; Schofield, Kerry; Selzam, Saskia; Dale, Philip S; Petrill, Stephen A; Kovas, Yulia

2017-02-21

Individuals differ in their level of general anxiety as well as in their level of anxiety towards specific activities, such as mathematics and spatial tasks. Both specific anxieties correlate moderately with general anxiety, but the aetiology of their association remains unexplored. Moreover, the factor structure of spatial anxiety is to date unknown. The present study investigated the factor structure of spatial anxiety, its aetiology, and the origins of its association with general and mathematics anxiety in a sample of 1,464 19-21-year-old twin pairs from the UK representative Twins Early Development Study. Participants reported their general, mathematics and spatial anxiety as part of an online battery of tests. We found that spatial anxiety is a multifactorial construct, including two components: navigation anxiety and rotation/visualization anxiety. All anxiety measures were moderately heritable (30% to 41%), and non-shared environmental factors explained the remaining variance. Multivariate genetic analysis showed that, although some genetic and environmental factors contributed to all anxiety measures, a substantial portion of genetic and non-shared environmental influences were specific to each anxiety construct. This suggests that anxiety is a multifactorial construct phenotypically and aetiologically, highlighting the importance of studying anxiety within specific contexts.

Spatializing health research: what we know and where we are heading

PubMed Central

Yang, Tse-Chuan; Shoff, Carla; Noah, Aggie J.

2013-01-01

Beyond individual-level factors, researchers have adopted a spatial perspective to explore potentially modifiable environmental determinants of health. A spatial perspective can be integrated into health research by incorporating spatial data into studies or analyzing georeferenced data. Given the rapid changes in data collection methods and the complex dynamics between individuals and environment, we argue that GIS functions have shortcomings with respect to analytical capability and are limited when it comes to visualizing the temporal component in spatio-temporal data. In addition, we maintain that relatively little effort has been made to handle spatial heterogeneity. To that end, health researchers should be persuaded to better justify the theoretical meaning underlying the spatial matrix in analysis, while spatial data collectors, GIS specialists, spatial analysis methodologists, and the different breeds of users should be encouraged to work together making health research move forward through addressing these issues. PMID:23733281

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

PubMed

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

2015-01-01

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

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

Thomas Steven Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Communicating and Evaluating the Causes of Seismicity in Oklahoma Using ArcGIS Online Story Map Web Applications

NASA Astrophysics Data System (ADS)

Justman, D.; Rose, K.; Bauer, J. R.; Miller, R., III; Vasylkivska, V.; Romeo, L.

2016-12-01

ArcGIS Online story maps allows users to communicate complex topics with geospatially enabled stories. This story map web application entitled "Evaluating the Mysteries of Seismicity in Oklahoma" has been employed as part of a broader research effort investigating the relationships between spatiotemporal systems and seismicity to understand the recent increase in seismicity by reviewing literature, exploring, and performing analyses on key datasets. It offers information about the unprecedented increase in seismic events since 2008, earthquake history, the risk to the population, physical mechanisms behind earthquakes, natural and anthropogenic earthquake factors, and individual & cumulative spatial extents of these factors. The cumulative spatial extents for natural, anthropogenic, and all combined earthquake factors were determined using the Cumulative Spatial Impact Layers (CSILs) tool developed at the National Energy Technology Laboratory (NETL). Results show positive correlations between the average number of influences (datasets related to individual factors) and the number of earthquakes for every 100 square mile grid cell in Oklahoma, along with interesting spatial correlations for the individual & cumulative spatial extents of these factors when overlaid with earthquake density and a hotspot analysis for earthquake magnitude from 2010 to 2015.

Revealed access to haemodialysis facilities in northeastern Iran: Factors that matter in rural and urban areas.

PubMed

Kiani, Behzad; Bagheri, Nasser; Tara, Ahmad; Hoseini, Benyamin; Tabesh, Hamed; Tara, Mahmood

2017-11-07

Poor access to haemodialysis facilities is associated with high mortality and morbidity rates. This study investigated factors affecting revealed access to the haemodialysis facilities considering patients living in rural and urban areas without any haemodialysis facility (Group A) and those living urban areas with haemodialysis facilities (Group B). This study is based on selfreported Actual Access Time (AAT) to referred haemodialysis facilities and other information regarding travel to haemodialysis facilities from patients. All significant variables on univariate analysis were entered into a univariate general linear model in order to identify factors associated with AAT. Both spatial (driving time and distance) and non-spatial factors (sex, income level, caregivers, transportation mode, education level, ethnicity and personal vehicle ownership) influenced the revealed access identified in Group A. The non-spatial factors for Group B patients were the same as for Group A, but no spatial factor was identified in Group B. It was found that accessibility is strongly underestimated when driving time is chosen as accessibility measure to haemodialysis facilities. Analysis of revealed access determinants provides policymakers with an appropriate decision base for making appropriate decisions and finding solutions to decrease the access time for patients under haemodialysis therapy. Driving time alone is not a good proxy for measuring access to haemodialysis facilities as there are many other potential obstacles, such as women's special travel problems, poor other transportation possibilities, ethnicity disparities, low education levels, low caregiver status and low-income.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

Human factors issues and approaches in the spatial layout of a space station control room, including the use of virtual reality as a design analysis tool

NASA Technical Reports Server (NTRS)

Hale, Joseph P., II

1994-01-01

Human Factors Engineering support was provided for the 30% design review of the late Space Station Freedom Payload Control Area (PCA). The PCA was to be the payload operations control room, analogous to the Spacelab Payload Operations Control Center (POCC). This effort began with a systematic collection and refinement of the relevant requirements driving the spatial layout of the consoles and PCA. This information was used as input for specialized human factors analytical tools and techniques in the design and design analysis activities. Design concepts and configuration options were developed and reviewed using sketches, 2-D Computer-Aided Design (CAD) drawings, and immersive Virtual Reality (VR) mockups.

Spatial and temporal variations of wind erosion climatic erosivity in the farming-pastoral zone of Northern China

NASA Astrophysics Data System (ADS)

Yue, Shuping; Yang, Ruixin; Yan, Yechao; Yang, Zhengwei; Wang, Dandan

2018-03-01

Wind erosion climatic erosivity is an important parameter to assess the possible effects of climatic conditions on wind erosion. In this paper, the wind erosion climatic factor (C-factor), which was used to quantify the wind erosion climatic erosivity, was calculated for the period 1960-2014 based on monthly meteorological data collected from 101 stations in the farming-pastoral zone of Northern China. The Mann-Kendall (M-K) test, trend analysis, and geostatistical analysis methods were used to explore the spatial and temporal characteristics of the wind erosion climatic erosivity in this region. The result suggests that the annual C-factor, with a maximum of 76.05 in 1969 and a minimum of 26.57 in 2007, has a significant decreasing trend over the past 55 years. Strong seasonality in the C-factor was found, with the highest value in spring, which accounts for a significant proportion of the annual C-factor (41.46%). However, the coefficient of variation of the seasonal C-factor reaches a maximum in winter and a minimum in spring. The mean annual C-factor varies substantially across the region. Areas with high values of the mean annual C-factor (C ≥ 100) are located in Ulanqab and Dingxi, while areas with low values (C ≤ 10) lie in Lanzhou, Linxia, Dingxi, Xining, and Chengde. Spatial analysis on the trend of the C-factor reveals that 81% of the stations show statistically significant decreases at a 90% confidence level. An examination of the concentration ratio of the C-factor shows that the wind erosion climatic erosivity is concentrated in spring, especially in April, which makes this period particularly important for implementing soil conservation measures.

Point pattern analysis applied to flood and landslide damage events in Switzerland (1972-2009)

NASA Astrophysics Data System (ADS)

Barbería, Laura; Schulte, Lothar; Carvalho, Filipe; Peña, Juan Carlos

2017-04-01

Damage caused by meteorological and hydrological extreme events depends on many factors, not only on hazard, but also on exposure and vulnerability. In order to reach a better understanding of the relation of these complex factors, their spatial pattern and underlying processes, the spatial dependency between values of damage recorded at sites of different distances can be investigated by point pattern analysis. For the Swiss flood and landslide damage database (1972-2009) first steps of point pattern analysis have been carried out. The most severe events have been selected (severe, very severe and catastrophic, according to GEES classification, a total number of 784 damage points) and Ripley's K-test and L-test have been performed, amongst others. For this purpose, R's library spatstat has been used. The results confirm that the damage points present a statistically significant clustered pattern, which could be connected to prevalence of damages near watercourses and also to rainfall distribution of each event, together with other factors. On the other hand, bivariate analysis shows there is no segregated pattern depending on process type: flood/debris flow vs landslide. This close relation points to a coupling between slope and fluvial processes, connectivity between small-size and middle-size catchments and the influence of spatial distribution of precipitation, temperature (snow melt and snow line) and other predisposing factors such as soil moisture, land-cover and environmental conditions. Therefore, further studies will investigate the relationship between the spatial pattern and one or more covariates, such as elevation, distance from watercourse or land use. The final goal will be to perform a regression model to the data, so that the adjusted model predicts the intensity of the point process as a function of the above mentioned covariates.

Schistosomiasis Breeding Environment Situation Analysis in Dongting Lake Area

NASA Astrophysics Data System (ADS)

Li, Chuanrong; Jia, Yuanyuan; Ma, Lingling; Liu, Zhaoyan; Qian, Yonggang

2013-01-01

Monitoring environmental characteristics, such as vegetation, soil moisture et al., of Oncomelania hupensis (O. hupensis)’ spatial/temporal distribution is of vital importance to the schistosomiasis prevention and control. In this study, the relationship between environmental factors derived from remotely sensed data and the density of O. hupensis was analyzed by a multiple linear regression model. Secondly, spatial analysis of the regression residual was investigated by the semi-variogram method. Thirdly, spatial analysis of the regression residual and the multiple linear regression model were both employed to estimate the spatial variation of O. hupensis density. Finally, the approach was used to monitor and predict the spatial and temporal variations of oncomelania of Dongting Lake region, China. And the areas of potential O. hupensis habitats were predicted and the influence of Three Gorges Dam (TGB)project on the density of O. hupensis was analyzed.

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.

Acoustic fill factors for a 120 inch diameter fairing

NASA Technical Reports Server (NTRS)

Lee, Y. Albert

1992-01-01

Data from the acoustic test of a 120-inch diameter payload fairing were collected and an analysis of acoustic fill factors were performed. Correction factors for obtaining a weighted spatial average of the interior sound pressure level (SPL) were derived based on this database and a normalized 200-inch diameter fairing database. The weighted fill factors were determined and compared with statistical energy analysis (VAPEPS code) derived fill factors. The comparison is found to be reasonable.

Spatially distributed modeling of soil organic carbon across China with improved accuracy

NASA Astrophysics Data System (ADS)

Li, Qi-quan; Zhang, Hao; Jiang, Xin-ye; Luo, Youlin; Wang, Chang-quan; Yue, Tian-xiang; Li, Bing; Gao, Xue-song

2017-06-01

There is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.

Optimizing Pedestrian-Friendly Walking Path for the First and Last Mile Transit Journey by Using the Analytical Network Process (anp) Decision Model and GIS Network Analysis

NASA Astrophysics Data System (ADS)

Naharudin, N.; Ahamad, M. S. S.; Sadullah, A. F. M.

2017-10-01

Every transit trip begins and ends with pedestrian travel. People need to walk to access the transit services. However, their choice to walk depends on many factors including the connectivity, level of comfort and safety. These factors can influence the pleasantness of riding the transit itself, especially during the first/last mile (FLM) journey. This had triggered few studies attempting to measure the pedestrian-friendliness a walking environment can offer. There were studies that implement the pedestrian experience on walking to assess the pedestrian-friendliness of a walking environment. There were also studies that use spatial analysis to measure it based on the path connectivity and accessibility to public facilities and amenities. Though both are good, but the perception-based studies and spatial analysis can be combined to derive more holistic results. This paper proposes a framework for selecting a pedestrian-friendly path for the FLM transit journey by using the two techniques (perception-based and spatial analysis). First, the degree of importance for the factors influencing a good walking environment will be aggregated by using Analytical Network Process (ANP) decision rules based on people's preferences on those factors. The weight will then be used as attributes in the GIS network analysis. Next, the network analysis will be performed to find a pedestrian-friendly walking route based on the priorities aggregated by ANP. It will choose routes passing through the preferred attributes accordingly. The final output is a map showing pedestrian-friendly walking path for the FLM transit journey.

Hydrology Affects Environmental and Spatial Structuring of Microalgal Metacommunities in Tropical Pacific Coast Wetlands.

PubMed

Rojo, Carmen; Mesquita-Joanes, Francesc; Monrós, Juan S; Armengol, Javier; Sasa, Mahmood; Bonilla, Fabián; Rueda, Ricardo; Benavent-Corai, José; Piculo, Rubén; Segura, M Matilde

2016-01-01

The alternating climate between wet and dry periods has important effects on the hydrology and therefore on niche-based processes of water bodies in tropical areas. Additionally, assemblages of microorganism can show spatial patterns, in the form of a distance decay relationship due to their size or life form. We aimed to test spatial and environmental effects, modulated by a seasonal flooding climatic pattern, on the distribution of microalgae in 30 wetlands of a tropical dry forest region: the Pacific coast of Costa Rica and Nicaragua. Three surveys were conducted corresponding to the beginning, the highest peak, and the end of the hydrological year during the wet season, and species abundance and composition of planktonic and benthic microalgae was determined. Variation partitioning analysis (as explained by spatial distance or environmental factors) was applied to each seasonal dataset by means of partial redundancy analysis. Our results show that microalgal assemblages were structured by spatial and environmental factors depending on the hydrological period of the year. At the onset of hydroperiod and during flooding, neutral effects dominated community dynamics, but niche-based local effects resulted in more structured algal communities at the final periods of desiccating water bodies. Results suggest that climate-mediated effects on hydrology can influence the relative role of spatial and environmental factors on metacommunities of microalgae. Such variability needs to be accounted in order to describe accurately community dynamics in tropical coastal wetlands.

Hydrology Affects Environmental and Spatial Structuring of Microalgal Metacommunities in Tropical Pacific Coast Wetlands

PubMed Central

Rojo, Carmen; Mesquita-Joanes, Francesc; Monrós, Juan S.; Armengol, Javier; Sasa, Mahmood; Bonilla, Fabián; Rueda, Ricardo; Benavent-Corai, José; Piculo, Rubén; Segura, M. Matilde

2016-01-01

The alternating climate between wet and dry periods has important effects on the hydrology and therefore on niche-based processes of water bodies in tropical areas. Additionally, assemblages of microorganism can show spatial patterns, in the form of a distance decay relationship due to their size or life form. We aimed to test spatial and environmental effects, modulated by a seasonal flooding climatic pattern, on the distribution of microalgae in 30 wetlands of a tropical dry forest region: the Pacific coast of Costa Rica and Nicaragua. Three surveys were conducted corresponding to the beginning, the highest peak, and the end of the hydrological year during the wet season, and species abundance and composition of planktonic and benthic microalgae was determined. Variation partitioning analysis (as explained by spatial distance or environmental factors) was applied to each seasonal dataset by means of partial redundancy analysis. Our results show that microalgal assemblages were structured by spatial and environmental factors depending on the hydrological period of the year. At the onset of hydroperiod and during flooding, neutral effects dominated community dynamics, but niche-based local effects resulted in more structured algal communities at the final periods of desiccating water bodies. Results suggest that climate-mediated effects on hydrology can influence the relative role of spatial and environmental factors on metacommunities of microalgae. Such variability needs to be accounted in order to describe accurately community dynamics in tropical coastal wetlands. PMID:26900916

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

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Rachmawati, Ro'fah

2014-03-01

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

Accounting for biotic spatial variability in fields: Case of resistance screening against sunflower Verticillium wilt

PubMed Central

Missonnier, Hélène; Jacques, Alban; Bang, JiSu; Daydé, Jean; Mirleau-Thebaud, Virginie

2017-01-01

In breeding for disease resistance, the magnitude of the genetic response is difficult to appreciate because of environmental stresses that interact with the plant genotype. We discuss herein the fundamental problems in breeding for disease resistance with the aim being to better understand the interactions between plant, pathogen, and spatial patterns. The goal of this study is to fine tune breeding decisions by incorporating spatial patterns of such biotic factors into the definition of disease-occurrence probability. We use a preexisting statistics method based on geostatistics for a descriptive analysis of biotic factors for trial quality control. The plant-population structure used for spatial-pattern analysis consists of two F1-hybrid cultivars, defined as symptomatic and asymptomatic controls with respect to the studied pathogen. The controls are inserted at specific locations to establish a grid arrangement over the field that include the F1-hybrid cultivars under evaluation. We characterize the spatial structure of the pathogen population and of the general plant environment—with undetermined but present abiotic constraints—not by using direct notation such as flower time or rainfall but by using plant behavior (i.e., leaf symptom severity, indirect notation). The analysis indicates areas with higher or lower risk of disease and reveals a correlation between the symptomatic control and the effective level of disease for sunflowers. This result suggests that the pathogen and/or abiotic components are major factors in determining the probability that a plant develops the disease, which could lead to a misinterpretation of plant resistance. PMID:28817567

Accounting for biotic spatial variability in fields: Case of resistance screening against sunflower Verticillium wilt.

PubMed

Missonnier, Hélène; Jacques, Alban; Bang, JiSu; Daydé, Jean; Mirleau-Thebaud, Virginie

2017-01-01

In breeding for disease resistance, the magnitude of the genetic response is difficult to appreciate because of environmental stresses that interact with the plant genotype. We discuss herein the fundamental problems in breeding for disease resistance with the aim being to better understand the interactions between plant, pathogen, and spatial patterns. The goal of this study is to fine tune breeding decisions by incorporating spatial patterns of such biotic factors into the definition of disease-occurrence probability. We use a preexisting statistics method based on geostatistics for a descriptive analysis of biotic factors for trial quality control. The plant-population structure used for spatial-pattern analysis consists of two F1-hybrid cultivars, defined as symptomatic and asymptomatic controls with respect to the studied pathogen. The controls are inserted at specific locations to establish a grid arrangement over the field that include the F1-hybrid cultivars under evaluation. We characterize the spatial structure of the pathogen population and of the general plant environment-with undetermined but present abiotic constraints-not by using direct notation such as flower time or rainfall but by using plant behavior (i.e., leaf symptom severity, indirect notation). The analysis indicates areas with higher or lower risk of disease and reveals a correlation between the symptomatic control and the effective level of disease for sunflowers. This result suggests that the pathogen and/or abiotic components are major factors in determining the probability that a plant develops the disease, which could lead to a misinterpretation of plant resistance.

[Analysis of influence on spatial distribution of fishing ground for Antarctic krill fishery in the northern South Shetland Islands based on GWR model].

PubMed

Chen, Lyu Feng; Zhu, Guo Ping

2018-03-01

Based on Antarctic krill fishery and marine environmental data collected by scientific observers, using geographically weighted regression (GWR) model, we analyzed the effects of the factors with spatial attributes, i.e., depth of krill swarm (DKS) and distance from fishing position to shore (DTS), and sea surface temperature (SST), on the spatial distribution of fishing ground in the northern South Shetland Islands. The results showed that there was no significant aggregation in spatial distribution of catch per unit fishing effort (CPUE). Spatial autocorrelations (positive) among three factors were observed in 2010 and 2013, but were not in 2012 and 2016. Results from GWR model showed that the extent for the impacts on spatial distribution of CPUEs varied among those three factors, following the order DKS>SST>DTS. Compared to the DKS and DTS, the impact of SST on the spatial distribution of CPUEs presented adverse trend in the eastern and western parts of the South Shetland Islands. Negative correlations occurred for the spatial effects of DKS and DTS on distribution of CPUEs, though with inter-annual and regional variation. Our results provide metho-dological reference for researches on the underlying mechanism for fishing ground formation for Antarctic krill fishery.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2016-01-01

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

Modelling soil properties in a crop field located in Croatia

NASA Astrophysics Data System (ADS)

Bogunovic, Igor; Pereira, Paulo; Millan, Mesic; Percin, Aleksandra; Zgorelec, Zeljka

2016-04-01

Development of tillage activities had negative effects on soil quality as destruction of soil horizons, compacting and aggregates destruction, increasing soil erosion and loss of organic matter. For a better management in order to mitigate the effects of intensive soil management in land degradation it is fundamental to map the spatial distribution of soil properties (Brevik et al., 2016). The understanding the distribution of the variables in space is very important for a sustainable management, in order to identify areas that need a potential intervention and decrease the economic losses (Galiati et al., 2016). The objective of this work is study the spatial distribution of some topsoil properties as clay, fine silt, coarse silt, fine sand, coarse sand, penetration resistance, moisture and organic matter in a crop field located in Croatia. A grid with 275x25 (625 m2) was designed and a total of 48 samples were collected. Previous to data modelling, data normality was checked using the Shapiro wilk-test. As in previous cases (Pereira et al., 2015), data did not followed the normal distribution, even after a logarithmic (Log), square-root, and box cox transformation. Thus, for modeling proposes, we used the log transformed data, since was the closest to the normality. In order to identify groups among the variables we applied a principal component analysis (PCA), based on the correlation matrix. On average clay content was 15.47% (±3.23), fine silt 24.24% (±4.08), coarse silt 35.34% (±3.12), fine sand 20.93% (±4.68), coarse sand 4.02% (±1.69), penetration resistance 0.66 MPa (±0.28), organic matter 1.51% (±0.25) and soil moisture 32.04% (±3.27). The results showed that the PCA identified three factors explained at least one of the variables. The first factor had high positive loadings in soil clay, fine silt and organic matter and a high negative loading in fine sand. The second factor had high positive loadings in coarse sand and moisture and a high negative loading in coarse silt. Finally, the factor 3 had a positive loading in penetration resistance. The loadings of these three factors were mapped using ordinary kriging method. The analysis of incremental spatial correlation identified that the highest spatial correlation in the factor 1 was identified at 41.87 m, in factor 2 at 75.61 m and factor 3 at 143.9 m. In the case of factor 2, the maximum peak of spatial autocorrelation was significant at a p<0.05. This showed that the variable has a random distribution, as confirmed with the Moran's I spatial correlation analysis. In relation to the other factors the maximum peaks were significantly clustered at a p<0.001. These results suggested that the each factor has a different spatial pattern and the studied soil properties explained by each factor had a different spatial distribution. References Breivik, E., Baumgarten, A., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Jordán, A. Soil mapping, classification, and modelling: history and future directions. Geoderma, 264, Part B, 256-274. Galiati, A., Gristina, L., Crescimanno, Barone, E., Novara, A. (2016) Towards more efficient incentives for agri-environment measures in degraded and eroded vineyards. Land Degradation and Development, DOI: 10.1002/ldr.2389 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2015) Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, 26, 180-192.

Spatial environmental risk factors for pedestrian injury collisions in Ciudad Juárez, Mexico (2008-2009): implications for urban planning.

PubMed

Fuentes, Cesar Mario; Hernandez, Vladimir

2013-01-01

The aim of this study is to examine the spatial distribution of pedestrian injury collisions and analyse the environmental (social and physical) risk factors in Ciudad Juarez, Mexico. More specifically, this study investigates the influence of land use, density, traffic and socio-economic characteristics. This cross sectional study is based on pedestrian injury collision data that were collected by the Municipal Transit Police during 2008-2009. This research presents an analysis of vehicle-pedestrian collisions and their spatial risk determinants using mixed methods that included (1) spatial/geographical information systems (GIS) analysis of pedestrian collision data and (2) ordinary least squares (OLS) regression analysis to explain the density of pedestrian collisions data. In our model, we found a higher probability for pedestrian collisions in census tracts with population and employment density, large concentration of commercial/retail land uses and older people (65 and more). Interventions to alleviate this situation including transportation planning such as decentralisation of municipal transport system, investment in road infrastructure - density of traffic lights, pedestrian crossing, road design, improves lane demarcation. Besides, land use planning interventions should be implemented in commercial/retail areas, in particular separating pedestrian and vehicular spaces.

Analysis of suicide mortality in Brazil: spatial distribution and socioeconomic context.

PubMed

Dantas, Ana P; Azevedo, Ulicélia N de; Nunes, Aryelly D; Amador, Ana E; Marques, Marilane V; Barbosa, Isabelle R

2018-01-01

To perform a spatial analysis of suicide mortality and its correlation with socioeconomic indicators in Brazilian municipalities. This is an ecological study with Brazilian municipalities as a unit of analysis. Data on deaths from suicide and contextual variables were analyzed. The spatial distribution, intensity and significance of the clusters were analyzed with the global Moran index, MoranMap and local indicators of spatial association (LISA), seeking to identify patterns through geostatistical analysis. A total of 50,664 deaths from suicide were registered in Brazil between 2010 and 2014. The average suicide mortality rate in Brazil was 5.23/100,000 population. The Brazilian municipalities presenting the highest rates were Taipas do Tocantins, state of Tocantins (79.68 deaths per 100,000 population), Itaporã, state of Mato Grosso do Sul (75.15 deaths per 100,000 population), Mampituba, state of Rio Grande do Sul (52.98 deaths per 100,000 population), Paranhos, state of Mato Grosso do Sul (52.41 deaths per 100,000 population), and Monjolos, state of Minas Gerais (52.08 deaths per 100,000 population). Although weak spatial autocorrelation was observed for suicide mortality (I = 0.2608), there was a formation of clusters in the South. In the bivariate spatial and classical analysis, no correlation was observed between suicide mortality and contextual variables. Suicide mortality in Brazil presents a weak spatial correlation and low or no spatial relationship with socioeconomic factors.

Representation of vegetation by continental data sets derived from NOAA-AVHRR data

NASA Technical Reports Server (NTRS)

Justice, C. O.; Townshend, J. R. G.; Kalb, V. L.

1991-01-01

Images of the normalized difference vegetation index (NDVI) are examined with specific attention given to the effect of spatial scales on the understanding of surface phenomena. A scale variance analysis is conducted on NDVI annual and seasonal images of Africa taken from 1987 NOAA-AVHRR data at spatial scales ranging from 8-512 km. The scales at which spatial variation takes place are determined and the relative magnitude of the variations are considered. Substantial differences are demonstrated, notably an increase in spatial variation with coarsening spatial resolution. Different responses in scale variance as a function of spatial resolution are noted in an analysis of maximum value composites for February and September; the difference is most marked in areas with very seasonal vegetation. The spatial variation at different scales is attributed to different factors, and methods involving the averaging of areas of transition and surface heterogeneity can oversimplify surface conditions. The spatial characteristics and the temporal variability of areas should be considered to accurately apply satellite data to global models.

Dynamics of land change in India: a fine-scale spatial analysis

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Roy, P. S.; Sharma, Y.; Jain, A. K.; Ramachandran, R.; Joshi, P. K.

2015-12-01

Land is scarce in India: India occupies 2.4% of worlds land area, but supports over 1/6th of worlds human and livestock population. This high population to land ratio, combined with socioeconomic development and increasing consumption has placed tremendous pressure on India's land resources for food, feed, and fuel. In this talk, we present contemporary (1985 to 2005) spatial estimates of land change in India using national-level analysis of Landsat imageries. Further, we investigate the causes of the spatial patterns of change using two complementary lines of evidence. First, we use statistical models estimated at macro-scale to understand the spatial relationships between land change patterns and their concomitant drivers. This analysis using our newly compiled extensive socioeconomic database at village level (~630,000 units), is 100x higher in spatial resolution compared to existing datasets, and covers over 200 variables. The detailed socioeconomic data enabled the fine-scale spatial analysis with Landsat data. Second, we synthesized information from over 130 survey based case studies on land use drivers in India to complement our macro-scale analysis. The case studies are especially useful to identify unobserved variables (e.g. farmer's attitude towards risk). Ours is the most detailed analysis of contemporary land change in India, both in terms of national extent, and the use of detailed spatial information on land change, socioeconomic factors, and synthesis of case studies.

Modality-specificity of Selective Attention Networks.

PubMed

Stewart, Hannah J; Amitay, Sygal

2015-01-01

To establish the modality specificity and generality of selective attention networks. Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled "general attention." The third component was labeled "auditory attention," as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as "spatial orienting" and "spatial conflict," respectively-they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task-all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

Natural Hazard Susceptibility Assessment for Road Planning Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Karlsson, Caroline S. J.; Kalantari, Zahra; Mörtberg, Ulla; Olofsson, Bo; Lyon, Steve W.

2017-11-01

Inadequate infrastructural networks can be detrimental to society if transport between locations becomes hindered or delayed, especially due to natural hazards which are difficult to control. Thus determining natural hazard susceptible areas and incorporating them in the initial planning process, may reduce infrastructural damages in the long run. The objective of this study was to evaluate the usefulness of expert judgments for assessing natural hazard susceptibility through a spatial multi-criteria analysis approach using hydrological, geological, and land use factors. To utilize spatial multi-criteria analysis for decision support, an analytic hierarchy process was adopted where expert judgments were evaluated individually and in an aggregated manner. The estimates of susceptible areas were then compared with the methods weighted linear combination using equal weights and factor interaction method. Results showed that inundation received the highest susceptibility. Using expert judgment showed to perform almost the same as equal weighting where the difference in susceptibility between the two for inundation was around 4%. The results also showed that downscaling could negatively affect the susceptibility assessment and be highly misleading. Susceptibility assessment through spatial multi-criteria analysis is useful for decision support in early road planning despite its limitation to the selection and use of decision rules and criteria. A natural hazard spatial multi-criteria analysis could be used to indicate areas where more investigations need to be undertaken from a natural hazard point of view, and to identify areas thought to have higher susceptibility along existing roads where mitigation measures could be targeted after in-situ investigations.

Natural Hazard Susceptibility Assessment for Road Planning Using Spatial Multi-Criteria Analysis.

PubMed

Karlsson, Caroline S J; Kalantari, Zahra; Mörtberg, Ulla; Olofsson, Bo; Lyon, Steve W

2017-11-01

Inadequate infrastructural networks can be detrimental to society if transport between locations becomes hindered or delayed, especially due to natural hazards which are difficult to control. Thus determining natural hazard susceptible areas and incorporating them in the initial planning process, may reduce infrastructural damages in the long run. The objective of this study was to evaluate the usefulness of expert judgments for assessing natural hazard susceptibility through a spatial multi-criteria analysis approach using hydrological, geological, and land use factors. To utilize spatial multi-criteria analysis for decision support, an analytic hierarchy process was adopted where expert judgments were evaluated individually and in an aggregated manner. The estimates of susceptible areas were then compared with the methods weighted linear combination using equal weights and factor interaction method. Results showed that inundation received the highest susceptibility. Using expert judgment showed to perform almost the same as equal weighting where the difference in susceptibility between the two for inundation was around 4%. The results also showed that downscaling could negatively affect the susceptibility assessment and be highly misleading. Susceptibility assessment through spatial multi-criteria analysis is useful for decision support in early road planning despite its limitation to the selection and use of decision rules and criteria. A natural hazard spatial multi-criteria analysis could be used to indicate areas where more investigations need to be undertaken from a natural hazard point of view, and to identify areas thought to have higher susceptibility along existing roads where mitigation measures could be targeted after in-situ investigations.

Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis

PubMed Central

Li, Xing; Wang, Jin; Lin, Hualiang; Chen, Lingling; Wu, Zhifeng; Ma, Wenjun

2017-01-01

Background: Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods: Moran’s I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results: Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature (q = 0.33) was the dominant factor of dengue fever, followed by precipitation (q = 0.24), road density (q = 0.24), and water body area (q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions: Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou. PMID:28714925

Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis.

PubMed

Cao, Zheng; Liu, Tao; Li, Xing; Wang, Jin; Lin, Hualiang; Chen, Lingling; Wu, Zhifeng; Ma, Wenjun

2017-07-17

Background : Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods : Moran's I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results : Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature ( q = 0.33) was the dominant factor of dengue fever, followed by precipitation ( q = 0.24), road density ( q = 0.24), and water body area ( q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions : Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou.

Psychometric analysis of five measures of spatial ability.

PubMed

Hogan, Thomas P

2012-02-01

This study analyzed psychometric properties of five measures of spatial ability on 96 young adults, with supplementary analysis for three of the measures on another sample of 71 young adults. Two measures were taken from the widely cited Kit of Factor-Referenced Cognitive Tests and three other measures were taken from a relatively new source originally intended as laboratory demonstrations. Previous research provided limited information on the psychometric properties of the measures. All five measures yielded adequate reliability and loaded on a single factor. Three measures yielded markedly skewed distributions. Two measures showed clear sex differences with men scoring higher but this difference seemed contaminated by a speed factor; three measures did not show a sex difference. Recommendations for use of the measures in future studies are provided.

Inter-annual variability of carbon fluxes in temperate forest ecosystems: effects of biotic and abiotic factors

NASA Astrophysics Data System (ADS)

Chen, M.; Keenan, T. F.; Hufkens, K.; Munger, J. W.; Bohrer, G.; Brzostek, E. R.; Richardson, A. D.

2014-12-01

Carbon dynamics in terrestrial ecosystems are influenced by both abiotic and biotic factors. Abiotic factors, such as variation in meteorological conditions, directly drive biophysical and biogeochemical processes; biotic factors, referring to the inherent properties of the ecosystem components, reflect the internal regulating effects including temporal dynamics and memory. The magnitude of the effect of abiotic and biotic factors on forest ecosystem carbon exchange has been suggested to vary at different time scales. In this study, we design and conduct a model-data fusion experiment to investigate the role and relative importance of the biotic and abiotic factors for inter-annual variability of the net ecosystem CO2 exchange (NEE) of temperate deciduous forest ecosystems in the Northeastern US. A process-based model (FöBAAR) is parameterized at four eddy-covariance sites using all available flux and biometric measurements. We conducted a "transplant" modeling experiment, that is, cross- site and parameter simulations with different combinations of site meteorology and parameters. Using wavelet analysis and variance partitioning techniques, analysis of model predictions identifies both spatial variant and spatially invariant parameters. Variability of NEE was primarily modulated by gross primary productivity (GPP), with relative contributions varying from hourly to yearly time scales. The inter-annual variability of GPP and NEE is more regulated by meteorological forcing, but spatial variability in certain model parameters (biotic response) has more substantial effects on the inter-annual variability of ecosystem respiration (Reco) through the effects on carbon pools. Both the biotic and abiotic factors play significant roles in modulating the spatial and temporal variability in terrestrial carbon cycling in the region. Together, our study quantifies the relative importance of both, and calls for better understanding of them to better predict regional CO2 exchanges.

Analyzing Protein Clusters on the Plasma Membrane: Application of Spatial Statistical Analysis Methods on Super-Resolution Microscopy Images.

PubMed

Paparelli, Laura; Corthout, Nikky; Pavie, Benjamin; Annaert, Wim; Munck, Sebastian

2016-01-01

The spatial distribution of proteins within the cell affects their capability to interact with other molecules and directly influences cellular processes and signaling. At the plasma membrane, multiple factors drive protein compartmentalization into specialized functional domains, leading to the formation of clusters in which intermolecule interactions are facilitated. Therefore, quantifying protein distributions is a necessity for understanding their regulation and function. The recent advent of super-resolution microscopy has opened up the possibility of imaging protein distributions at the nanometer scale. In parallel, new spatial analysis methods have been developed to quantify distribution patterns in super-resolution images. In this chapter, we provide an overview of super-resolution microscopy and summarize the factors influencing protein arrangements on the plasma membrane. Finally, we highlight methods for analyzing clusterization of plasma membrane proteins, including examples of their applications.

Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals.

PubMed

Nijhof, Carl O P; Huijbregts, Mark A J; Golsteijn, Laura; van Zelm, Rosalie

2016-04-01

We compared the influence of spatial variability in environmental characteristics and the uncertainty in measured substance properties of seven chemicals on freshwater fate factors (FFs), representing the residence time in the freshwater environment, and on exposure factors (XFs), representing the dissolved fraction of a chemical. The influence of spatial variability was quantified using the SimpleBox model in which Europe was divided in 100 × 100 km regions, nested in a regional (300 × 300 km) and supra-regional (500 × 500 km) scale. Uncertainty in substance properties was quantified by means of probabilistic modelling. Spatial variability and parameter uncertainty were expressed by the ratio k of the 95%ile and 5%ile of the FF and XF. Our analysis shows that spatial variability ranges in FFs of persistent chemicals that partition predominantly into one environmental compartment was up to 2 orders of magnitude larger compared to uncertainty. For the other (less persistent) chemicals, uncertainty in the FF was up to 1 order of magnitude larger than spatial variability. Variability and uncertainty in freshwater XFs of the seven chemicals was negligible (k < 1.5). We found that, depending on the chemical and emission scenario, accounting for region-specific environmental characteristics in multimedia fate modelling, as well as accounting for parameter uncertainty, can have a significant influence on freshwater fate factor predictions. Therefore, we conclude that it is important that fate factors should not only account for parameter uncertainty, but for spatial variability as well, as this further increases the reliability of ecotoxicological impacts in LCA. Copyright © 2016 Elsevier Ltd. All rights reserved.

A spatial analysis of the determinants of pneumonia and influenza hospitalizations in Ontario (1992-2001).

PubMed

Crighton, Eric J; Elliott, Susan J; Moineddin, Rahim; Kanaroglou, Pavlos; Upshur, Ross

2007-04-01

Previous research on the determinants of pneumonia and influenza has focused primarily on the role of individual level biological and behavioural risk factors resulting in partial explanations and largely curative approaches to reducing the disease burden. This study examines the geographic patterns of pneumonia and influenza hospitalizations and the role that broad ecologic-level factors may have in determining them. We conducted a county level, retrospective, ecologic study of pneumonia and influenza hospitalizations in the province of Ontario, Canada, between 1992 and 2001 (N=241,803), controlling for spatial dependence in the data. Non-spatial and spatial regression models were estimated using a range of environmental, social, economic, behavioural, and health care predictors. Results revealed low education to be positively associated with hospitalization rates over all age groups and both genders. The Aboriginal population variable was also positively associated in most models except for the 65+-year age group. Behavioural factors (daily smoking and heavy drinking), environmental factors (passive smoking, poor housing, temperature), and health care factors (influenza vaccination) were all significantly associated in different age and gender-specific models. The use of spatial error regression models allowed for unbiased estimation of regression parameters and their significance levels. These findings demonstrate the importance of broad age and gender-specific population-level factors in determining pneumonia and influenza hospitalizations, and illustrate the need for place and population-specific policies that take these factors into consideration.

Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate-siliciclastic alternations of the Hainich CZE, central Germany

NASA Astrophysics Data System (ADS)

Kohlhepp, Bernd; Lehmann, Robert; Seeber, Paul; Küsel, Kirsten; Trumbore, Susan E.; Totsche, Kai U.

2017-12-01

The quality of near-surface groundwater reservoirs is controlled, but also threatened, by manifold surface-subsurface interactions. Vulnerability studies typically evaluate the variable interplay of surface factors (land management, infiltration patterns) and subsurface factors (hydrostratigraphy, flow properties) in a thorough way, but disregard the resulting groundwater quality. Conversely, hydrogeochemical case studies that address the chemical evolution of groundwater often lack a comprehensive analysis of the structural buildup. In this study, we aim to reconstruct the actual spatial groundwater quality pattern from a synoptic analysis of the hydrostratigraphy, lithostratigraphy, pedology and land use in the Hainich Critical Zone Exploratory (Hainich CZE). This CZE represents a widely distributed yet scarcely described setting of thin-bedded mixed carbonate-siliciclastic strata in hillslope terrains. At the eastern Hainich low-mountain hillslope, bedrock is mainly formed by alternated marine sedimentary rocks of the Upper Muschelkalk (Middle Triassic) that partly host productive groundwater resources. Spatial patterns of the groundwater quality of a 5.4 km long well transect are derived by principal component analysis and hierarchical cluster analysis. Aquifer stratigraphy and geostructural links were deduced from lithological drill core analysis, mineralogical analysis, geophysical borehole logs and mapping data. Maps of preferential recharge zones and recharge potential were deduced from digital (soil) mapping, soil survey data and field measurements of soil hydraulic conductivities (Ks). By attributing spatially variable surface and subsurface conditions, we were able to reconstruct groundwater quality clusters that reflect the type of land management in their preferential recharge areas, aquifer hydraulic conditions and cross-formational exchange via caprock sinkholes or ascending flow. Generally, the aquifer configuration (spatial arrangement of strata, valley incision/outcrops) and related geostructural links (enhanced recharge areas, karst phenomena) control the role of surface factors (input quality and locations) vs. subsurface factors (water-rock interaction, cross-formational flow) for groundwater quality in the multi-layered aquifer system. Our investigation reveals general properties of alternating sequences in hillslope terrains that are prone to forming multi-layered aquifer systems. This synoptic analysis is fundamental and indispensable for a mechanistic understanding of ecological functioning, sustainable resource management and protection.

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

PubMed

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

2014-01-01

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

Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China

PubMed Central

2013-01-01

Background Human brucellosis incidence in China has been increasing dramatically since 1999. However, epidemiological features and potential factors underlying the re-emergence of the disease remain less understood. Methods Data on human and animal brucellosis cases at the county scale were collected for the year 2004 to 2010. Also collected were environmental and socioeconomic variables. Epidemiological features including spatial and temporal patterns of the disease were characterized, and the potential factors related to the spatial heterogeneity and the temporal trend of were analysed using Poisson regression analysis, Granger causality analysis, and autoregressive distributed lag (ADL) models, respectively. Results The epidemic showed a significantly higher spatial correlation with the number of sheep and goats than swine and cattle. The disease was most prevalent in grassland areas with elevation between 800–1,600 meters. The ADL models revealed that local epidemics were correlated with comparatively lower temperatures and less sunshine in winter and spring, with a 1–7 month lag before the epidemic peak in May. Conclusions Our findings indicate that human brucellosis tended to occur most commonly in grasslands at moderate elevation where sheep and goats were the predominant livestock, and in years with cooler winter and spring or less sunshine. PMID:24238301

Spatial controls of occurrence and spread of wildfires in the Missouri Ozark Highlands.

PubMed

Yang, Jian; He, Hong S; Shifley, Stephen R

2008-07-01

Understanding spatial controls on wildfires is important when designing adaptive fire management plans and optimizing fuel treatment locations on a forest landscape. Previous research about this topic focused primarily on spatial controls for fire origin locations alone. Fire spread and behavior were largely overlooked. This paper contrasts the relative importance of biotic, abiotic, and anthropogenic constraints on the spatial pattern of fire occurrence with that on burn probability (i.e., the probability that fire will spread to a particular location). Spatial point pattern analysis and landscape succession fire model (LANDIS) were used to create maps to show the contrast. We quantified spatial controls on both fire occurrence and fire spread in the Midwest Ozark Highlands region, USA. This area exhibits a typical anthropogenic surface fire regime. We found that (1) human accessibility and land ownership were primary limiting factors in shaping clustered fire origin locations; (2) vegetation and topography had a negligible influence on fire occurrence in this anthropogenic regime; (3) burn probability was higher in grassland and open woodland than in closed-canopy forest, even though fire occurrence density was less in these vegetation types; and (4) biotic and abiotic factors were secondary descriptive ingredients for determining the spatial patterns of burn probability. This study demonstrates how fire occurrence and spread interact with landscape patterns to affect the spatial distribution of wildfire risk. The application of spatial point pattern data analysis would also be valuable to researchers working on landscape forest fire models to integrate historical ignition location patterns in fire simulation.

Spatial modeling of potential woody biomass flow

Treesearch

Woodam Chung; Nathaniel Anderson

2012-01-01

The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

The calibration analysis of soil infiltration formula in farmland scale

NASA Astrophysics Data System (ADS)

Qian, Tao; Han, Na Na; Chang, Shuan Ling

2018-06-01

Soil infiltration characteristic is an important basis of farmland scale parameter estimation. Based on 12 groups of double-loop infiltration tests conducted in the test field of tianjin agricultural university west campus. Based on the calibration theory and the combination of statistics, the calibration analysis of phillips formula was carried out and the spatial variation characteristics of the calibration factor were analyzed. Results show that in study area based on the soil stability infiltration rate A calculate calibration factor αA calibration effect is best, that is suitable for the area formula of calibration infiltration and αA variation coefficient is 0.3234, with A certain degree of spatial variability.

Determinants of the geographic distribution of Puumala virus and Lyme borreliosis infections in Belgium.

PubMed

Linard, Catherine; Lamarque, Pénélope; Heyman, Paul; Ducoffre, Geneviève; Luyasu, Victor; Tersago, Katrien; Vanwambeke, Sophie O; Lambin, Eric F

2007-05-02

Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover and land use influence disease transmission by controlling both the spatial distribution of vectors or hosts, and the probability of contact with susceptible human populations. The objective of this study was to combine environmental and socio-economic factors to explain the spatial distribution of two emerging human diseases in Belgium, Puumala virus (PUUV) and Lyme borreliosis. Municipalities were taken as units of analysis. Negative binomial regressions including a correction for spatial endogeneity show that the spatial distribution of PUUV and Lyme borreliosis infections are associated with a combination of factors linked to the vector and host populations, to human behaviours, and to landscape attributes. Both diseases are associated with the presence of forests, which are the preferred habitat for vector or host populations. The PUUV infection risk is higher in remote forest areas, where the level of urbanisation is low, and among low-income populations. The Lyme borreliosis transmission risk is higher in mixed landscapes with forests and spatially dispersed houses, mostly in wealthy peri-urban areas. The spatial dependence resulting from a combination of endogenous and exogenous processes could be accounted for in the model on PUUV but not for Lyme borreliosis. A large part of the spatial variation in disease risk can be explained by environmental and socio-economic factors. The two diseases not only are most prevalent in different regions but also affect different groups of people. Combining these two criteria may increase the efficiency of information campaigns through appropriate targeting.

Climatic factors associated with amyotrophic lateral sclerosis: a spatial analysis from Taiwan.

PubMed

Tsai, Ching-Piao; Tzu-Chi Lee, Charles

2013-11-01

Few studies have assessed the spatial association of amyotrophic lateral sclerosis (ALS) incidence in the world. The aim of this study was to identify the association of climatic factors and ALS incidence in Taiwan. A total of 1,434 subjects with the primary diagnosis of ALS between years 1997 and 2008 were identified in the national health insurance research database. The diagnosis was also verified by the national health insurance programme, which had issued and providing them with "serious disabling disease (SDD) certificates". Local indicators of spatial association were employed to investigate spatial clustering of age-standardised incidence ratios in the townships of the study area. Spatial regression was utilised to reveal any association of annual average climatic factors and ALS incidence for the 12-year study period. The climatic factors included the annual average time of sunlight exposure, average temperature, maximum temperature, minimum temperature, atmospheric pressure, rainfall, relative humidity and wind speed with spatial autocorrelation controlled. Significant correlations were only found for exposure to sunlight and rainfall and it was similar in both genders. The annual average of the former was found to be negatively correlated with ALS, while the latter was positively correlated with ALS incidence. While accepting that ALS is most probably multifactorial, it was concluded that sunlight deprivation and/or rainfall are associated to some degree with ALS incidence in Taiwan.

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.

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis

PubMed Central

Zhou, Ying; Levy, Jonathan I

2007-01-01

Background There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. Methods We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. Results We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100–400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200–500 m for nitrogen dioxide and 100–300 m for ultrafine particle counts. Conclusion First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize. PMID:17519039

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

PubMed

Wu, Siqi; Joseph, Antony; Hammonds, Ann S; Celniker, Susan E; Yu, Bin; Frise, Erwin

2016-04-19

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

Influence of Topographic and Hydrographic Factors on the Spatial Distribution of Leptospirosis Disease in São Paulo County, Brazil: An Approach Using Geospatial Techniques and GIS Analysis

NASA Astrophysics Data System (ADS)

Ferreira, M. C.; Ferreira, M. F. M.

2016-06-01

Leptospirosis is a zoonosis caused by Leptospira genus bacteria. Rodents, especially Rattus norvegicus, are the most frequent hosts of this microorganism in the cities. The human transmission occurs by contact with urine, blood or tissues of the rodent and contacting water or mud contaminated by rodent urine. Spatial patterns of concentration of leptospirosis are related to the multiple environmental and socioeconomic factors, like housing near flooding areas, domestic garbage disposal sites and high-density of peoples living in slums located near river channels. We used geospatial techniques and geographical information system (GIS) to analysing spatial relationship between the distribution of leptospirosis cases and distance from rivers, river density in the census sector and terrain slope factors, in Sao Paulo County, Brazil. To test this methodology we used a sample of 183 geocoded leptospirosis cases confirmed in 2007, ASTER GDEM2 data, hydrography and census sectors shapefiles. Our results showed that GIS and geospatial analysis techniques improved the mapping of the disease and permitted identify the spatial pattern of association between location of cases and spatial distribution of the environmental variables analyzed. This study showed also that leptospirosis cases might be more related to the census sectors located on higher river density areas and households situated at shorter distances from rivers. In the other hand, it was not possible to assert that slope terrain contributes significantly to the location of leptospirosis cases.

A comparative study: The spatial organization of pre and post disaster house in traditional cultured area; study case: Core house project in Kasongan, Yogyakarta, Indonesia

NASA Astrophysics Data System (ADS)

Agustina, Vicky

2017-11-01

This study involves revealing the spatial organization typology differences between pre and post disaster houses through core house project in Kasongan, Yogyakarta, Indonesia. The goal is to gain understanding of the way of traditional cultured people re-shaping their space and environment after disaster reconstruction through the core house and find the factors that determine the form. The study has been done by comparing and analyzing the spatial properties and functions between both objects using justified graph technique which is one of the basic methodology that able to identify how people are organized in space. Upon the comparison and analysis of these aspects, it appears that the old house size has impact toward significant changes of the spatial properties also the dwellers put physical factor over culture when evaluating the present house. Through these findings, this study highlights that spatial organization of traditional house has temporal spatial value and the core house concept had influenced the changes of the local spatial behaviour and their perception of their house standard.

Modality-specificity of Selective Attention Networks

PubMed Central

Stewart, Hannah J.; Amitay, Sygal

2015-01-01

Objective: To establish the modality specificity and generality of selective attention networks. Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled “general attention.” The third component was labeled “auditory attention,” as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as “spatial orienting” and “spatial conflict,” respectively—they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task—all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific. PMID:26635709

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

PubMed

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System

PubMed Central

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China. PMID:25157827

Integrating GIS with AHP and Fuzzy Logic to generate hand, foot and mouth disease hazard zonation (HFMD-HZ) model in Thailand

NASA Astrophysics Data System (ADS)

Samphutthanon, R.; Tripathi, N. K.; Ninsawat, S.; Duboz, R.

2014-12-01

The main objective of this research was the development of an HFMD hazard zonation (HFMD-HZ) model by applying AHP and Fuzzy Logic AHP methodologies for weighting each spatial factor such as disease incidence, socio-economic and physical factors. The outputs of AHP and FAHP were input into a Geographic Information Systems (GIS) process for spatial analysis. 14 criteria were selected for analysis as important factors: disease incidence over 10 years from 2003 to 2012, population density, road density, land use and physical features. The results showed a consistency ratio (CR) value for these main criteria of 0.075427 for AHP, the CR for FAHP results was 0.092436. As both remained below the threshold of 0.1, the CR value were acceptable. After linking to actual geospatial data (disease incidence 2013) through spatial analysis by GIS for validation, the results of the FAHP approach were found to match more accurately than those of the AHP approach. The zones with the highest hazard of HFMD outbreaks were located in two main areas in central Muang Chiang Mai district including suburbs and Muang Chiang Rai district including the vicinity. The produced hazardous maps may be useful for organizing HFMD protection plans.

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.

Empirical Research on Spatial Diffusion Process of Knowledge Spillovers

NASA Astrophysics Data System (ADS)

Jin, Xuehui

2018-02-01

Firstly, this paper gave a brief review of the core issues of previous studies on spatial distribution of knowledge spillovers. That laid the theoretical foundation for further research. Secondly, this paper roughly described the diffusion process of solar patents in Bejing-Tianjin-Hebei and the Pearl River Delta regions by means of correlation analysis based on patent information of the application date and address of patentee. After that, this paper introduced the variables of spatial distance, knowledge absorptive capacity, knowledge gap and pollution control and built the empirical model of patent, and then collecting data to test them. The results showed that knowledge absorptive capacity was the most significant factor than the other three, followed by the knowledge gap. The influence of spatial distance on knowledge spillovers was limited and the most weak influence factor was pollution control.

[Assessment on ecological security spatial differences of west areas of Liaohe River based on GIS].

PubMed

Wang, Geng; Wu, Wei

2005-09-01

Ecological security assessment and early warning research have spatiality; non-linearity; randomicity, it is needed to deal with much spatial information. Spatial analysis and data management are advantages of GIS, it can define distribution trend and spatial relations of environmental factors, and show ecological security pattern graphically. The paper discusses the method of ecological security spatial differences of west areas of Liaohe River based on GIS and ecosystem non-health. First, studying on pressure-state-response (P-S-R) assessment indicators system, investigating in person and gathering information; Second, digitizing the river, applying fuzzy AHP to put weight, quantizing and calculating by fuzzy comparing; Last, establishing grid data-base; expounding spatial differences of ecological security by GIS Interpolate and Assembly.

Spatial analysis of malaria in Anhui province, China

PubMed Central

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

2008-01-01

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

Spatial Variability in ADHD-Related Behaviors Among Children Born to Mothers Residing Near the New Bedford Harbor Superfund Site

PubMed Central

Vieira, Verónica M.; Fabian, M. Patricia; Webster, Thomas F.; Levy, Jonathan I.; Korrick, Susan A.

2017-01-01

Abstract Attention-deficit/hyperactivity disorder (ADHD) has an uncertain etiology, with potential contributions from different risk factors such as prenatal environmental exposure to organochlorines and metals, social risk factors, and genetics. The degree to which geographic variability in ADHD is independent of, or explained by, risk factors may provide etiological insight. We investigated determinants of geographic variation in ADHD-related behaviors among children living near the polychlorinated biphenyl–contaminated New Bedford Harbor (NBH) Superfund site in Massachusetts. Participants were 573 children recruited at birth (1993–1998) who were born to mothers residing near the NBH site. We assessed ADHD-related behaviors at age 8 years using Conners’ Teacher Rating Scale–Revised: Long Version. Adjusted generalized additive models were used to smooth the association of pregnancy residence with ADHD-related behaviors and assess whether prenatal organochlorine or metal exposures, sociodemographic factors, or other factors explained spatial patterns. Models that adjusted for child's age and sex displayed significantly increased ADHD-related behavior among children whose mothers resided west of the NBH site during pregnancy. These spatial patterns persisted after adjusting for prenatal exposure to organochlorines and metals but were no longer significant after controlling for sociodemographic factors. The findings underscore the value of spatial analysis in identifying high-risk subpopulations and evaluating candidate risk factors. PMID:28444119

[Habitat factor analysis for Torreya grandis cv. Merrillii based on spatial information technology].

PubMed

Wang, Xiao-ming; Wang, Ke; Ao, Wei-jiu; Deng, Jin-song; Han, Ning; Zhu, Xiao-yun

2008-11-01

Torreya grandis cv. Merrillii, a tertiary survival plant, is a rare tree species of significant economic value and expands rapidly in China. Its special habitat factor analysis has the potential value to provide guide information for its planting, management, and sustainable development, because the suitable growth conditions for this tree species are special and strict. In this paper, the special habitat factors for T. grandis cv. Merrillii in its core region, i.e., in seven villages of Zhuji City, Zhejiang Province were analyzed with Principal Component Analysis (PCA) and a series of data, such as IKONOS image, Digital Elevation Model (DEM), and field survey data supported by the spatial information technology. The results showed that T. grandis cv. Merrillii exhibited high selectivity of environmental factors such as elevation, slope, and aspect. 96.22% of T. grandis cv. Merrillii trees were located at the elevation from 300 to 600 m, 97.52% of them were found to present on the areas whose slope was less than 300, and 74.43% of them distributed on sunny and half-sunny slopes. The results of PCA analysis indicated that the main environmental factors affecting the habitat of T. grandis cv. Merrillii were moisture, heat, and soil nutrients, and moisture might be one of the most important ecological factors for T. grandis cv. Merrillii due to the unique biological and ecological characteristics of the tree species.

Geo-additive modelling of malaria in Burundi

PubMed Central

2011-01-01

Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. Methods The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. Results The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. Conclusions In this paper, semiparametric models are used to model the effects of both climatic covariates and spatial effects on malaria distribution in Burundi. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature of the previous month. From the spatial effects, important spatial patterns of malaria that are related to factors other than climatic variables are identified. Potential explanations (factors) could be related to socio-economic conditions, food shortage, limited access to health care service, precarious housing, promiscuity, poor hygienic conditions, limited access to drinking water, land use (rice paddies for example), displacement of the population (due to armed conflicts). PMID:21835010

Characterisation factors for life cycle impact assessment of sound emissions.

PubMed

Cucurachi, S; Heijungs, R

2014-01-15

Noise is a serious stressor affecting the health of millions of citizens. It has been suggested that disturbance by noise is responsible for a substantial part of the damage to human health. However, no recommended approach to address noise impacts was proposed by the handbook for life cycle assessment (LCA) of the European Commission, nor are characterisation factors (CFs) and appropriate inventory data available in commonly used databases. This contribution provides CFs to allow for the quantification of noise impacts on human health in the LCA framework. Noise propagation standards and international reports on acoustics and noise impacts were used to define the model parameters. Spatial data was used to calculate spatially-defined CFs in the form of 10-by-10-km maps. The results of this analysis were combined with data from the literature to select input data for representative archetypal situations of emission (e.g. urban day with a frequency of 63 Hz, rural night at 8000 Hz, etc.). A total of 32 spatial and 216 archetypal CFs were produced to evaluate noise impacts at a European level (i.e. EU27). The possibility of a user-defined characterisation factor was added to support the possibility of portraying the situation of full availability of information, as well as a highly-localised impact analysis. A Monte Carlo-based quantitative global sensitivity analysis method was applied to evaluate the importance of the input factors in determining the variance of the output. The factors produced are ready to be implemented in the available LCA databases and software. The spatial approach and archetypal approach may be combined and selected according to the amount of information available and the life cycle under study. The framework proposed and used for calculations is flexible enough to be expanded to account for impacts on target subjects other than humans and to continents other than Europe. © 2013 Elsevier B.V. All rights reserved.

Income Inequality across Micro and Meso Geographic Scales in the Midwestern United States, 1979-2009

ERIC Educational Resources Information Center

Peters, David J.

2012-01-01

This article examines the spatial distribution of income inequality and the socioeconomic factors affecting it using spatial analysis techniques across 16,285 block groups, 5,050 tracts, and 618 counties in the western part of the North Central Region of the United States. Different geographic aggregations result in different inequality outcomes,…

Spatial distribution and influence factors of interprovincial terrestrial physical geographical names in China

NASA Astrophysics Data System (ADS)

Zhang, S.; Wang, Y.; Ju, H.

2017-12-01

The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. Based on toponomy dictionaries and different thematic maps, the attributes and the spatial extent of the interprovincial terrestrial physical geographical names (ITPGN, including terrain ITPGN and water ITPGN) were extracted. The coefficient of variation and Moran's I were combined together to measure the spatial variation and spatial association of ITPGN. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. The results showed that 11325 ITPGN were extracted, including 7082 terrain ITPGN and 4243 water ITPGN. Hunan Province had the largest number of ITPGN in China, and Shanghai had the smallest number. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. Further analysis showed that the number of ITPGN was positively related with the relative elevation and the population where the relative elevation was lower than 2000m and the population was less than 50 million. But the number of ITPGN showed a negative relationship with the two factors when their values became larger, indicating a large number of unnamed entities existed in complex terrain areas and a decreasing number of terrestrial physical geographical entities in densely populated area. Based on these analysis, we suggest the government take the ITPGN as management units to realize a balance development between different parts of the entities and strengthen the geographical names census and the nomination of unnamed interprovincial physical geographical entities. This study also demonstrated that the methods of literature survey, coefficient of variation and Moran's I can be combined to enhance the understanding of the spatial pattern of ITPGN.

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

PubMed Central

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

2014-01-01

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

[Spatial epidemiological study on malaria epidemics in Hainan province].

PubMed

Wen, Liang; Shi, Run-He; Fang, Li-Qun; Xu, De-Zhong; Li, Cheng-Yi; Wang, Yong; Yuan, Zheng-Quan; Zhang, Hui

2008-06-01

To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, A.B.; McMahon, G.

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States - higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, Anne B.; McMahon, Gerard

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Determinants of the geographic distribution of Puumala virus and Lyme borreliosis infections in Belgium

PubMed Central

Linard, Catherine; Lamarque, Pénélope; Heyman, Paul; Ducoffre, Geneviève; Luyasu, Victor; Tersago, Katrien; Vanwambeke, Sophie O; Lambin, Eric F

2007-01-01

Background Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover and land use influence disease transmission by controlling both the spatial distribution of vectors or hosts, and the probability of contact with susceptible human populations. The objective of this study was to combine environmental and socio-economic factors to explain the spatial distribution of two emerging human diseases in Belgium, Puumala virus (PUUV) and Lyme borreliosis. Municipalities were taken as units of analysis. Results Negative binomial regressions including a correction for spatial endogeneity show that the spatial distribution of PUUV and Lyme borreliosis infections are associated with a combination of factors linked to the vector and host populations, to human behaviours, and to landscape attributes. Both diseases are associated with the presence of forests, which are the preferred habitat for vector or host populations. The PUUV infection risk is higher in remote forest areas, where the level of urbanisation is low, and among low-income populations. The Lyme borreliosis transmission risk is higher in mixed landscapes with forests and spatially dispersed houses, mostly in wealthy peri-urban areas. The spatial dependence resulting from a combination of endogenous and exogenous processes could be accounted for in the model on PUUV but not for Lyme borreliosis. Conclusion A large part of the spatial variation in disease risk can be explained by environmental and socio-economic factors. The two diseases not only are most prevalent in different regions but also affect different groups of people. Combining these two criteria may increase the efficiency of information campaigns through appropriate targeting. PMID:17474974

Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China.

PubMed

Li, Yifan; Wang, Juanle; Gao, Mengxu; Fang, Liqun; Liu, Changhua; Lyu, Xin; Bai, Yongqing; Zhao, Qiang; Li, Hairong; Yu, Hongjie; Cao, Wuchun; Feng, Liqiang; Wang, Yanjun; Zhang, Bin

2017-05-26

Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The impact degree of these factors on TBE risks has the following descending order: temperature, relative humidity, vegetation coverage, precipitation and topography. A high-risk area with a triangle shape was determined in the central part of Hulunbuir; the low-risk area is located in the two belts next to the outside edge of the central triangle. The TBE risk distribution revealed that the impact of the geographic factors changed depending on the heterogeneity.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011.

PubMed

Li, Li; Qian, Jun; Ou, Chun-Quan; Zhou, Ying-Xue; Guo, Cui; Guo, Yuming

2014-07-01

There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001-2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. Copyright © 2014 Elsevier Ltd. All rights reserved.

Factors affecting private forest landowner interest in ecosystem management: linking spatial and survey data.

PubMed

Jacobson, Michael G

2002-10-01

Many factors influence forest landowner management decisions. This study examines landowner decisions regarding participation in ecosystem management activities, such as a landscape corridor cutting across their private lands. Landscape corridors are recognized worldwide as an important tool in biodiversity conservation. For ecosystem management activities to occur in areas dominated by a multitude of small private forest landholdings, landowner participation and cooperation is necessary. Data from a survey of landowners combined with an analysis of their land's spatial attributes is used to assess their interest in ecosystem management. Results suggest that spatial attributes are not good predictors of an owner's interest in ecosystem management. Other factors such as attitudes and opinions about the environment are more effective in explaining landowner interest. The results have implications for any land manager using GIS data and implementing ecosystem management activities on private forestland.

Spatial analysis for prevalence of type 2 diabetes mellitus - A state investigation

NASA Astrophysics Data System (ADS)

Zainal, Siti Salsabilah Nabilah; Masnan, Maz Jamilah; Amin, Nor Azrita Mohd; Mohamed, Nordin

2017-11-01

Type 2 Diabetes Mellitus (T2DM) is a chronic and non-communicable disease, which is characterized as the cause of premature deaths in the world. Unfortunately, Malaysia is one of the many countries facing this epidemic. Based on the increasing current trend of T2DM patients' cases from the National Diabetes Registry (NDR) Report from 2009 to 2012, there were approximately 2.6 million adults aged 18 years and above living with diabetes disease in Malaysia. Thus, this study aims to (i) perform preliminary spatial analysis for the prevalence of T2DM patients based on some factors, (ii) map the findings of the analyses according to some spatial properties, and (iii) analyze the pattern of diagnosed T2DM patients based on the studied factors. The studied population is one of the highest prevalence states of T2DM in Malaysia. This study is expected to reveal some demographic patterns that probably significant to this alarming epidemic.

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

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

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

DOE PAGES

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.; ...

2016-04-06

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Influence of Different Factors on Relative Air Humidity in Zaragoza, Spain

NASA Astrophysics Data System (ADS)

Cuadrat, José M.

2015-03-01

In this study, the spatial patterns of relative air humidity and its relation to urban, geographical and meteorological factors in the city of Zaragoza (Spain) is discussed. We created a relative humidity database by means of 32 urban transects. Data were taken on different days and with different weather types. This data set was used to map the mean spatial distribution of urban dry island (UDI). Using stepwise multiple regression analysis and Landsat ETM+ images the relationships between mean UDI and the main geographic-urban factors: topography, land cover and surface reflectivity, have been analyzed. Different spatial patterns of UDI were determined using Principal Component Analysis (Varimax rotation). The three components extracted accounted for 91% of the total variance. PC1 accounted for the most general patterns (similar to mean UDI); PC2 showed a shift of dry areas to the SE and PC3 a shift to NW. Using data on wind direction in Zaragoza, we have found that the displacement of dry areas to the SE (PC 2) was greater during NW winds while the shift to the NW (PC 3) was produced mainly by SE winds.

[Spatial tendency of urban land use in new Yinzhou Town of Ningbo City, Zhejiang Province of East China].

PubMed

Jiang, Wen-Wei; Guo, Hui-Hui; Mei, Yan-Xia

2012-03-01

By adopting gradient analysis combining with the analysis of urban land use degree, this paper studied the spatial layout characteristics of residential and industrial lands in new Yinzhou Town, and explored the location characters of various urban land use by selecting public green land, public facilities, and road as the location advantage factors. Gradient analysis could effectively connect with the spatial layout of urban land use, and quantitatively depict the spatial character of urban land use. In the new town, there was a new urban spatial center mostly within the radius of 2 km, namely, the urban core area had obvious location advantage in the cross-shaft direction urban development. On the south of Yinzhou Avenue, the urban hinterland would be constructed soon. In the future land use of the new town, the focus would be the reasonable vicissitude of industrial land after the adjustment of industrial structure, the high-efficient intensive use of the commercial land restricted by the compulsive condition of urban core area, and the agricultural land protection in the southeastern urban-rural fringe.

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

Factor Analysis of the WAIS and Twenty French-Kit Reference Tests.

ERIC Educational Resources Information Center

Ramsey, Philip H.

1979-01-01

The Wechsler Adult Intelligence Scale (WAIS) and 20 tests from the French Kit were administered to over 100 undergraduates. Analyses revealed ten factors: verbal comprehension, visualization, memory span, syllogistic reasoning, general reasoning, induction, mechanical knowledge, number facility, spatial orientation, and associative memory.…

Spatial analysis of the etiology of amyotrophic lateral sclerosis among 1991 Gulf War veterans.

PubMed

Miranda, Marie Lynn; Alicia Overstreet Galeano, M; Tassone, Eric; Allen, Kelli D; Horner, Ronnie D

2008-11-01

Veterans of the 1991 Gulf War have an increased risk of amyotrophic lateral sclerosis (ALS), but the etiology is unknown. This study sought to identify geographic areas with elevated risk for the later development of ALS among military personnel who served in the first Gulf War. A unified geographic information system (GIS) was constructed to allow analysis of secondary data on troop movements in the 1991 Gulf War theatre in the Persian Gulf region including Iraq, northern Saudi Arabia, and Kuwait. We fit Bayesian Poisson regression models to adjust for potential risk factors, including one relatively discrete environmental exposure, and to identify areas associated with elevated risk of ALS. We found that service in particular locations of the Gulf was associated with an elevated risk for later developing ALS, both before and after adjustment for branch of service and potential of exposure to chemical warfare agents in and around Khamisiyah, Iraq. Specific geographic locations of troop units within the 1991 Gulf War theatre are associated with an increased risk for the subsequent development of ALS among members of those units. The identified spatial locations represent the logical starting points in the search for potential etiologic factors of ALS among Gulf War veterans. Of note, for locations where the relative odds of subsequently developing ALS are among the highest, specific risk factors, whether environmental or occupationally related, have not been identified. The results of spatial models can be used to subsequently look for risk factors that follow the spatial pattern of elevated risk.

Modal energy analysis for mechanical systems excited by spatially correlated loads

NASA Astrophysics Data System (ADS)

Zhang, Peng; Fei, Qingguo; Li, Yanbin; Wu, Shaoqing; Chen, Qiang

2018-10-01

MODal ENergy Analysis (MODENA) is an energy-based method, which is proposed to deal with vibroacoustic problems. The performance of MODENA on the energy analysis of a mechanical system under spatially correlated excitation is investigated. A plate/cavity coupling system excited by a pressure field is studied in a numerical example, in which four kinds of pressure fields are involved, which include the purely random pressure field, the perfectly correlated pressure field, the incident diffuse field, and the turbulent boundary layer pressure fluctuation. The total energies of subsystems differ to reference solution only in the case of purely random pressure field and only for the non-excited subsystem (the cavity). A deeper analysis on the scale of modal energy is further conducted via another numerical example, in which two structural modes excited by correlated forces are coupled with one acoustic mode. A dimensionless correlation strength factor is proposed to determine the correlation strength between modal forces. Results show that the error on modal energy increases with the increment of the correlation strength factor. A criterion is proposed to establish a link between the error and the correlation strength factor. According to the criterion, the error is negligible when the correlation strength is weak, in this situation the correlation strength factor is less than a critical value.

a Buffer Analysis Based on Co-Location Algorithm

NASA Astrophysics Data System (ADS)

Zhou, G.; Huang, S.; Wang, H.; Zhang, R.; Wang, Q.; Sha, H.; Liu, X.; Pan, Q.

2018-05-01

Buffer analysis is a common tool of spatial analysis, which deals with the problem of proximity in GIS. Buffer analysis researches the relationship between the center object and other objects around a certain distance. Buffer analysis can make the complicated problem be more scientifically and visually, and provide valuable information for users. Over the past decades, people have done a lot of researches on buffer analysis. Along with the constantly improvement of spatial analysis accuracy needed by people, people hope that the results of spatial analysis can be more exactly express the actual situation. Due to the influence of some certain factors, the impact scope and contact range of a geographic elements on the surrounding objects are uncertain. As all we know, each object has its own characteristics and changing rules in the nature. They are both independent and relative to each other. However, almost all the generational algorithms of existing buffer analysis are based on fixed buffer distance, which do not consider the co-location relationship among instances. Consequently, it is a waste of resource to retrieve the useless information, and useful information is ignored.

Spatial Variability in ADHD-Related Behaviors Among Children Born to Mothers Residing Near the New Bedford Harbor Superfund Site.

PubMed

Vieira, Verónica M; Fabian, M Patricia; Webster, Thomas F; Levy, Jonathan I; Korrick, Susan A

2017-05-15

Attention-deficit/hyperactivity disorder (ADHD) has an uncertain etiology, with potential contributions from different risk factors such as prenatal environmental exposure to organochlorines and metals, social risk factors, and genetics. The degree to which geographic variability in ADHD is independent of, or explained by, risk factors may provide etiological insight. We investigated determinants of geographic variation in ADHD-related behaviors among children living near the polychlorinated biphenyl-contaminated New Bedford Harbor (NBH) Superfund site in Massachusetts. Participants were 573 children recruited at birth (1993-1998) who were born to mothers residing near the NBH site. We assessed ADHD-related behaviors at age 8 years using Conners' Teacher Rating Scale-Revised: Long Version. Adjusted generalized additive models were used to smooth the association of pregnancy residence with ADHD-related behaviors and assess whether prenatal organochlorine or metal exposures, sociodemographic factors, or other factors explained spatial patterns. Models that adjusted for child's age and sex displayed significantly increased ADHD-related behavior among children whose mothers resided west of the NBH site during pregnancy. These spatial patterns persisted after adjusting for prenatal exposure to organochlorines and metals but were no longer significant after controlling for sociodemographic factors. The findings underscore the value of spatial analysis in identifying high-risk subpopulations and evaluating candidate risk factors. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Spatial memory tasks in rodents: what do they model?

PubMed

Morellini, Fabio

2013-10-01

The analysis of spatial learning and memory in rodents is commonly used to investigate the mechanisms underlying certain forms of human cognition and to model their dysfunction in neuropsychiatric and neurodegenerative diseases. Proper interpretation of rodent behavior in terms of spatial memory and as a model of human cognitive functions is only possible if various navigation strategies and factors controlling the performance of the animal in a spatial task are taken into consideration. The aim of this review is to describe the experimental approaches that are being used for the study of spatial memory in rats and mice and the way that they can be interpreted in terms of general memory functions. After an introduction to the classification of memory into various categories and respective underlying neuroanatomical substrates, I explain the concept of spatial memory and its measurement in rats and mice by analysis of their navigation strategies. Subsequently, I describe the most common paradigms for spatial memory assessment with specific focus on methodological issues relevant for the correct interpretation of the results in terms of cognitive function. Finally, I present recent advances in the use of spatial memory tasks to investigate episodic-like memory in mice.

Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model

NASA Astrophysics Data System (ADS)

Verburg, Peter H.; Soepboer, Welmoed; Veldkamp, A.; Limpiada, Ramil; Espaldon, Victoria; Mastura, Sharifah S. A.

2002-09-01

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

Modeling the spatial dynamics of regional land use: the CLUE-S model.

PubMed

Verburg, Peter H; Soepboer, Welmoed; Veldkamp, A; Limpiada, Ramil; Espaldon, Victoria; Mastura, Sharifah S A

2002-09-01

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

Environmental factors explaining the vegetation patterns in a temperate peatland.

PubMed

Pellerin, Stéphanie; Lagneau, Louis-Adrien; Lavoie, Martin; Larocque, Marie

2009-08-01

Although ombrotrophic temperate peatlands are important ecosystems for maintaining biodiversity in eastern North America, the environmental factors influencing their flora are only partly understood. The relationships between plant species distribution and environmental factors were thus studied within the oldest temperate peatland of Québec. Plant assemblages were identified by cluster analysis while CCA was used to related vegetation gradients to environmental factors. Five assemblages were identified; three typical of open bog and two characterized by more minerotrophic vegetation. Thicker peat deposit was encounter underlying the bog assemblages while higher water table level and percentage of free surface water distinguished the minerotrophic assemblages. Overall, the floristic patterns observed were spatially structured along the margins and the expanse. The most important environmental factors explaining this spatial gradient were the percentage of free surface water and the highest water-table level.

Separating the effects of environment and space on tree species distribution: from population to community.

PubMed

Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang

2013-01-01

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. PMID:27827946

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

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.

Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas.

PubMed

Song, Weize; Jia, Haifeng; Li, Zhilin; Tang, Deliang

2018-08-01

Urban air pollutant distribution is a concern in environmental and health studies. Particularly, the spatial distribution of NO 2 and PM 2.5 , which represent photochemical smog and haze pollution in urban areas, is of concern. This paper presents a study quantifying the seasonal differences between urban NO 2 and PM 2.5 distributions in Foshan, China. A geographical semi-variogram analysis was conducted to delineate the spatial variation in daily NO 2 and PM 2.5 concentrations. The data were collected from 38 sites in the government-operated monitoring network. The results showed that the total spatial variance of NO 2 is 38.5% higher than that of PM 2.5 . The random spatial variance of NO 2 was 1.6 times than that of PM 2.5 . The nugget effect (i.e., random to total spatial variance ratio) values of NO 2 and PM 2.5 were 29.7 and 20.9%, respectively. This indicates that urban NO 2 distribution was affected by both local and regional influencing factors, while urban PM 2.5 distribution was dominated by regional influencing factors. NO 2 had a larger seasonally averaged spatial autocorrelation distance (48km) than that of PM 2.5 (33km). The spatial range of NO 2 autocorrelation was larger in winter than the other seasons, and PM 2.5 has a smaller range of spatial autocorrelation in winter than the other seasons. Overall, the geographical semi-variogram analysis is a very effective method to enrich the understanding of NO 2 and PM 2.5 distributions. It can provide scientific evidences for the buffering radius selection of spatial predictors for land use regression models. It will also be beneficial for developing the targeted policies and measures to reduce NO 2 and PM 2.5 pollution levels. Copyright © 2018 Elsevier B.V. All rights reserved.

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China).

PubMed

Cui, Lifang; Wang, Lunche; Singh, Ramesh P; Lai, Zhongping; Jiang, Liangliang; Yao, Rui

2018-05-23

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments.

PubMed

Krami, Loghman Khoda; Amiri, Fazel; Sefiyanian, Alireza; Shariff, Abdul Rashid B Mohamed; Tabatabaie, Tayebeh; Pradhan, Biswajeet

2013-12-01

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF(G)) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF(G) techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF(G) technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.

Geographic variations in cervical cancer risk in San Luis Potosí state, Mexico: A spatial statistical approach.

PubMed

Terán-Hernández, Mónica; Ramis-Prieto, Rebeca; Calderón-Hernández, Jaqueline; Garrocho-Rangel, Carlos Félix; Campos-Alanís, Juan; Ávalos-Lozano, José Antonio; Aguilar-Robledo, Miguel

2016-09-29

Worldwide, Cervical Cancer (CC) is the fourth most common type of cancer and cause of death in women. It is a significant public health problem, especially in low and middle-income/Gross Domestic Product (GDP) countries. In the past decade, several studies of CC have been published, that identify the main modifiable and non-modifiable CC risk factors for Mexican women. However, there are no studies that attempt to explain the residual spatial variation in CC incidence In Mexico, i.e. spatial variation that cannot be ascribed to known, spatially varying risk factors. This paper uses a spatial statistical methodology that takes into account spatial variation in socio-economic factors and accessibility to health services, whilst allowing for residual, unexplained spatial variation in risk. To describe residual spatial variations in CC risk, we used generalised linear mixed models (GLMM) with both spatially structured and unstructured random effects, using a Bayesian approach to inference. The highest risk is concentrated in the southeast, where the Matlapa and Aquismón municipalities register excessive risk, with posterior probabilities greater than 0.8. The lack of coverage of Cervical Cancer-Screening Programme (CCSP) (RR 1.17, 95 % CI 1.12-1.22), Marginalisation Index (RR 1.05, 95 % CI 1.03-1.08), and lack of accessibility to health services (RR 1.01, 95 % CI 1.00-1.03) were significant covariates. There are substantial differences between municipalities, with high-risk areas mainly in low-resource areas lacking accessibility to health services for CC. Our results clearly indicate the presence of spatial patterns, and the relevance of the spatial analysis for public health intervention. Ignoring the spatial variability means to continue a public policy that does not tackle deficiencies in its national CCSP and to keep disadvantaging and disempowering Mexican women in regard to their health care.

Effects of spatial disturbance on common loon nest site selection and territory success

USGS Publications Warehouse

McCarthy, K.P.; DeStefano, S.

2011-01-01

The common loon (Gavia immer) breeds during the summer on northern lakes and water bodies that are also often desirable areas for aquatic recreation and human habitation. In northern New England, we assessed how the spatial nature of disturbance affects common loon nest site selection and territory success. We found through classification and regression analysis that distance to and density of disturbance factors can be used to classify observed nest site locations versus random points, suggesting that these factors affect loon nest site selection (model 1: Correct classification = 75%, null = 50%, K = 0.507, P < 0.001; model 2: Correct classification = 78%, null = 50%, K = 0.551, P < 0.001). However, in an exploratory analysis, we were unable to show a relation between spatial disturbance variables and breeding success (P = 0.595, R 2 = 0.436), possibly because breeding success was so low during the breeding seasons of 2007-2008. We suggest that by selecting nest site locations that avoid disturbance factors, loons thereby limit the effect that disturbance will have on their breeding success. Still, disturbance may force loons to use sub-optimal nesting habitat, limiting the available number of territories, and overall productivity. We advise that management efforts focus on limiting disturbance factors to allow breeding pairs access to the best nesting territories, relieving disturbance pressures that may force sub-optimal nest placement. ?? 2011 The Wildlife Society.

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

PubMed

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

2015-07-01

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

Modeling the impact of spatial relationships on horizontal curve safety.

PubMed

Findley, Daniel J; Hummer, Joseph E; Rasdorf, William; Zegeer, Charles V; Fowler, Tyler J

2012-03-01

The curved segments of roadways are more hazardous because of the additional centripetalforces exerted on a vehicle, driver expectations, and other factors. The safety of a curve is dependent on various factors, most notably by geometric factors, but the location of a curve in relation to other curves is also thought to influence the safety of those curves because of a driver's expectation to encounter additional curves. The link between an individual curve's geometric characteristics and its safety performance has been established, but spatial considerations are typically not included in a safety analysis. The spatial considerations included in this research consisted of four components: distance to adjacent curves, direction of turn of the adjacent curves, and radius and length of the adjacent curves. The primary objective of this paper is to quantify the spatial relationship between adjacent horizontal curves and horizontal curve safety using a crash modification factor. Doing so enables a safety professional to more accurately estimate safety to allocate funding to reduce or prevent future collisions and more efficiently design new roadway sections to minimize crash risk where there will be a series of curves along a route. The most important finding from this research is the statistical significance of spatial considerations for the prediction of horizontal curve safety. The distances to adjacent curves were found to be a reliable predictor of observed collisions. This research recommends a model which utilizes spatial considerations for horizontal curve safety prediction in addition to current Highway Safety Manual prediction capabilities using individual curve geometric features. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatial synchrony in cisco recruitment

USGS Publications Warehouse

Myers, Jared T.; Yule, Daniel L.; Jones, Michael L.; Ahrenstorff, Tyler D.; Hrabik, Thomas R.; Claramunt, Randall M.; Ebener, Mark P.; Berglund, Eric K.

2015-01-01

We examined the spatial scale of recruitment variability for disparate cisco (Coregonus artedi) populations in the Great Lakes (n = 8) and Minnesota inland lakes (n = 4). We found that the scale of synchrony was approximately 400 km when all available data were utilized; much greater than the 50-km scale suggested for freshwater fish populations in an earlier global analysis. The presence of recruitment synchrony between Great Lakes and inland lake cisco populations supports the hypothesis that synchronicity is driven by climate and not dispersal. We also found synchrony in larval densities among three Lake Superior populations separated by 25–275 km, which further supports the hypothesis that broad-scale climatic factors are the cause of spatial synchrony. Among several candidate climate variables measured during the period of larval cisco emergence, maximum wind speeds exhibited the most similar spatial scale of synchrony to that observed for cisco. Other factors, such as average water temperatures, exhibited synchrony on broader spatial scales, which suggests they could also be contributing to recruitment synchrony. Our results provide evidence that abiotic factors can induce synchronous patterns of recruitment for populations of cisco inhabiting waters across a broad geographic range, and show that broad-scale synchrony of recruitment can occur in freshwater fish populations as well as those from marine systems.

Individual-Level Influences on Perceptions of Neighborhood Disorder: A Multilevel Analysis

ERIC Educational Resources Information Center

Latkin, Carl A.; German, Danielle; Hua, Wei; Curry, Aaron D.

2009-01-01

Health outcomes are associated with aggregate neighborhood measures and individual neighborhood perceptions. In this study, the authors sought to delineate individual, social network, and spatial factors that may influence perceptions of neighborhood disorder. Multilevel regression analysis showed that neighborhood perceptions were more negative…

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2017-12-01

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

Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Tang, Zhongqian; Zhang, Hua; Yi, Shanzhen; Xiao, Yangfan

2018-03-01

GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial variability and are accompanied with considerable uncertainty. It is, thus, important to incorporate spatial variability and uncertainty into GIS-based decision analysis procedures. This research develops a spatially explicit, probabilistic GIS-MCDA approach for the delineation of potentially flood susceptible areas. The approach integrates the probabilistic and the local ordered weighted averaging (OWA) methods via Monte Carlo simulation, to take into account the uncertainty related to criteria weights, spatial heterogeneity of preferences and the risk attitude of the analyst. The approach is applied to a pilot study for the Gucheng County, central China, heavily affected by the hazardous 2012 flood. A GIS database of six geomorphological and hydrometeorological factors for the evaluation of susceptibility was created. Moreover, uncertainty and sensitivity analysis were performed to investigate the robustness of the model. The results indicate that the ensemble method improves the robustness of the model outcomes with respect to variation in criteria weights and identifies which criteria weights are most responsible for the variability of model outcomes. Therefore, the proposed approach is an improvement over the conventional deterministic method and can provides a more rational, objective and unbiased tool for flood susceptibility evaluation.

Spatial analysis to identify hotspots of prevalence of schizophrenia.

PubMed

Moreno, Berta; García-Alonso, Carlos R; Negrín Hernández, Miguel A; Torres-González, Francisco; Salvador-Carulla, Luis

2008-10-01

The geographical distribution of mental health disorders is useful information for epidemiological research and health services planning. To determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain. The study included 774 patients with schizophrenia who were users of the community mental health care service in the area of South Granada. Spatial analysis (Kernel estimation) and Bayesian relative risks were used to locate potential hotspots. Availability and accessibility were both rated in each zone and spatial algebra was applied to identify hotspots in a particular zone. The age-corrected prevalence rate of schizophrenia was 2.86 per 1,000 population in the South Granada area. Bayesian analysis showed a relative risk varying from 0.43 to 2.33. The area analysed had a non-uniform spatial distribution of schizophrenia, with one main hotspot (zone S2). This zone had poor accessibility to and availability of mental health services. A municipality-based variation exists in the prevalence of schizophrenia and related disorders in the study area. Spatial analysis techniques are useful tools to analyse the heterogeneous distribution of a variable and to explain genetic/environmental factors in hotspots related with a lack of easy availability of and accessibility to adequate health care services.

Spatial epidemiology of Toxoplasma gondii infection in goats in Serbia.

PubMed

Djokic, Vitomir; Klun, Ivana; Musella, Vincenzo; Rinaldi, Laura; Cringoli, Giuseppe; Sotiraki, Smaragda; Djurkovic-Djakovic, Olgica

2014-05-01

A major risk factor for Toxoplasma gondii infection is consumption of undercooked meat. Increasing demand for goat meat is likely to promote the role of this animal for human toxoplasmosis. As there are virtually no data on toxoplasmosis in goats in Serbia, we undertook a cross-sectional serological study, including prediction modelling using geographical information systems (GIS). Sera from 431 goats reared in 143 households/farms throughout Serbia, sampled between January 2010 and September 2011, were examined for T. gondii antibodies by a modified agglutination test. Seroprevalence was 73.3% at the individual level and 84.6% at the farm level. Risk factor analysis showed above two-fold higher risk of infection for goats used for all purposes compared to dairy goats (P = 0.012), almost seven-fold higher risk for goats kept as sole species versus those kept with other animals (P = 0.001) and a two-fold lower risk for goats introduced from outside the farm compared to those raised on the farm (P = 0.027). Moreover, households/farms located in centre-eastern Serbia were found to be less often infected than those in northern Serbia (P = 0.004). The risk factor analysis was fully supported by spatial analysis based on a GIS database containing data on origin, serology, land cover, elevation, meteorology and a spatial prediction map based on kriging analysis, which showed western Serbia as the area most likely for finding goats positive for T. gondii and centre-eastern Serbia as the least likely. In addition, rainfall favoured seropositivity, whereas temperature, humidity and elevation did not.

Ecological factors associated with pandemic influenza A (H1N1) hospitalization rates in California, USA: a geospatial analysis.

PubMed

Maliszewski, Paul J; Wei, Ran

2011-11-01

The 2009 H1N1 influenza A virus subtype (H1N1) pandemic had a large impact in the United States of America (USA), causing an estimated 192,000 to 398,000 hospitalizations and 8,720 to 18,050 deaths between April 2009 and mid-March 2010. Recent research on the 2009 H1N1 pandemic has largely focused on individual, non-spatial demographic characterizations (e.g. age and race/ethnicity) associated with H1N1 hospitalizations. Broader ecological factors such as transportation use, land use and other socioeconomic factors are important aspects of influenza studies that have not been empirically examined. This research explores and identifies ecological factors associated with 2009 H1N1 pandemic hospitalization rates. We conducted a spatial regression analysis of county level hospitalization rates from 3 April to 15 September, 2009 obtained via the California Department of Public Health. Hospitalization rates were found to be spatially dependent. Public transportation usage rates and agricultural land use proportions were significant environmental factors positively related to hospitalization rates. Consistent with public health official's assumptions and existing evidence, county percentages of persons less than 18 years of age were positively associated with hospitalization. These findings help to clarify the limited consensus and dubious evidence on the role of broader ecological factors associated with pandemic influenza. A better understanding of the ecological risk factors associated with hospitalizations should also benefit public health officials with respect to their work aiming at improving emergency supply allocation and non-pharmaceutical intervention strategies in the context of an influenza pandemic.

Vegetation Cover Analysis in Shaanxi Province of China Based on Grid Pixel Ternd Analysis and Stability Evaluation

NASA Astrophysics Data System (ADS)

Yue, H.; Liu, Y.

2018-04-01

As a key factor affecting the biogeochemical cycle of human existence, terrestrial vegetation is vulnerable to natural environment and human activities, with obvious temporal and spatial characteristics. The change of vegetation cover will affect the ecological balance and environmental quality to a great extent. Therefore, the research on the causes and influencing factors of vegetation cover has become the focus of attention of scholars at home and abroad. In the evolution of human activities and natural environment, the vegetation coverage in Shaanxi has changed accordingly. Using MODIS/NDVI 2000-2014 time series data, using the method of raster pixel trend analysis, stability evaluation, rescaled range analysis and correlation analysis, the climatic factors in Shaanxi province were studied in the near 15 years vegetation spatial and temporal variation and influence of vegetation NDVI changes. The results show that NDVI in Shaanxi province in the near 15 years increased by 0.081, the increase of NDVI in Northern Shaanxi was obvious, and negative growth was found in some areas of Guanzhong, southern Shaanxi NDVI overall still maintained at a high level; the trend of vegetation change in Shaanxi province has obvious spatial differences, most of the province is a slight tendency to improve vegetation, there are many obvious improvement areas in Northern Shaanxi Province. Guanzhong area vegetation area decreased, the small range of variation of vegetation in Shaanxi province; the most stable areas are mainly concentrated in the southern, southern Yanan, Yulin, Xi'an area of Weinan changed greatly; Shaanxi Province in recent 15 a, the temperature and precipitation have shown an increasing trend, and the vegetation NDVI is more closely related to the average annual rainfall, with increase of 0.48 °C/10 years and 69.5 mm per year.

Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques.

PubMed

Wan, Yongshan; Qian, Yun; Migliaccio, Kati White; Li, Yuncong; Conrad, Cecilia

2014-03-01

Most studies using multivariate techniques for pollution source evaluation are conducted in free-flowing rivers with distinct point and nonpoint sources. This study expanded on previous research to a managed "canal" system discharging into the Indian River Lagoon, Florida, where water and land management is the single most important anthropogenic factor influencing water quality. Hydrometric and land use data of four drainage basins were uniquely integrated into the analysis of 25 yr of monthly water quality data collected at seven stations to determine the impact of water and land management on the spatial variability of water quality. Cluster analysis (CA) classified seven monitoring stations into four groups (CA groups). All water quality parameters identified by discriminant analysis showed distinct spatial patterns among the four CA groups. Two-step principal component analysis/factor analysis (PCA/FA) was conducted with (i) water quality data alone and (ii) water quality data in conjunction with rainfall, flow, and land use data. The results indicated that PCA/FA of water quality data alone was unable to identify factors associated with management activities. The addition of hydrometric and land use data into PCA/FA revealed close associations of nutrients and color with land management and storm-water retention in pasture and citrus lands; total suspended solids, turbidity, and NO + NO with flow and Lake Okeechobee releases; specific conductivity with supplemental irrigation supply; and dissolved O with wetland preservation. The practical implication emphasizes the importance of basin-specific land and water management for ongoing pollutant loading reduction and ecosystem restoration programs. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

An integrated GIS application system for soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

2014-11-01

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

NASA Astrophysics Data System (ADS)

Li, Lin; Yang, Yong; Wang, Hongbin; Dong, Jing; Zhao, Yujun; He, Jianbin; Fan, Honggang

Geographic information system (GIS) is a computer application system, which possesses the ability of manipulating spatial information and has been used in many fields related with the spatial information management. Many methods and models have been established for analyzing animal diseases distribution models and temporal-spatial transmission models. Great benefits have been gained from the application of GIS in animal disease epidemiology. GIS is now a very important tool in animal disease epidemiological research. Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. In this paper, we analyzed the cow anthrax spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow anthrax in this district in combination of spatial statistical analysis and GIS. The Cow anthrax is biogeochemical disease, and its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of anthrax cow for monitoring and the prevention and control of anthrax has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness. The GIS computer application software ArcGIS9.1 was used to overcome the lack of data of sampling sites.Using ArcGIS 9.1 and GEODA to analyze the cow anthrax spatial distribution of district A. we gained some conclusions about cow anthrax' density: (1) there is a spatial clustering model. (2) there is an intensely spatial autocorrelation. We established a prediction model to estimate the anthrax distribution based on the spatial characteristic of the density of cow anthrax. Comparing with the true distribution, the prediction model has a well coincidence and is feasible to the application. The method using a GIS tool facilitates can be implemented significantly in the cow anthrax monitoring and investigation, and the space statistics - related prediction model provides a fundamental use for other study on space-related animal diseases.

The Construct Validity of the Category Test: Is It a Measure of Reasoning or Intelligence?

ERIC Educational Resources Information Center

Johnstone, Brick; And Others

1997-01-01

The construct validity of the Category Test (W. C. Halstead, 1947) was studied for 308 adults with heterogeneous cognitive dysfunction. Factor analysis indicated that Category subtests load on three factors distinct from intelligence: (1) symbol recognition/counting; (2) spatial position reasoning; (3) and proportional reasoning. Clinical…

Quantifying the linear and nonlinear relations between the urban form fragmentation and the carbon emission distribution

NASA Astrophysics Data System (ADS)

Zuo, S.; Dai, S.; Ren, Y.; Yu, Z.

2017-12-01

Scientifically revealing the spatial heterogeneity and the relationship between the fragmentation of urban landscape and the direct carbon emissions are of great significance to land management and urban planning. In fact, the linear and nonlinear effects among the various factors resulted in the carbon emission spatial map. However, there is lack of the studies on the direct and indirect relations between the carbon emission and the city functional spatial form changes, which could not be reflected by the land use change. The linear strength and direction of the single factor could be calculated through the correlation and Geographically Weighted Regression (GWR) analysis, the nonlinear power of one factor and the interaction power of each two factors could be quantified by the Geodetector analysis. Therefore, we compared the landscape fragmentation metrics of the urban land cover and functional district patches to characterize the landscape form and then revealed the relations between the landscape fragmentation level and the direct the carbon emissions based on the three methods. The results showed that fragmentation decreased and the fragmented patches clustered at the coarser resolution. The direct CO2 emission density and the population density increased when the fragmentation level aggregated. The correlation analysis indicated the weak linear relation between them. The spatial variation of GWR output indicated the fragmentation indicator (MESH) had the positive influence on the carbon emission located in the relatively high emission region, and the negative effects regions accounted for the small part of the area. The Geodetector which explores the nonlinear relation identified the DIVISION and MESH as the most powerful direct factor for the land cover patches, NP and PD for the functional district patches, and the interactions between fragmentation indicator (MESH) and urban sprawl metrics (PUA and DIS) had the greatly increased explanation powers on the urban carbon emission. Overall, this study provides a framework to understand the relation between the urban landscape fragmentation and the carbon emission for the low carbon city construction planning in the other cities.

The Role of Spatial Analysis in Detecting the Consequence of the Factory Sites : Case Study of Assalaya Factory-Sudan

NASA Astrophysics Data System (ADS)

Khair, Amar Sharaf Eldin; Purwanto; RyaSunoko, Henna; Abdullah, Omer Adam

2018-02-01

Spatial analysis is considered as one of the most important science for identifying the most appropriate site for industrialization and also to alleviate the environmental ramifications caused by factories. This study aims at analyzing the Assalaya sugarcane factory site by the use of spatial analysis to determine whether it has ramification on the White Nile River. The methodology employed for this study is Global Position System (GPS) to identify the coordinate system of the study phenomena and other relative factors. The study will also make use Geographical Information System (GIS) to implement the spatial analysis. Satellite data (LandsatDem-Digital Elevation Model) will be considered for the study area and factory in identifying the consequences by analyzing the location of the factory through several features such as hydrological, contour line and geological analysis. Data analysis reveals that the factory site is inappropriate and according to observation on the ground it has consequences on the White Nile River. Based on the finding, the study recommended some suggestions to avoid the aftermath of any factory in general. We have to take advantage of this new technological method to aid in selecting most apt locations for industries that will create an ambient environment.

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia.

PubMed

Teurlai, Magali; Menkès, Christophe Eugène; Cavarero, Virgil; Degallier, Nicolas; Descloux, Elodie; Grangeon, Jean-Paul; Guillaumot, Laurent; Libourel, Thérèse; Lucio, Paulo Sergio; Mathieu-Daudé, Françoise; Mangeas, Morgan

2015-12-01

Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3 °C, mean incidence rates during epidemics could double. In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries.

Socio-economic and Climate Factors Associated with Dengue Fever Spatial Heterogeneity: A Worked Example in New Caledonia

PubMed Central

Teurlai, Magali; Menkès, Christophe Eugène; Cavarero, Virgil; Degallier, Nicolas; Descloux, Elodie; Grangeon, Jean-Paul; Guillaumot, Laurent; Libourel, Thérèse; Lucio, Paulo Sergio; Mathieu-Daudé, Françoise; Mangeas, Morgan

2015-01-01

Background/Objectives Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. Methods We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. Results The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3°C, mean incidence rates during epidemics could double. Conclusion In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries. PMID:26624008

Geostatistics: a common link between medical geography, mathematical geology, and medical geology

PubMed Central

Goovaerts, P.

2015-01-01

Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963

Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

PubMed

Goovaerts, P

2014-08-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

Spatial analysis of falls in an urban community of Hong Kong

PubMed Central

Lai, Poh C; Low, Chien T; Wong, Martin; Wong, Wing C; Chan, Ming H

2009-01-01

Background Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations. Results This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh) and Standard Deviational Ellipse (SDE) techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population. Conclusion The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies. PMID:19291326

Diffraction analysis of sidelobe characteristics of optical elements with ripple error

NASA Astrophysics Data System (ADS)

Zhao, Lei; Luo, Yupeng; Bai, Jian; Zhou, Xiangdong; Du, Juan; Liu, Qun; Luo, Yujie

2018-03-01

The ripple errors of the lens lead to optical damage in high energy laser system. The analysis of sidelobe on the focal plane, caused by ripple error, provides a reference to evaluate the error and the imaging quality. In this paper, we analyze the diffraction characteristics of sidelobe of optical elements with ripple errors. First, we analyze the characteristics of ripple error and build relationship between ripple error and sidelobe. The sidelobe results from the diffraction of ripple errors. The ripple error tends to be periodic due to fabrication method on the optical surface. The simulated experiments are carried out based on angular spectrum method by characterizing ripple error as rotationally symmetric periodic structures. The influence of two major parameter of ripple including spatial frequency and peak-to-valley value to sidelobe is discussed. The results indicate that spatial frequency and peak-to-valley value both impact sidelobe at the image plane. The peak-tovalley value is the major factor to affect the energy proportion of the sidelobe. The spatial frequency is the major factor to affect the distribution of the sidelobe at the image plane.

Do spatial patterns of urbanization and land consumption reflect different socioeconomic contexts in Europe?

PubMed

Salvati, Luca; Zambon, Ilaria; Chelli, Francesco Maria; Serra, Pere

2018-06-01

Land-use changes and urban sprawl have transformed European cities, with a direct impact on both metropolitan structures and socioeconomic functions. However, these processes tend to be relatively different across countries, being influenced by place-specific factors associated to socioeconomic, historical, political and cultural factors that influence decisions on the use of land. Considering 155 metropolitan areas in 6 European macro-regions, the present study investigates spatial patterns of land consumption profiling cities according to a large set of territorial variables, with the final objective to identify relevant socioeconomic dimensions characteristic of recent processes of urban growth. Investigating the socioeconomic background underlying land-use changes in metropolitan regions allows identification of place-specific factors improving the design of effective strategies containing land consumption in different European urban typologies. An exhaustive analysis of land-use changes at regional and local spatial scales contributes to find alternative policies for land-use efficiency and long-term environmental sustainability. Copyright © 2018 Elsevier B.V. All rights reserved.

Can the functional stability of forest ecosystems be evaluated from the spatial analysis of stands? A case study from the Bialowieza Primeval Forest (Poland)

Treesearch

Andrzej Bobiec

2000-01-01

Variability of external and internal factors entails specific spatial patterns and functional dynamics of communities. The study of the oak-lime-hornbeam (Quercus robur-Tilia cordata-Carpimus) forest in the Bialowieza Primeval Forest supports the concept of silvatic unit, determining the minimal structural area. To find out if the dynamics of a stand...

Net primary production and canopy nitrogen in a temperate forest landscape: an analysis using imaging spectroscopy, modeling and field data

Treesearch

Scott V. Ollinger; Marie-Louise Smith

2005-01-01

Understanding spatial patterns of net primary production (NPP) is central to the study of terrestrial ecosystems, but efforts are frequently hampered by a lack of spatial information regarding factors such as nitrogen availability and site history. Here, we examined the degree to which canopy nitrogen can serve as an indicator of patterns of NPP at the Bartlett...

Spatial data analysis and the use of maps in scientific health articles.

PubMed

Nucci, Luciana Bertoldi; Souccar, Patrick Theodore; Castilho, Silvia Diez

2016-07-01

Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. To verify the spread and use of those processes in national and international scientific papers. An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia.

PubMed

Pérez-Flórez, Mauricio; Ocampo, Clara Beatriz; Valderrama-Ardila, Carlos; Alexander, Neal

2016-06-27

The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America.

The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis.

PubMed

Cheng, Gong; Li, Dan; Zhuang, Dafang; Wang, Yong

2016-12-01

We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control. GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter. (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011. The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children.

PubMed

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S K; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P; Puumalainen, Taneli; Lupisan, Socorro P; Ruutu, Petri; Ladesma, Erma; Williams, Gail M; Riley, Ian; Simões, Eric A F

2014-03-04

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from -14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs.

A multi-level analysis of the relationship between environmental factors and questing Ixodes ricinus dynamics in Belgium

PubMed Central

2012-01-01

Background Ticks are the most important pathogen vectors in Europe. They are known to be influenced by environmental factors, but these links are usually studied at specific temporal or spatial scales. Focusing on Ixodes ricinus in Belgium, we attempt to bridge the gap between current “single-sided” studies that focus on temporal or spatial variation only. Here, spatial and temporal patterns of ticks are modelled together. Methods A multi-level analysis of the Ixodes ricinus patterns in Belgium was performed. Joint effects of weather, habitat quality and hunting on field sampled tick abundance were examined at two levels, namely, sampling level, which is associated with temporal dynamics, and site level, which is related to spatial dynamics. Independent variables were collected from standard weather station records, game management data and remote sensing-based land cover data. Results At sampling level, only a marginally significant effect of daily relative humidity and temperature on the abundance of questing nymphs was identified. Average wind speed of seven days prior to the sampling day was found important to both questing nymphs and adults. At site level, a group of landscape-level forest fragmentation indices were highlighted for both questing nymph and adult abundance, including the nearest-neighbour distance, the shape and the aggregation level of forest patches. No cross-level effects or spatial autocorrelation were found. Conclusions Nymphal and adult ticks responded differently to environmental variables at different spatial and temporal scales. Our results can advise spatio-temporal extents of environment data collection for continuing empirical investigations and potential parameters for biological tick models. PMID:22830528

Principal component analysis to assess the composition and fate of impurities in a large river-embedded reservoir: Qingcaosha Reservoir.

PubMed

Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun

2013-08-01

Qingcaosha Reservoir (QR) is the largest river-embedded reservoir in east China, which receives its source water from the Yangtze River (YR). The temporal and spatial variations in dissolved organic matter (DOM), chromophoric DOM (CDOM), nitrogen, phosphorus and phytoplankton biomass were investigated from June to September in 2012 and were integrated by principal component analysis (PCA). Three PCA factors were identified: (1) phytoplankton related factor 1, (2) total DOM related factor 2, and (3) eutrophication related factor 3. Factor 1 was a lake-type parameter which correlated with chlorophyll-a and protein-like CDOM (r = 0.793 and r = 0.831, respectively). Factor 2 was a river-type parameter which correlated with total DOC and humic-like CDOM (r = 0.668 and r = 0.726, respectively). Factor 3 correlated with total nitrogen and phosphorus (r = 0.864 and r = 0.621, respectively). The low flow speed, self-sedimentation and nutrient accumulation in QR resulted in increases in PCA factor 1 scores (phytoplankton biomass and derived CDOM) in the spatial scale, indicating a change of river-type water (YR) to lake-type water (QR). In summer, the water temperature variation induced a growth-bloom-decay process of phytoplankton combined with the increase of PCA factor 2 (humic-like CDOM) in the QR, which was absent in the YR.

Spatiotemporal Analysis of Malaria in Urban Ahmedabad (Gujarat), India: Identification of Hot Spots and Risk Factors for Targeted Intervention

PubMed Central

Parizo, Justin; Sturrock, Hugh J. W.; Dhiman, Ramesh C.; Greenhouse, Bryan

2016-01-01

The world population, especially in developing countries, has experienced a rapid progression of urbanization over the last half century. Urbanization has been accompanied by a rise in cases of urban infectious diseases, such as malaria. The complexity and heterogeneity of the urban environment has made study of specific urban centers vital for urban malaria control programs, whereas more generalizable risk factor identification also remains essential. Ahmedabad city, India, is a large urban center located in the state of Gujarat, which has experienced a significant Plasmodium vivax and Plasmodium falciparum disease burden. Therefore, a targeted analysis of malaria in Ahmedabad city was undertaken to identify spatiotemporal patterns of malaria, risk factors, and methods of predicting future malaria cases. Malaria incidence in Ahmedabad city was found to be spatially heterogeneous, but temporally stable, with high spatial correlation between species. Because of this stability, a prediction method utilizing historic cases from prior years and seasons was used successfully to predict which areas of Ahmedabad city would experience the highest malaria burden and could be used to prospectively target interventions. Finally, spatial analysis showed that normalized difference vegetation index, proximity to water sources, and location within Ahmedabad city relative to the dense urban core were the best predictors of malaria incidence. Because of the heterogeneity of urban environments and urban malaria itself, the study of specific large urban centers is vital to assist in allocating resources and informing future urban planning. PMID:27382081

Multidimensional poverty measure and analysis: a case study from Hechi City, China.

PubMed

Wang, Yanhui; Wang, Baixue

2016-01-01

Aiming at the anti-poverty outline of China and the human-environment sustainable development, we propose a multidimensional poverty measure and analysis methodology for measuring the poverty-stricken counties and their contributing factors. We build a set of multidimensional poverty indicators with Chinese characteristics, integrating A-F double cutoffs, dimensional aggregation and decomposition approach, and GIS spatial analysis to evaluate the poor's multidimensional poverty characteristics under different geographic and socioeconomic conditions. The case study from 11 counties of Hechi City shows that, firstly, each county existed at least four respects of poverty, and overall the poverty level showed the spatial pattern of surrounding higher versus middle lower. Secondly, three main poverty contributing factors were unsafe housing, family health and adults' illiteracy, while the secondary factors include fuel type and children enrollment rate, etc., generally demonstrating strong autocorrelation; in terms of poverty degree, the western of the research area shows a significant aggregation effect, whereas the central and the eastern represent significant spatial heterogeneous distribution. Thirdly, under three kinds of socioeconomic classifications, the intra-classification diversities of H, A, and MPI are greater than their inter-classification ones, while each of the three indexes has a positive correlation with both the rocky desertification degree and topographic fragmentation degree, respectively. This study could help policymakers better understand the local poverty by identifying the poor, locating them and describing their characteristics, so as to take differentiated poverty alleviation measures according to specific conditions of each county.

Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes.

PubMed

Kounina, Anna; Margni, Manuele; Shaked, Shanna; Bulle, Cécile; Jolliet, Olivier

2014-08-01

This paper develops continent-specific factors for the USEtox model and analyses the accuracy of different model architectures, spatial scales and archetypes in evaluating toxic impacts, with a focus on freshwater pathways. Inter-continental variation is analysed by comparing chemical fate and intake fractions between sub-continental zones of two life cycle impact assessment models: (1) the nested USEtox model parameterized with sub-continental zones and (2) the spatially differentiated IMPACTWorld model with 17 interconnected sub-continental regions. Substance residence time in water varies by up to two orders of magnitude among the 17 zones assessed with IMPACTWorld and USEtox, and intake fraction varies by up to three orders of magnitude. Despite this variation, the nested USEtox model succeeds in mimicking the results of the spatially differentiated model, with the exception of very persistent volatile pollutants that can be transported to polar regions. Intra-continental variation is analysed by comparing fate and intake fractions modelled with the a-spatial (one box) IMPACT Europe continental model vs. the spatially differentiated version of the same model. Results show that the one box model might overestimate chemical fate and characterisation factors for freshwater eco-toxicity of persistent pollutants by up to three orders of magnitude for point source emissions. Subdividing Europe into three archetypes, based on freshwater residence time (how long it takes water to reach the sea), improves the prediction of fate and intake fractions for point source emissions, bringing them within a factor five compared to the spatial model. We demonstrated that a sub-continental nested model such as USEtox, with continent-specific parameterization complemented with freshwater archetypes, can thus represent inter- and intra-continental spatial variations, whilst minimizing model complexity. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

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

Clair, Geremy; Piehowski, Paul D.; Nicola, Teodora

Global proteomics approaches allow characterization of whole tissue lysates to an impressive depth. However, it is now increasingly recognized that to better understand the complexity of multicellular organisms, global protein profiling of specific spatially defined regions/substructures of tissues (i.e. spatially-resolved proteomics) is essential. Laser capture microdissection (LCM) enables microscopic isolation of defined regions of tissues preserving crucial spatial information. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, and that impact measurement robustness, quantification, and throughput. Here, we coupled LCM with a fully automated sample preparation workflow thatmore » with a single manual step allows: protein extraction, tryptic digestion, peptide cleanup and LC-MS/MS analysis of proteomes from microdissected tissues. Benchmarking against the current state of the art in ultrasensitive global proteomic analysis, our approach demonstrated significant improvements in quantification and throughput. Using our LCM-SNaPP proteomics approach, we characterized to a depth of more than 3,400 proteins, the ontogeny of protein changes during normal lung development in laser capture microdissected alveolar tissue containing ~4,000 cells per sample. Importantly, the data revealed quantitative changes for 350 low abundance transcription factors and signaling molecules, confirming earlier transcript-level observations and defining seven modules of coordinated transcription factor/signaling molecule expression patterns, suggesting that a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes. Our LCM-proteomics approach facilitates efficient, spatially-resolved, ultrasensitive global proteomics analyses in high-throughput that will be enabling for several clinical and biological applications.« less

Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data

USGS Publications Warehouse

Mwakanyamale, Kisa; Slater, Lee; Day-Lewis, Frederick D.; Elwaseif, Mehrez; Johnson, Carole D.

2012-01-01

Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwater-surface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area.

Simulation of multispectral multisource for device of consumer and medicine products analysis

NASA Astrophysics Data System (ADS)

Korolev, Timofey K.; Peretyagin, Vladimir S.

2017-06-01

One of the results of intensive development of led technology was the creation of a multi-component, managed devices and illumination/irradiation used in various fields of production (e.g., food industry analysis, food quality). The use of LEDs has become possible due to their structure determining spatial, energy, electrical, thermal and other characteristics. However, the development of the devices for illumination/irradiation require closer attention in the case if you want to provide precise illumination to the area of analysis, located at a specified distance from the radiation source. The present work is devoted to the development and modelling of a specialized source of radiation intended for solving problems of analysis of food products, medicines and water for suitability in drinking. In this work, we provided a mathematical model of spatial and spectral distribution of irridation from the source of infrared radiation ring structure. When you create this kind of source, you address factors such spectral component, the power settings, the spatial and energy components of the diodes.

Association of nitrogen compounds in drinking water with incidence of esophageal squamous cell carcinoma in Shexian, China.

PubMed

Zhang, Nan; Yu, Cao; Wen, Denggui; Chen, Jun; Ling, Yiwei; Terajima, Kenshi; Akazawa, Kohei; Shan, Baoen; Wang, Shijie

2012-01-01

The incidence of esophageal squamous cell carcinoma (ESCC), which is the eighth most common malignancy worldwide, is highest in China. The purpose of this study was to investigate the association between nitrogen compounds in drinking water with the incidence of ESCC by geographical spatial analysis. The incidence of ESCC is high in Shexian county, China, and environmental factors, particularly nitrogen-contaminated drinking water, are the main suspected risk factors. This study focuses on three nitrogen compounds in drinking water, namely, nitrates, nitrites, and ammonia, all of which are derived mainly from domestic garbage and agricultural fertilizer. The study surveyed 48 villages in the Shexian area with a total population of 54,716 (661 adults with ESCC and 54,055 non-cancer subjects). Hot-spot analysis was used to identify spatial clusters with a high incidence of ESCC and a high concentration of nitrogen compounds. Logistic regression analysis was used to detect risk factors for ESCC incidence. Most areas with high concentrations of nitrate nitrogen in drinking water had a high incidence of ESCC. Correlation analysis revealed a significant positive relationship between nitrate concentration and ESCC (P = 0.01). Logistic regression analysis also confirmed that nitrate nitrogen has a significantly higher odds ratio. The results indicate that nitrate nitrogen is associated with ESCC incidence in Shexian county. In conclusion, high concentrations of nitrate nitrogen in drinking water may be a significant risk factor for the incidence of ESCC.

Relationship Between Landcover Pattern and Surface Net Radiation in AN Coastal City

NASA Astrophysics Data System (ADS)

Zhao, X.; Liu, L.; Liu, X.; Zhao, Y.

2016-06-01

Taking Xiamen city as the study area this research first retrieved surface net radiation using meteorological data and Landsat 5 TM images of the four seasons in the year 2009. Meanwhile the 65 different landscape metrics of each analysis unit were acquired using landscape analysis method. Then the most effective landscape metrics affecting surface net radiation were determined by correlation analysis, partial correlation analysis, stepwise regression method, etc. At both class and landscape levels, this paper comprehensively analyzed the temporal and spatial variations of the surface net radiation as well as the effects of land cover pattern on it in Xiamen from a multi-seasonal perspective. The results showed that the spatial composition of land cover pattern shows significant influence on surface net radiation while the spatial allocation of land cover pattern does not. The proportions of bare land and forest land are effective and important factors which affect the changes of surface net radiation all the year round. Moreover, the proportion of forest land is more capable for explaining surface net radiation than the proportion of bare land. So the proportion of forest land is the most important and continuously effective factor which affects and explains the cross-seasonal differences of surface net radiation. This study is helpful in exploring the formation and evolution mechanism of urban heat island. It also gave theoretical hints and realistic guidance for urban planning and sustainable development.

Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

PubMed Central

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

Analysis of the spatial-temporal variation characteristics of vegetative drought and its relationship with meteorological factors in China from 1982 to 2010.

PubMed

Shen, Qiu; Liang, Liang; Luo, Xiang; Li, Yanjun; Zhang, Lianpeng

2017-08-25

Drought is a complex natural phenomenon that can cause reduced water supplies and can consequently have substantial effects on agriculture and socioeconomic activities. The objective of this study was to gain a better understanding of the spatial-temporal variation characteristics of vegetative drought and its relationship with meteorological factors in China. The Vegetation Condition Index (VCI) dataset calculated from NOAA/AVHRR images from 1982 to 2010 was used to analyse the spatial-temporal variation characteristics of vegetative drought in China. This study also examined the trends in meteorological factors and their influences on drought using monitoring data collected from 686 national ground meteorological stations. The results showed that the VCI appeared to slowly rise in China from 1982 to 2010. From 1982 to 1999, the VCI rose slowly. Then, around 2000, the VCI exhibited a severe fluctuation before it entered into a relatively stable stage. Drought frequencies in China were higher, showing a spatial distribution feature of "higher in the north and lower in the south". Based on the different levels of drought, the frequencies of mild and moderate drought in four geographical areas were higher, and the frequency of severe drought was higher only in ecologically vulnerable areas, such as the Tarim Basin and the Qaidam Basin. Drought was mainly influenced by meteorological factors, which differed regionally. In the northern region, the main influential factor was sunshine duration, while the other factors showed minimal effects. In the southern region and Tibetan Plateau, the main influential factors were sunshine duration and temperature. In the northwestern region, the main influential factors were wind velocity and station atmospheric pressure.

Using structured additive regression models to estimate risk factors of malaria: analysis of 2010 Malawi malaria indicator survey data.

PubMed

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.

Spatial and Temporal Dynamics of Pacific Oyster Hemolymph Microbiota across Multiple Scales

PubMed Central

Lokmer, Ana; Goedknegt, M. Anouk; Thieltges, David W.; Fiorentino, Dario; Kuenzel, Sven; Baines, John F.; Wegner, K. Mathias

2016-01-01

Unveiling the factors and processes that shape the dynamics of host associated microbial communities (microbiota) under natural conditions is an important part of understanding and predicting an organism's response to a changing environment. The microbiota is shaped by host (i.e., genetic) factors as well as by the biotic and abiotic environment. Studying natural variation of microbial community composition in multiple host genetic backgrounds across spatial as well as temporal scales represents a means to untangle this complex interplay. Here, we combined a spatially-stratified with a longitudinal sampling scheme within differentiated host genetic backgrounds by reciprocally transplanting Pacific oysters between two sites in the Wadden Sea (Sylt and Texel). To further differentiate contingent site from host genetic effects, we repeatedly sampled the same individuals over a summer season to examine structure, diversity and dynamics of individual hemolymph microbiota following experimental removal of resident microbiota by antibiotic treatment. While a large proportion of microbiome variation could be attributed to immediate environmental conditions, we observed persistent effects of antibiotic treatment and translocation suggesting that hemolymph microbial community dynamics is subject to within-microbiome interactions and host population specific factors. In addition, the analysis of spatial variation revealed that the within-site microenvironmental heterogeneity resulted in high small-scale variability, as opposed to large-scale (between-site) stability. Similarly, considerable within-individual temporal variability was in contrast with the overall temporal stability at the site level. Overall, our longitudinal, spatially-stratified sampling design revealed that variation in hemolymph microbiota is strongly influenced by site and immediate environmental conditions, whereas internal microbiome dynamics and oyster-related factors add to their long-term stability. The combination of small and large scale resolution of spatial and temporal observations therefore represents a crucial but underused tool to study host-associated microbiome dynamics. PMID:27630625

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

NASA Astrophysics Data System (ADS)

Gaitan, S.; ten Veldhuis, J. A. E.

2015-06-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes.

PubMed

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-07-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

PubMed Central

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-01-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran’s eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances. PMID:26257867

Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England.

PubMed

Wang, Chao; Quddus, Mohammed A; Ison, Stephen G

2009-07-01

Traffic congestion and road accidents are two external costs of transport and the reduction of their impacts is often one of the primary objectives for transport policy makers. The relationship between traffic congestion and road accidents however is not apparent and less studied. It is speculated that there may be an inverse relationship between traffic congestion and road accidents, and as such this poses a potential dilemma for transport policy makers. This study aims to explore the impact of traffic congestion on the frequency of road accidents using a spatial analysis approach, while controlling for other relevant factors that may affect road accidents. The M25 London orbital motorway, divided into 70 segments, was chosen to conduct this study and relevant data on road accidents, traffic and road characteristics were collected. A robust technique has been developed to map M25 accidents onto its segments. Since existing studies have often used a proxy to measure the level of congestion, this study has employed a precise congestion measurement. A series of Poisson based non-spatial (such as Poisson-lognormal and Poisson-gamma) and spatial (Poisson-lognormal with conditional autoregressive priors) models have been used to account for the effects of both heterogeneity and spatial correlation. The results suggest that traffic congestion has little or no impact on the frequency of road accidents on the M25 motorway. All other relevant factors have provided results consistent with existing studies.

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

PubMed

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Large-scale changes in network interactions as a physiological signature of spatial neglect

PubMed Central

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

2014-01-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. PMID:25367028

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

PubMed

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

2017-08-14

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

Stand Structure and Substrate Diversity as Two Major Drivers for Bryophyte Distribution in a Temperate Montane Ecosystem

PubMed Central

Chen, Yun; Niu, Shuai; Li, Peikun; Jia, Hongru; Wang, Hailiang; Ye, Yongzhong; Yuan, Zhiliang

2017-01-01

Elucidating the major drivers of bryophyte distribution is the first step to protecting bryophyte diversity. Topography, forest, substrates (ground, tree trunks, roots, rocks, and rotten wood), and spatial factor, which factors are the major drivers of bryophyte distribution? In this study, 53 plots were set in 400 m2 along the elevation gradient in Xiaoqinling, China. All bryophytes in the plots were collected and identified. Regression analysis was used to examine the relationship between bryophyte and substrate diversity. We compared the patterns of overall bryophyte diversity and diversity of bryophytes found on the ground, tree, and rock along elevational gradients. Canonical correspondence analysis was applied to relate species composition to selected environmental variables. The importance of topography, forest, substrates, and spatial factors was determined by variance partitioning. A total of 1378 bryophyte specimens were collected, and 240 species were identified. Bryophyte diversity was closely related to substrate diversity. The overall bryophyte diversity significantly increased with elevation; however, the response varied among ground, tree, and rock bryophytes. Tree diversity and herb layer were considered important environmental factors in determining bryophyte distribution. Species abundance was best explained by stand structure (17%), and species diversity was best explained by stand structure (35%) and substrate (40%). Results directly indicated that substrate diversity can improve bryophyte species diversity. The effects of micro-habitat formed by stand structure and substrate diversity were higher than those of spatial processes and topography factors on bryophyte distribution. This study proved that the determinant factors influencing bryophyte diversity reflect the trends in recent forest management, providing a real opportunity to improve forest biodiversity conservation. PMID:28603535

Stand Structure and Substrate Diversity as Two Major Drivers for Bryophyte Distribution in a Temperate Montane Ecosystem.

PubMed

Chen, Yun; Niu, Shuai; Li, Peikun; Jia, Hongru; Wang, Hailiang; Ye, Yongzhong; Yuan, Zhiliang

2017-01-01

Elucidating the major drivers of bryophyte distribution is the first step to protecting bryophyte diversity. Topography, forest, substrates (ground, tree trunks, roots, rocks, and rotten wood), and spatial factor, which factors are the major drivers of bryophyte distribution? In this study, 53 plots were set in 400 m 2 along the elevation gradient in Xiaoqinling, China. All bryophytes in the plots were collected and identified. Regression analysis was used to examine the relationship between bryophyte and substrate diversity. We compared the patterns of overall bryophyte diversity and diversity of bryophytes found on the ground, tree, and rock along elevational gradients. Canonical correspondence analysis was applied to relate species composition to selected environmental variables. The importance of topography, forest, substrates, and spatial factors was determined by variance partitioning. A total of 1378 bryophyte specimens were collected, and 240 species were identified. Bryophyte diversity was closely related to substrate diversity. The overall bryophyte diversity significantly increased with elevation; however, the response varied among ground, tree, and rock bryophytes. Tree diversity and herb layer were considered important environmental factors in determining bryophyte distribution. Species abundance was best explained by stand structure (17%), and species diversity was best explained by stand structure (35%) and substrate (40%). Results directly indicated that substrate diversity can improve bryophyte species diversity. The effects of micro-habitat formed by stand structure and substrate diversity were higher than those of spatial processes and topography factors on bryophyte distribution. This study proved that the determinant factors influencing bryophyte diversity reflect the trends in recent forest management, providing a real opportunity to improve forest biodiversity conservation.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain.

PubMed

Dominkovics, Pau; Granell, Carlos; Pérez-Navarro, Antoni; Casals, Martí; Orcau, Angels; Caylà, Joan A

2011-11-29

Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti

PubMed Central

2013-01-01

Background Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Methods Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Results Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”. Conclusions Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations. PMID:23587358

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

PubMed

Curtis, Andrew; Blackburn, Jason K; Widmer, Jocelyn M; Morris, J Glenn

2013-04-15

Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

PubMed Central

2011-01-01

Background Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Methods Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. Results The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. Conclusions In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios. PMID:22126392

Factors Controlling Vegetation Fires in Protected and Non-Protected Areas of Myanmar

PubMed Central

Biswas, Sumalika; Vadrevu, Krishna Prasad; Lwin, Zin Mar; Lasko, Kristofer; Justice, Christopher O.

2015-01-01

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar. PMID:25909632

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization.

PubMed

Gallina, Alessio; Garland, S Jayne; Wakeling, James M

2018-05-22

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF. Copyright © 2018 Elsevier Ltd. All rights reserved.

Factors controlling vegetation fires in protected and non-protected areas of myanmar.

PubMed

Biswas, Sumalika; Vadrevu, Krishna Prasad; Lwin, Zin Mar; Lasko, Kristofer; Justice, Christopher O

2015-01-01

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

Regional gradient analysis and spatial pattern of woody plant communities in Oregon forests.

Treesearch

J.L. Ohmann; T.A. Spies

1998-01-01

Knowledge of regional-scale patterns of ecological community structure, and of factors that control them, is largely conceptual. Regional- and local-scale factors associated with regional variation in community composition have not been quantified. We analyzed data on woody plant species abundance from 2443 field plots across natural and seminatural forests and...

Spatial organization of bacterial chromosomes

PubMed Central

Wang, Xindan; Rudner, David Z.

2014-01-01

Bacterial chromosomes are organized in stereotypical patterns that are faithfully and robustly regenerated in daughter cells. Two distinct spatial patterns were described almost a decade ago in our most tractable model organisms. In recent years, analysis of chromosome organization in a larger and more diverse set of bacteria and a deeper characterization of chromosome dynamics in the original model systems have provided a broader and more complete picture of both chromosome organization and the activities that generate the observed spatial patterns. Here, we summarize these different patterns highlighting similarities and differences and discuss the protein factors that help establish and maintain them. PMID:25460798

Effects of spatial disturbance on common loon nest site selection and territory success

USGS Publications Warehouse

McCarthy, Kyle P.; DeStefano, Stephen

2011-01-01

The common loon (Gavia immer) breeds during the summer on northern lakes and water bodies that are also often desirable areas for aquatic recreation and human habitation. In northern New England, we assessed how the spatial nature of disturbance affects common loon nest site selection and territory success. We found through classification and regression analysis that distance to and density of disturbance factors can be used to classify observed nest site locations versus random points, suggesting that these factors affect loon nest site selection (model 1: Correct classification = 75%, null = 50%, K = 0.507, P < 0.001; model 2: Correct classification = 78%, null = 50%, K = 0.551, P < 0.001). However, in an exploratory analysis, we were unable to show a relation between spatial disturbance variables and breeding success (P = 0.595, R2 = 0.436), possibly because breeding success was so low during the breeding seasons of 2007–2008. We suggest that by selecting nest site locations that avoid disturbance factors, loons thereby limit the effect that disturbance will have on their breeding success. Still, disturbance may force loons to use sub-optimal nesting habitat, limiting the available number of territories, and overall productivity. We advise that management efforts focus on limiting disturbance factors to allow breeding pairs access to the best nesting territories, relieving disturbance pressures that may force sub-optimal nest placement.

Aspect-related Vegetation Differences Amplify Soil Moisture Variability in Semiarid Landscapes

NASA Astrophysics Data System (ADS)

Yetemen, O.; Srivastava, A.; Kumari, N.; Saco, P. M.

2017-12-01

Soil moisture variability (SMV) in semiarid landscapes is affected by vegetation, soil texture, climate, aspect, and topography. The heterogeneity in vegetation cover that results from the effects of microclimate, terrain attributes (slope gradient, aspect, drainage area etc.), soil properties, and spatial variability in precipitation have been reported to act as the dominant factors modulating SMV in semiarid ecosystems. However, the role of hillslope aspect in SMV, though reported in many field studies, has not received the same degree of attention probably due to the lack of extensive large datasets. Numerical simulations can then be used to elucidate the contribution of aspect-driven vegetation patterns to this variability. In this work, we perform a sensitivity analysis to study on variables driving SMV using the CHILD landscape evolution model equipped with a spatially-distributed solar-radiation component that couples vegetation dynamics and surface hydrology. To explore how aspect-driven vegetation heterogeneity contributes to the SMV, CHILD was run using a range of parameters selected to reflect different scenarios (from uniform to heterogeneous vegetation cover). Throughout the simulations, the spatial distribution of soil moisture and vegetation cover are computed to estimate the corresponding coefficients of variation. Under the uniform spatial precipitation forcing and uniform soil properties, the factors affecting the spatial distribution of solar insolation are found to play a key role in the SMV through the emergence of aspect-driven vegetation patterns. Hence, factors such as catchment gradient, aspect, and latitude, define water stress and vegetation growth, and in turn affect the available soil moisture content. Interestingly, changes in soil properties (porosity, root depth, and pore-size distribution) over the domain are not as effective as the other factors. These findings show that the factors associated to aspect-related vegetation differences amplify the soil moisture variability of semi-arid landscapes.

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

PubMed

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

2017-08-15

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

[Spatial heterogeneity of soil salinization and its influencing factors in the typical region of the Mu Us Desert-Loess Plateau transitional zone, Northwest China].

PubMed

Zhao, Xuan; Hao, Qi Li; Sun, Ying Ying

2017-06-18

Studies on the spatial heterogeneity of saline soil in the Mu Us Desert-Loess Plateau transition zone are meaningful for understanding the mechanisms of land desertification. Taking the Mu Us Desert-Loess Plateau transition zone as the study subject, its spatial heterogeneity of pH, electrical conductivity (EC) and total salt content were analyzed by using on-site sampling followed with indoor analysis, classical statistical and geostatistical analysis. The results indicated that: 1) The average values of pH, EC and total salt content were 8.44, 5.13 mS·cm -1 and 21.66 g·kg -1 , respectively, and the coefficient of variation ranged from 6.9% to 73.3%. The pH was weakly variable, while EC and total salt content were moderately variable. 2) Results of semivariogram analysis showed that the most fitting model for spatial variability of all three indexes was spherical model. The C 0 /(C 0 +C) ratios of three indexes ranged from 8.6% to 14.3%, which suggested the spatial variability of all indexes had a strong spatial autocorrelation, and the structural factors played a more important role. The variation range decreased in order of pH

Geographic variation in patterns of nestedness among local stream fish assemblages in Virginia

USGS Publications Warehouse

Cook, R.R.; Angermeier, P.L.; Finn, D.S.; Poff, N.L.; Krueger, K.L.

2004-01-01

Nestedness of faunal assemblages is a multiscale phenomenon, potentially influenced by a variety of factors. Prior small-scale studies have found freshwater fish species assemblages to be nested along stream courses as a result of either selective colonization or extinction. However, within-stream gradients in temperature and other factors are correlated with the distributions of many fish species and may also contribute to nestedness. At a regional level, strongly nested patterns would require a consistent set of structuring mechanisms across streams, and correlation among species' tolerances of the environmental factors that influence distribution. Thus, nestedness should be negatively associated with the spatial extent of the region analyzed and positively associated with elevational gradients (a correlate of temperature and other environmental factors). We examined these relationships for the freshwater fishes of Virginia. Regions were defined within a spatial hierarchy and included whole river drainages, portions of drainages within physiographic provinces, and smaller subdrainages. In most cases, nestedness was significantly stronger in regions of smaller spatial extent and in regions characterized by greater topographic relief. Analysis of hydrologic variability and patterns of faunal turnover provided no evidence that interannual colonization/extinction dynamics contributed to elevational differences in nestedness. These results suggest that, at regional scales, nestedness is influenced by interactions between biotic and abiotic factors, and that the strongest nestedness is likely to occur where a small number of organizational processes predominate, i.e., over small spatial extents and regions exhibiting strong environmental gradients. ?? Springer-Verlag 2004.

Hedonic valuation of the spatial competition for urban circumstance utilities: case Wuhan, China

NASA Astrophysics Data System (ADS)

Zheng, Bin; Liu, Yaolin; Huang, Lina

2008-10-01

It has generally accepted Alonso's [1] theory about the allocation of different land uses of commerce, resident and industry in urban area. A bunch of researches have provided their aspects of the theme of the relationships between urban circumstances and urban land uses in either the influence of one or several designate circumstance factors on different land uses, or the comprehensive analysis of the influence of all kinds of circumstance on one selected land usage (e.g. residential use). There is still not a wholly analysis about the influence of all kinds of spatial characteristics, available for the location selection of different land uses. That's why this research selects to engage in a study on the difference among "consumer preferences" to the location amenities in the city. Here we regard the behavior as "spatial competition of the locations". Hedonic regression model (HRM) analysis is employed as the basic framework of the research. Tabular comparison of HRM parameters performed with principal components analysis (PCA) and Geographic Information Science (GIS) provides all necessary numerical investigation and spatial analysis until to the finally results. The research can be helpful for putting forward to a further integrated investigation on the relationship between urban circumstance and real land use values.

Archaeal ammonia oxidizers respond to soil factors at smaller spatial scales than the overall archaeal community does in a high Arctic polar oasis.

PubMed

Banerjee, Samiran; Kennedy, Nabla; Richardson, Alan E; Egger, Keith N; Siciliano, Steven D

2016-06-01

Archaea are ubiquitous and highly abundant in Arctic soils. Because of their oligotrophic nature, archaea play an important role in biogeochemical processes in nutrient-limited Arctic soils. With the existing knowledge of high archaeal abundance and functional potential in Arctic soils, this study employed terminal restriction fragment length polymorphism (t-RFLP) profiling and geostatistical analysis to explore spatial dependency and edaphic determinants of the overall archaeal (ARC) and ammonia-oxidizing archaeal (AOA) communities in a high Arctic polar oasis soil. ARC communities were spatially dependent at the 2-5 m scale (P < 0.05), whereas AOA communities were dependent at the ∼1 m scale (P < 0.0001). Soil moisture, pH, and total carbon content were key edaphic factors driving both the ARC and AOA community structure. However, AOA evenness had simultaneous correlations with dissolved organic nitrogen and mineral nitrogen, indicating a possible niche differentiation for AOA in which dry mineral and wet organic soil microsites support different AOA genotypes. Richness, evenness, and diversity indices of both ARC and AOA communities showed high spatial dependency along the landscape and resembled scaling of edaphic factors. The spatial link between archaeal community structure and soil resources found in this study has implications for predictive understanding of archaea-driven processes in polar oases.

Spatial variation of pneumonia hospitalization risk in Twin Cities metro area, Minnesota.

PubMed

Iroh Tam, P Y; Krzyzanowski, B; Oakes, J M; Kne, L; Manson, S

2017-11-01

Fine resolution spatial variability in pneumonia hospitalization may identify correlates with socioeconomic, demographic and environmental factors. We performed a retrospective study within the Fairview Health System network of Minnesota. Patients 2 months of age and older hospitalized with pneumonia between 2011 and 2015 were geocoded to their census block group, and pneumonia hospitalization risk was analyzed in relation to socioeconomic, demographic and environmental factors. Spatial analyses were performed using Esri's ArcGIS software, and multivariate Poisson regression was used. Hospital encounters of 17 840 patients were included in the analysis. Multivariate Poisson regression identified several significant associations, including a 40% increased risk of pneumonia hospitalization among census block groups with large, compared with small, populations of ⩾65 years, a 56% increased risk among census block groups in the bottom (first) quartile of median household income compared to the top (fourth) quartile, a 44% higher risk in the fourth quartile of average nitrogen dioxide emissions compared with the first quartile, and a 47% higher risk in the fourth quartile of average annual solar insolation compared to the first quartile. After adjusting for income, moving from the first to the second quartile of the race/ethnic diversity index resulted in a 21% significantly increased risk of pneumonia hospitalization. In conclusion, the risk of pneumonia hospitalization at the census-block level is associated with age, income, race/ethnic diversity index, air quality, and solar insolation, and varies by region-specific factors. Identifying correlates using fine spatial analysis provides opportunities for targeted prevention and control.

Challenging Hydrological Panaceas: Water poverty governance accounting for spatial scale in the Niger River Basin

NASA Astrophysics Data System (ADS)

Ward, John; Kaczan, David

2014-11-01

Water poverty in the Niger River Basin is a function of physical constraints affecting access and supply, and institutional arrangements affecting the ability to utilise the water resource. This distinction reflects the complexity of water poverty and points to the need to look beyond technical and financial means alone to reduce its prevalence and severity. Policy decisions affecting water resources are generally made at a state or national level. Hydrological and socio-economic evaluations at these levels, or at the basin level, cannot be presumed to be concordant with the differentiation of poverty or livelihood vulnerability at more local levels. We focus on three objectives: first, the initial mapping of observed poverty, using two health metrics and a household assets metric; second, the estimation of factors which potentially influence the observed poverty patterns; and third, a consideration of spatial non-stationarity, which identifies spatial correlates of poverty in the places where their effects appear most severe. We quantify the extent to which different levels of analysis influence these results. Comparative analysis of correlates of poverty at basin, national and local levels shows limited congruence. Variation in water quantity, and the presence of irrigation and dams had either limited or no significant correlation with observed variation in poverty measures across levels. Education and access to improved water quality were the only variables consistently significant and spatially stable across the entire basin. At all levels, education is the most consistent non-water correlate of poverty while access to protected water sources is the strongest water related correlate. The analysis indicates that landscape and scale matter for understanding water-poverty linkages and for devising policy concerned with alleviating water poverty. Interactions between environmental, social and institutional factors are complex and consequently a comprehensive understanding of poverty and its causes requires analysis at multiple spatial resolutions.

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

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

Keser, Saniye; Duzgun, Sebnem; Department of Geodetic and Geographic Information Technologies, Middle East Technical University, 06800 Ankara

Highlights: Black-Right-Pointing-Pointer Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. Black-Right-Pointing-Pointer Traditional non-spatial regression models may not provide sufficient information for better solid waste management. Black-Right-Pointing-Pointer Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. Black-Right-Pointing-Pointer Significances of global parameters may diminish at local scale for some provinces. Black-Right-Pointing-Pointer GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation datamore » is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.« less

Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site.

PubMed

Ndiath, Mansour M; Cisse, Badara; Ndiaye, Jean Louis; Gomis, Jules F; Bathiery, Ousmane; Dia, Anta Tal; Gaye, Oumar; Faye, Babacar

2015-11-18

In Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance. This study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis. From Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of -0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R(2) = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast of the district where hotspots are more accurate while low values are mainly found in the centre and in the north. Geographically-weighted regression and OLS showed important risks factors of malaria hotspots in Keur Soce. The outputs of such models can be a useful tool to understand occurrence of malaria hotspots in Senegal. An understanding of geographical variation and determination of the core areas of the disease may provide an explanation regarding possible proximal and distal contributors to malaria elimination in Senegal.

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

PubMed

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

2017-04-12

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

Exploratory study on Marine SDI implementation in Malaysia

NASA Astrophysics Data System (ADS)

Tarmidi, Zakri; Mohd Shariff, Abdul Rashid; Rodzi Mahmud, Ahmad; Zaiton Ibrahim, Zelina; Halim Hamzah, Abdul

2016-06-01

This paper discusses the explanatory study of the implementation of spatial data sharing between Malaysia's marine organisations. The survey method was selected with questionnaire as an instrument for data collection and analysis. The aim of the questionnaire was to determine the critical factors in enabling marine spatial data sharing in Malaysia, and the relationship between these indicators. A questionnaire was sent to 48 marine and coastal organisations in Malaysia, with 84.4% of respondents answering the questionnaire. The respondents selected were people who involved directly with GIS application in the organisations. The results show there are three main issues in implementing spatial data sharing; (1) GIS planning and implementation in the organisation, (2) spatial data sharing knowledge and implementation in the organisation and (3) collaboration to enable spatial data sharing within and between organisations. To improve GIS implementation, spatial data sharing implementation and collaboration in enabling spatial data sharing, a conceptual collaboration model was proposed with components of marine GIS strategic planning, spatial data sharing strategies and collaboration strategy.

The use of GIS to support sustainable management of vineyards in Plovdiv, Bulgaria.

PubMed

Arnaudova, Zh; Bileva, T

2011-01-01

Vine is a traditional branch of plant growing in Bulgaria. The exact location of the vine culture is a specific complex of environmental factors influencing its development, such as climate, soil, landscape and traditions of the region. GIS is a platform to better manage, evaluate and present spatial data in a useful visual form. It's improves the decision-making by combining data with accurate location and management the vineyards. In the present survey were studied and analyzed the factors in choosing an appropriate location for the cultivation of vine varieties in selected regions of Plovdiv. Climatic and soil characteristics, topography and environmental factors as well as presence of virus vector nematodes from family Longidoridae in creating the vines through GIS spatial analysis are taken into account.

Macro and micro geo-spatial environment consideration for landfill site selection in Sharjah, United Arab Emirates.

PubMed

Al-Ruzouq, Rami; Shanableh, Abdallah; Omar, Maher; Al-Khayyat, Ghadeer

2018-02-17

Waste management involves various procedures and resources for proper handling of waste materials in compliance with health codes and environmental regulations. Landfills are one of the oldest, most convenient, and cheapest methods to deposit waste. However, landfill utilization involves social, environmental, geotechnical, cost, and restrictive regulation considerations. For instance, landfills are considered a source of hazardous air pollutants that can cause health and environmental problems related to landfill gas and non-methanic organic compounds. The increasing number of sensors and availability of remotely sensed images along with rapid development of spatial technology are helping with effective landfill site selection. The present study used fuzzy membership and the analytical hierarchy process (AHP) in a geo-spatial environment for landfill site selection in the city of Sharjah, United Arab Emirates. Macro- and micro-level factors were considered; the macro-level contained social and economic factors, while the micro-level accounted for geo-environmental factors. The weighted spatial layers were combined to generate landfill suitability and overall suitability index maps. Sensitivity analysis was then carried out to rectify initial theoretical weights. The results showed that 30.25% of the study area had a high suitability index for landfill sites in the Sharjah, and the most suitable site was selected based on weighted factors. The developed fuzzy-AHP methodology can be applied in neighboring regions with similar geo-natural conditions.

Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models

PubMed Central

2018-01-01

Objectives The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96), not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94) and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68) were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range) and mean (standard deviation) values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01) and 1.05 (1.31), respectively. Conclusions Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas. PMID:29397644

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.

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.

Spatial analysis of land use and shallow groundwater vulnerability in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA

USGS Publications Warehouse

LaMotte, A.E.; Greene, E.A.

2007-01-01

Spatial relations between land use and groundwater quality in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA were analyzed by the use of two spatial models. One model used a logit analysis and the other was based on geostatistics. The models were developed and compared on the basis of existing concentrations of nitrate as nitrogen in samples from 529 domestic wells. The models were applied to produce spatial probability maps that show areas in the watershed where concentrations of nitrate in groundwater are likely to exceed a predetermined management threshold value. Maps of the watershed generated by logistic regression and probability kriging analysis showing where the probability of nitrate concentrations would exceed 3 mg/L (>0.50) compared favorably. Logistic regression was less dependent on the spatial distribution of sampled wells, and identified an additional high probability area within the watershed that was missed by probability kriging. The spatial probability maps could be used to determine the natural or anthropogenic factors that best explain the occurrence and distribution of elevated concentrations of nitrate (or other constituents) in shallow groundwater. This information can be used by local land-use planners, ecologists, and managers to protect water supplies and identify land-use planning solutions and monitoring programs in vulnerable areas. ?? 2006 Springer-Verlag.

[Relationship between reference values of fibrinogen and geographical factors based on neural network analysis].

PubMed

Li, Meng-Jiao; Ge, Miao; Wang, Cong-Xia; Cen, Min-Yi; Jiang, Ji-Lin; He, Jin-Wei; Lin, Qian-Yi; Liu, Xin

2016-08-20

To analyze the relationship between the reference values of fibrinogen (FIB) in healthy Chinese adults and geographical factors to provide scientific evidences for establishing the uniform standard. The reference values of FIB of 10701 Chinese healthy adults from 103 cities were collected to investigate their relationship with 18 geographical factors including spatial index, terrain index, climate index, and soil index. Geographical factors that significantly correlated with the reference values were selected for constructing the BP neural network model. The spatial distribution map of the reference value of FIB of healthy Chinese adults was fitted by disjunctive kriging interpolation. We used the 5-layer neural network and selected 2000 times of training covering 11 hidden layers to build the simulation rule for simulating the relationship between FIB and geographical environmental factors using the MATLAB software. s The reference value of FIB in healthy Chinese adults was significantly correlated with the latitude, sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual range of air temperature, average annual soil gravel content, and soil cation exchange capacity (silt). The artificial neural networks were created to analyze the simulation of the selected indicators of geographical factors. The spatial distribution map of the reference values of FIB in healthy Chinese adults showed a distribution pattern that FIB levels were higher in the South and lower in the North, and higher in the East and lower in the West. When the geographical factors of a certain area are known, the reference values of FIB in healthy Chinese adults can be obtained by establishing the neural network mode or plotting the spatial distribution map.

Do marginalized neighbourhoods have less healthy retail food environments? An analysis using Bayesian spatial latent factor and hurdle models.

PubMed

Luan, Hui; Minaker, Leia M; Law, Jane

2016-08-22

Findings of whether marginalized neighbourhoods have less healthy retail food environments (RFE) are mixed across countries, in part because inconsistent approaches have been used to characterize RFE 'healthfulness' and marginalization, and researchers have used non-spatial statistical methods to respond to this ultimately spatial issue. This study uses in-store features to categorize healthy and less healthy food outlets. Bayesian spatial hierarchical models are applied to explore the association between marginalization dimensions and RFE healthfulness (i.e., relative healthy food access that modelled via a probability distribution) at various geographical scales. Marginalization dimensions are derived from a spatial latent factor model. Zero-inflation occurring at the walkable-distance scale is accounted for with a spatial hurdle model. Neighbourhoods with higher residential instability, material deprivation, and population density are more likely to have access to healthy food outlets within a walkable distance from a binary 'have' or 'not have' access perspective. At the walkable distance scale however, materially deprived neighbourhoods are found to have less healthy RFE (lower relative healthy food access). Food intervention programs should be developed for striking the balance between healthy and less healthy food access in the study region as well as improving opportunities for residents to buy and consume foods consistent with dietary recommendations.

Spatial effects on hybrid electric vehicle adoption

DOE PAGES

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

2017-03-08

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

An Integrative Platform for Three-dimensional Quantitative Analysis of Spatially Heterogeneous Metastasis Landscapes

NASA Astrophysics Data System (ADS)

Guldner, Ian H.; Yang, Lin; Cowdrick, Kyle R.; Wang, Qingfei; Alvarez Barrios, Wendy V.; Zellmer, Victoria R.; Zhang, Yizhe; Host, Misha; Liu, Fang; Chen, Danny Z.; Zhang, Siyuan

2016-04-01

Metastatic microenvironments are spatially and compositionally heterogeneous. This seemingly stochastic heterogeneity provides researchers great challenges in elucidating factors that determine metastatic outgrowth. Herein, we develop and implement an integrative platform that will enable researchers to obtain novel insights from intricate metastatic landscapes. Our two-segment platform begins with whole tissue clearing, staining, and imaging to globally delineate metastatic landscape heterogeneity with spatial and molecular resolution. The second segment of our platform applies our custom-developed SMART 3D (Spatial filtering-based background removal and Multi-chAnnel forest classifiers-based 3D ReconsTruction), a multi-faceted image analysis pipeline, permitting quantitative interrogation of functional implications of heterogeneous metastatic landscape constituents, from subcellular features to multicellular structures, within our large three-dimensional (3D) image datasets. Coupling whole tissue imaging of brain metastasis animal models with SMART 3D, we demonstrate the capability of our integrative pipeline to reveal and quantify volumetric and spatial aspects of brain metastasis landscapes, including diverse tumor morphology, heterogeneous proliferative indices, metastasis-associated astrogliosis, and vasculature spatial distribution. Collectively, our study demonstrates the utility of our novel integrative platform to reveal and quantify the global spatial and volumetric characteristics of the 3D metastatic landscape with unparalleled accuracy, opening new opportunities for unbiased investigation of novel biological phenomena in situ.

Spatially Characterizing Effective Timber Supply

NASA Technical Reports Server (NTRS)

Berry, J. K.; Sailor, J.

1982-01-01

The structure of a computer-oriented cartographic model for assessing roundwood supply for generation of base load electricity is discussed. The model provides an analytical procedure for coupling spatial information of harvesting economics and owner willingness to sell stumpages. Supply is characterized in terms of standing timber; of accessibility considering various harvesting and hauling factors; and of availability as affected by ownership and residential patterns. Factors governing accessibility to timber include effective harvesting distance to haulic roads as modified by barriers and slopes. Haul distance is expressed in units that take into account the relative ease of travel along various road types to a central processing facility. Areas of accessible timber are grouped into spatial units, termed 'timbersheds', of common access to particular haul road segments that belong to unique 'transport zones'. Timber availability considerations include size of ownership parcels, housing density and excluded areas. The analysis techniques are demonstrated for a cartographic data base in western Massachusetts.

Spatially Distinct Neutrophil Responses within the Inflammatory Lesions of Pneumonic Plague

PubMed Central

Stasulli, Nikolas M.; Eichelberger, Kara R.; Price, Paul A.; Pechous, Roger D.; Montgomery, Stephanie A.; Parker, Joel S.

2015-01-01

ABSTRACT During pneumonic plague, the bacterium Yersinia pestis elicits the development of inflammatory lung lesions that continue to expand throughout infection. This lesion development and persistence are poorly understood. Here, we examine spatially distinct regions of lung lesions using laser capture microdissection and transcriptome sequencing (RNA-seq) analysis to identify transcriptional differences between lesion microenvironments. We show that cellular pathways involved in leukocyte migration and apoptosis are downregulated in the center of lung lesions compared to the periphery. Probing for the bacterial factor(s) important for the alteration in neutrophil survival, we show both in vitro and in vivo that Y. pestis increases neutrophil survival in a manner that is dependent on the type III secretion system effector YopM. This research explores the complexity of spatially distinct host-microbe interactions and emphasizes the importance of cell relevance in assays in order to fully understand Y. pestis virulence. PMID:26463167

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

PubMed

Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children

PubMed Central

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S. K.; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P.; Puumalainen, Taneli; Lupisan, Socorro P.; Ruutu, Petri; Ladesma, Erma; Williams, Gail M.; Riley, Ian; Simões, Eric A. F.

2014-01-01

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from −14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs. PMID:24550454

Structural Variability of Tropospheric Growth Factors Transforming Mid-latitude Cyclones to Severe Storms over the North Atlantic

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2015-04-01

The development of European surface wind storms out of normal mid-latitude cyclones is substantially influenced by upstream tropospheric growth factors over the Northern Atlantic. The main factors include divergence and vorticity advection in the upper troposphere, latent heat release and the presence of instabilities of short baroclinic waves of suitable wave lengths. In this study we examine a subset of these potential growth factors and their related influences on the transformation of extra-tropical cyclones into severe damage prone surface storm systems. Previous studies have shown links between specific growth factors and surface wind storms related to extreme cyclones. In our study we investigate in further detail spatial and temporal variability patterns of these upstream processes at different vertical levels of the troposphere. The analyses will comprise of the three growth factors baroclinicity, latent heat release and upper tropospheric divergence. Our definition of surface wind storms is based on the Storm Severity Index (SSI) alongside a wind tracking algorithm identifying areas of exceedances of the local 98th percentile of the 10m wind speed. We also make use of a well-established extra-tropical cyclone identification and tracking algorithm. These cyclone tracks form the base for a composite analysis of the aforementioned growth factors using ERA-Interim Reanalysis from 1979 - 2014 for the extended winter season (ONDJFM). Our composite analysis corroborates previous similar studies but extends them by using an impact based algorithm for the identification of strong wind systems. Based on this composite analysis we further identify variability patterns for each growth factor most important for the transformation of a cyclone into a surface wind storm. We thus also address the question whether the link between storm intensity and related growth factor anomaly taking into account its spatial variability is stable and can be quantified. While the robustness of our preliminary results is generally dependent on the growth factor investigated, some examples include i) the overall availability of latent heat seems to be less important than its spatial structure around the cyclone core and ii) the variability of upper-tropospheric baroclinicity appears to be highest north of the surface position of the cyclone, especially for those that transform into a surface storm.

[Analysis and spatial description to correlative factors on food hygiene appeal and food poison in restaurants of city zone in Qingdao].

PubMed

Liu, Ying; Guo, Xin-biao; Li, Hai-rong; Yang, Lin-sheng

2006-07-01

To study some factors that affected food poison and appeals in restaurants which were hidden danger on the cards. Data on food hygiene events from 2002 to 2004 in restaurants of 14 blocks which were located in the important city zone of Qingdao were collected and studied. The spatial distribution was conducted by means of Geographic Information System (GIS). Possible factors related to food hygiene events were investigated and analysed by NCSS Data statistics software. information of every block were marked on digitalized map by ARCVIEW3.2a software in order to show the spatial distribution of food hygiene events palpably in different areas in the course of three years. It was showed that air temperature, humidity, sunlight length were the important factors of food poison. Average amount of guests and floating population related to administration level of sanitation, the level of sanitation administration, geography location, business status of restaurants related to their status of food sanitation. This study showed the method that analysed and studied status of food sanitation from different areas by GIS were effective, simple and palpable.

Spatial analysis of soil organic carbon in Zhifanggou catchment of the Loess Plateau.

PubMed

Li, Mingming; Zhang, Xingchang; Zhen, Qing; Han, Fengpeng

2013-01-01

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km(2). A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g · kg(-1). The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0-10 and 10-20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.

Contrasting patterns of fine-scale herb layer species composition in temperate forests

NASA Astrophysics Data System (ADS)

Chudomelová, Markéta; Zelený, David; Li, Ching-Feng

2017-04-01

Although being well described at the landscape level, patterns in species composition of forest herb layer are rarely studied at smaller scales. Here, we examined fine-scale environmental determinants and spatial structures of herb layer communities in thermophilous oak- and hornbeam dominated forests of the south-eastern part of the Czech Republic. Species composition of herb layer vegetation and environmental variables were recorded within a fixed grid of 2 × 2 m subplots regularly distributed within 1-ha quadrate plots in three forest stands. For each site, environmental models best explaining species composition were constructed using constrained ordination analysis. Spatial eigenvector mapping was used to model and account for spatial structures in community variation. Mean Ellenberg indicator values calculated for each subplot were used for ecological interpretation of spatially structured residual variation. The amount of variation explained by environmental and spatial models as well as the selection of variables with the best explanatory power differed among sites. As an important environmental factor, relative elevation was common to all three sites, while pH and canopy openness were shared by two sites. Both environmental and community variation was mostly coarse-scaled, as was the spatially structured portion of residual variation. When corrected for bias due to spatial autocorrelation, those environmental factors with already weak explanatory power lost their significance. Only a weak evidence of possibly omitted environmental predictor was found for autocorrelated residuals of site models using mean Ellenberg indicator values. Community structure was determined by different factors at different sites. The relative importance of environmental filtering vs. spatial processes was also site specific, implying that results of fine-scale studies tend to be shaped by local conditions. Contrary to expectations based on other studies, overall dominance of spatial processes at fine scale has not been detected. Ecologists should keep this in mind when making generalizations about community dynamics.

The Uncertainties on the GIS Based Land Suitability Assessment for Urban and Rural Planning

NASA Astrophysics Data System (ADS)

Liu, H.; Zhan, Q.; Zhan, M.

2017-09-01

The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

[Spatial patterns and influence factors of specialization in tea cultivation based on geographically weighted regression model: A case study of Anxi County of Fujian Province, China].

PubMed

Shui, Wei; DU, Yong; Chen, Yi Ping; Jian, Xiao Mei; Fan, Bing Xiong

2017-04-18

Anxi County, specializing in tea cultivation, was taken as a case in this research. Pearson correlation analysis, ordinary least squares model (OLS) and geographically weighted regression model (GWR) were used to select four primary influence factors of specialization in tea cultivation (i.e., the average elevation, net income per capita, proportion of agricultural population, and the distance from roads) by analyzing the specialization degree of each town of Anxi County. Meanwhile, the spatial patterns of specialization in tea cultivation of Anxi County were evaluated. The results indicated that specialization in tea cultivation of Anxi County showed an obvious spatial auto-correlation, and a spatial pattern with "low-middle-high" circle structure, which was similar to Von Thünen's circle structure model, appeared from the county town to its surrounding region. Meanwhile, GWR (0.624) had a better fitting degree than OLS (0.595), and GWR could reasonably expound the spatial data. Contrary to the agricultural location theory of Von Thünen's model, which indicated that distance from market was a determination factor, the specialization degree of tea cultivation in Anxi was mainly decided by natural conditions of mountain area, instead of the social factors. Specialization degree of tea cultivation was positively correlated with the average elevation, net income per capita and the proportion of agricultural population, while a negative correlation was found between the distance from roads and specialization degree of tea cultivation. Coefficients of regression between the specialization degree of tea cultivation and two factors (i.e., the average elevation and net income per capita) showed a spatial pattern of higher level in the north direction and lower level in the south direction. On the contrary, the regression coefficients for the proportion of agricultural population increased from south to north of Anxi County. Furthermore, regression coefficient for the distance from roads showed a spatial pattern of higher level in the northeast direction and lower level in the southwest direction of Anxi County.

Soil analysis based on sa,ples withdrawn from different volumes: correlation versus calibration

Treesearch

Lucian Weilopolski; Kurt Johnsen; Yuen Zhang

2010-01-01

Soil, particularly in forests, is replete with spatial variation with respect to soil C. Th e present standard chemical method for soil analysis by dry combustion (DC) is destructive, and comprehensive sampling is labor intensive and time consuming. Th ese, among other factors, are contributing to the development of new methods for soil analysis. Th ese include a near...

Spatial analysis of effects of mowing and burning on colony expansion in reintroduced black-tailed prairie dog (Cynomys ludovicianus)

Treesearch

Jason Northcott; Mark C. Andersen; Gary W. Roemer; Ed L. Fredrickson; Michael DeMers; Joe Truett; Paulette L. Ford

2008-01-01

Factors governing the rate and direction of prairie dog (Cynomys spp.) colony expansion remain poorly understood. However, increased knowledge and ability to control these factors may lead to more effective reintroductions of prairie dogs and restoration of grassland habitats. We present density and directional analyses of the establishment of new...

Including the third dimension: a spatial analysis of TB cases in Houston Harris County.

PubMed

Feske, Marsha L; Teeter, Larry D; Musser, James M; Graviss, Edward A

2011-12-01

To reach the tuberculosis (TB) elimination goals established by the Institute of Medicine (IOM) and the Centers for Disease Control and Prevention (CDC), measures must be taken to speed the currently stagnant TB elimination rate and curtail a future peak in TB incidence. Increases in TB incidence have historically coincided with immigration, poverty, and joblessness; all situations that are currently occurring worldwide. Effective TB elimination strategies will require the geographical elucidation of areas within the U.S. that have endemic TB, and systematic surveillance of the locations and location-based risk factors associated with TB transmission. Surveillance data was used to assess the spatial distribution of cases, the yearly TB incidence by census tract, and the statistical significance of case clustering. The analysis revealed that there are neighborhoods within Houston/Harris County that had a heavy TB burden. The maximum yearly incidence varied from 245/100,000-754/100,000 and was not exclusively dependent of the number of cases reported. Geographically weighted regression identified risk factors associated with the spatial distribution of cases such as: poverty, age, Black race, and foreign birth. Public transportation was also associated with the spatial distribution of cases and census tracts identified as high incidence were found to be irregularly clustered within communities of varied SES. Copyright © 2011 Elsevier Ltd. All rights reserved.

Ensemble of ground subsidence hazard maps using fuzzy logic

NASA Astrophysics Data System (ADS)

Park, Inhye; Lee, Jiyeong; Saro, Lee

2014-06-01

Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

Collapse Causes Analysis and Numerical Simulation for a Rigid Frame Multiple Arch Bridge

NASA Astrophysics Data System (ADS)

Zuo, XinDai

2018-03-01

Following the collapse accident of Baihe Bridge, the author built a plane model of the whole bridge firstly and analyzed the carrying capacity of the structure for a 170-tons lorry load. Then the author built a spatial finite element model which can accurately simulate the bridge collapse course. The collapse course was simulated and the accident scene was reproduced. Spatial analysis showed rotational stiffness of the pier bottom had a large influence on the collapse from of the superstructures. The conclusion was that the170 tons lorry load and multiple arch bridge design were the important factors leading to collapse.

Dynamics of fractional condensation of a substance on a probe for spectral analysis

NASA Astrophysics Data System (ADS)

Zakharov, Yu. A.; Kokorina, O. B.; Lysogorskiĭ, Yu. V.; Sevastianov, A. A.

2008-11-01

The fractional separation of trace metals on a cold tungsten probe from salt matrix vapor, which interferes with the spectral analysis, is studied. The spatial structure of the vapor flows of sodium chloride, potassium sulfate, and indium atoms is visualized at characteristic wavelengths as they interact with the probe. The vapor flow rate and the probe orientation were varied. It is found that the smoke of the matrix does not prevent the deposition of the metal on the probe because of spatial separation of these fractions and that the detrimental effect of thermal gas expansion and other factors is eliminated. The sensitivity of the atomic absorption analysis of indium impurities in these salts is increased by an order of magnitude.

Model-assisted analysis of spatial and temporal variations in fruit temperature and transpiration highlighting the role of fruit development.

PubMed

Nordey, Thibault; Léchaudel, Mathieu; Saudreau, Marc; Joas, Jacques; Génard, Michel

2014-01-01

Fruit physiology is strongly affected by both fruit temperature and water losses through transpiration. Fruit temperature and its transpiration vary with environmental factors and fruit characteristics. In line with previous studies, measurements of physical and thermal fruit properties were found to significantly vary between fruit tissues and maturity stages. To study the impact of these variations on fruit temperature and transpiration, a modelling approach was used. A physical model was developed to predict the spatial and temporal variations of fruit temperature and transpiration according to the spatial and temporal variations of environmental factors and thermal and physical fruit properties. Model predictions compared well to temperature measurements on mango fruits, making it possible to accurately simulate the daily temperature variations of the sunny and shaded sides of fruits. Model simulations indicated that fruit development induced an increase in both the temperature gradient within the fruit and fruit water losses, mainly due to fruit expansion. However, the evolution of fruit characteristics has only a very slight impact on the average temperature and the transpiration per surface unit. The importance of temperature and transpiration gradients highlighted in this study made it necessary to take spatial and temporal variations of environmental factors and fruit characteristics into account to model fruit physiology.

Bayesian spatial analysis of childhood diseases in Zimbabwe.

PubMed

Tsiko, Rodney Godfrey

2015-09-02

Many sub-Saharan countries are confronted with persistently high levels of childhood morbidity and mortality because of the impact of a range of demographic, biological and social factors or situational events that directly precipitate ill health. In particular, under-five morbidity and mortality have increased in recent decades due to childhood diarrhoea, cough and fever. Understanding the geographic distribution of such diseases and their relationships to potential risk factors can be invaluable for cost effective intervention. Bayesian semi-parametric regression models were used to quantify the spatial risk of childhood diarrhoea, fever and cough, as well as associations between childhood diseases and a range of factors, after accounting for spatial correlation between neighbouring areas. Such semi-parametric regression models allow joint analysis of non-linear effects of continuous covariates, spatially structured variation, unstructured heterogeneity, and other fixed effects on childhood diseases. Modelling and inference made use of the fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulation techniques. The analysis was based on data derived from the 1999, 2005/6 and 2010/11 Zimbabwe Demographic and Health Surveys (ZDHS). The results suggest that until recently, sex of child had little or no significant association with childhood diseases. However, a higher proportion of male than female children within a given province had a significant association with childhood cough, fever and diarrhoea. Compared to their counterparts in rural areas, children raised in an urban setting had less exposure to cough, fever and diarrhoea across all the survey years with the exception of diarrhoea in 2010. In addition, the link between sanitation, parental education, antenatal care, vaccination and childhood diseases was found to be both intuitive and counterintuitive. Results also showed marked geographical differences in the prevalence of childhood diarrhoea, fever and cough. Across all the survey years Manicaland province reported the highest cases of childhood diseases. There is also clear evidence of significant high prevalence of childhood diseases in Mashonaland than in Matabeleland provinces.

Multi-scale approach to the environmental factors effects on spatio-temporal variability of Chironomus salinarius (Diptera: Chironomidae) in a French coastal lagoon

NASA Astrophysics Data System (ADS)

Cartier, V.; Claret, C.; Garnier, R.; Fayolle, S.; Franquet, E.

2010-03-01

The complexity of the relationships between environmental factors and organisms can be revealed by sampling designs which consider the contribution to variability of different temporal and spatial scales, compared to total variability. From a management perspective, a multi-scale approach can lead to time-saving. Identifying environmental patterns that help maintain patchy distribution is fundamental in studying coastal lagoons, transition zones between continental and marine waters characterised by great environmental variability on spatial and temporal scales. They often present organic enrichment inducing decreased species richness and increased densities of opportunist species like C hironomus salinarius, a common species that tends to swarm and thus constitutes a nuisance for human populations. This species is dominant in the Bolmon lagoon, a French Mediterranean coastal lagoon under eutrophication. Our objective was to quantify variability due to both spatial and temporal scales and identify the contribution of different environmental factors to this variability. The population of C. salinarius was sampled from June 2007 to June 2008 every two months at 12 sites located in two areas of the Bolmon lagoon, at two different depths, with three sites per area-depth combination. Environmental factors (temperature, dissolved oxygen both in sediment and under water surface, sediment organic matter content and grain size) and microbial activities (i.e. hydrolase activities) were also considered as explanatory factors of chironomid densities and distribution. ANOVA analysis reveals significant spatial differences regarding the distribution of chironomid larvae for the area and the depth scales and their interaction. The spatial effect is also revealed for dissolved oxygen (water), salinity and fine particles (area scale), and for water column depth. All factors but water column depth show a temporal effect. Spearman's correlations highlight the seasonal effect (temperature, dissolved oxygen in sediment and water) as well as the effect of microbial activities on chironomid larvae. Our results show that a multi-scale approach identifies patchy distribution, even when there is relative environmental homogeneity.

The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations

PubMed Central

Yang, Qianqian; Li, Tongwen; Shen, Huanfeng; Zhang, Liangpei

2017-01-01

The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 exist. Spatially, RH is positively correlated with PM2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency. PMID:29206181

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

PubMed

Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

2012-10-24

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

[Lake eutrophication modeling in considering climatic factors change: a review].

PubMed

Su, Jie-Qiong; Wang, Xuan; Yang, Zhi-Feng

2012-11-01

Climatic factors are considered as the key factors affecting the trophic status and its process in most lakes. Under the background of global climate change, to incorporate the variations of climatic factors into lake eutrophication models could provide solid technical support for the analysis of the trophic evolution trend of lake and the decision-making of lake environment management. This paper analyzed the effects of climatic factors such as air temperature, precipitation, sunlight, and atmosphere on lake eutrophication, and summarized the research results about the lake eutrophication modeling in considering in considering climatic factors change, including the modeling based on statistical analysis, ecological dynamic analysis, system analysis, and intelligent algorithm. The prospective approaches to improve the accuracy of lake eutrophication modeling with the consideration of climatic factors change were put forward, including 1) to strengthen the analysis of the mechanisms related to the effects of climatic factors change on lake trophic status, 2) to identify the appropriate simulation models to generate several scenarios under proper temporal and spatial scales and resolutions, and 3) to integrate the climatic factors change simulation, hydrodynamic model, ecological simulation, and intelligent algorithm into a general modeling system to achieve an accurate prediction of lake eutrophication under climatic change.

Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models

PubMed Central

Li, Shujuan; Ren, Hongyan; Hu, Wensheng; Lu, Liang; Xu, Xinliang; Zhuang, Dafang; Liu, Qiyong

2014-01-01

Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in China. The identification of the spatiotemporal pattern of HFRS will provide a foundation for the effective control of the disease. Based on the incidence of HFRS, as well as environmental factors, and social-economic factors of China from 2005–2012, this paper identified the spatiotemporal characteristics of HFRS distribution and the factors that impact this distribution. The results indicate that the spatial distribution of HFRS had a significant, positive spatial correlation. The spatiotemporal heterogeneity was affected by the temperature, precipitation, humidity, NDVI of January, NDVI of August for the previous year, land use, and elevation in 2005–2009. However, these factors did not explain the spatiotemporal heterogeneity of HFRS incidences in 2010–2012. Spatiotemporal heterogeneity of provincial HFRS incidences and its relation to environmental factors would provide valuable information for hygiene authorities to design and implement effective measures for the prevention and control of HFRS in China. PMID:25429681

Analysis of Spatial Pattern and Influencing Factors of E-Commerce

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, J.; Zhang, S.

2017-09-01

This paper aims to study the relationship between e-commerce development and geographical characteristics using data of e-commerce, economy, Internet, express delivery and population from 2011 to 2015. Moran's I model and GWR model are applied to analyze the spatial pattern of E-commerce and its influencing factors. There is a growth trend of e-commerce from west to east, and it is obvious to see that e-commerce development has a space-time clustering, especially around the Yangtze River delta. The comprehensive factors caculated through PCA are described as fundamental social productivity, resident living standard and population sex structure. The first two factors have positive correlation with e-commerce, and the intensity of effect increases yearly. However, the influence of population sex structure on the E-commerce development is not significant. Our results suggest that the clustering of e-commerce has a downward trend and the impact of driving factors on e-commerce is observably distinct from year to year in space.

Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modelling

PubMed Central

Osei, Frank B; Duker, Alfred A

2008-01-01

Background Cholera has persisted in Ghana since its introduction in the early 70's. From 1999 to 2005, the Ghana Ministry of Health officially reported a total of 26,924 cases and 620 deaths to the WHO. Etiological studies suggest that the natural habitat of V. cholera is the aquatic environment. Its ability to survive within and outside the aquatic environment makes cholera a complex health problem to manage. Once the disease is introduced in a population, several environmental factors may lead to prolonged transmission and secondary cases. An important environmental factor that predisposes individuals to cholera infection is sanitation. In this study, we exploit the importance of two main spatial measures of sanitation in cholera transmission in an urban city, Kumasi. These are proximity and density of refuse dumps within a community. Results A spatial statistical modelling carried out to determine the spatial dependency of cholera prevalence on refuse dumps show that, there is a direct spatial relationship between cholera prevalence and density of refuse dumps, and an inverse spatial relationship between cholera prevalence and distance to refuse dumps. A spatial scan statistics also identified four significant spatial clusters of cholera; a primary cluster with greater than expected cholera prevalence, and three secondary clusters with lower than expected cholera prevalence. A GIS based buffer analysis also showed that the minimum distance within which refuse dumps should not be sited within community centres is 500 m. Conclusion The results suggest that proximity and density of open space refuse dumps play a contributory role in cholera infection in Kumasi. PMID:19087235

Influence of local meteorology and NO2 conditions on ground-level ozone concentrations in the eastern part of Texas, USA.

PubMed

Gorai, A K; Tuluri, F; Tchounwou, P B; Ambinakudige, S

2015-02-01

The influence of local climatic factors on ground-level ozone concentrations is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis on the role of temperature, wind speed, wind direction, and NO 2 level on ground-level ozone concentrations over the region of Eastern Texas, USA. Ozone concentrations at the ground level depend on the formation and dispersion processes. Formation process mainly depends on the precursor sources, whereas, the dispersion of ozone depends on meteorological factors. Study results showed that the spatial mean of ground-level ozone concentrations was highly dependent on the spatial mean of NO 2 concentrations. However, spatial distributions of NO 2 and ozone concentrations were not uniformed throughout the study period due to uneven wind speeds and wind directions. Wind speed and wind direction also played a significant role in the dispersion of ozone. Temperature profile in the area rarely had any effects on the ozone concentrations due to low spatial variations.

Single mode, broad-waveguide ARROW-type semiconductor diode lasers

NASA Astrophysics Data System (ADS)

Al-Muhanna, Abdulrahman Ali

A broad transverse waveguide (low confinement) concept is used to achieve a record-high spatially incoherent cw output power of 11W for InGaAs active devices (λ = 0.97 μm) from 100μm wide-stripe and 2mm-long devices with low internal loss, α1 = 1cm-1, and high characteristic temperatures, T0 = 210K, and T1 = 1800K. A detailed above-threshold analysis reveals that reduction in gain spatial hole burning (GSHB) is possible in ARROW-type structures by using a low transverse confinement factor; consequently, a wider ARROW-core can be utilized. By incorporating both a broad-waveguide concept as well as an asymmetric structure in the transverse direction, and an ARROW-type structure in the lateral direction, a novel single-spatial mode diode laser with improved performance is obtained. Devices with low transverse confinement factor (Γ ~ 1%) and a core-region width of 7.8 μm achieved 510mW single-spatial mode pulsed output power (λ = 0.946 μm) with a full- width at half-maximum (FWHM) of the lateral far-field pattern of 4.7°.

Influence of local meteorology and NO2 conditions on ground-level ozone concentrations in the eastern part of Texas, USA

PubMed Central

Gorai, A. K.; Tuluri, F.; Tchounwou, P. B.; Ambinakudige, S.

2014-01-01

The influence of local climatic factors on ground-level ozone concentrations is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis on the role of temperature, wind speed, wind direction, and NO2 level on ground-level ozone concentrations over the region of Eastern Texas, USA. Ozone concentrations at the ground level depend on the formation and dispersion processes. Formation process mainly depends on the precursor sources, whereas, the dispersion of ozone depends on meteorological factors. Study results showed that the spatial mean of ground-level ozone concentrations was highly dependent on the spatial mean of NO2 concentrations. However, spatial distributions of NO2 and ozone concentrations were not uniformed throughout the study period due to uneven wind speeds and wind directions. Wind speed and wind direction also played a significant role in the dispersion of ozone. Temperature profile in the area rarely had any effects on the ozone concentrations due to low spatial variations. PMID:25755687

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

PubMed

Liévanos, Raoul S

2015-11-01

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

Regulation of Oligomeric Organization of the Serotonin 5-Hydroxytryptamine 2C (5-HT2C) Receptor Observed by Spatial Intensity Distribution Analysis*

PubMed Central

Ward, Richard J.; Pediani, John D.; Godin, Antoine G.; Milligan, Graeme

2015-01-01

The questions of whether G protein-coupled receptors exist as monomers, dimers, and/or oligomers and if these species interconvert in a ligand-dependent manner are among the most contentious current issues in biology. When employing spatial intensity distribution analysis to laser scanning confocal microscope images of cells stably expressing either a plasma membrane-associated form of monomeric enhanced green fluorescent protein (eGFP) or a tandem version of this fluorophore, the eGFP tandem was identified as a dimer. Similar studies on cells stably expressing an eGFP-tagged form of the epidermal growth factor receptor demonstrated that, although largely a monomer in the basal state, this receptor rapidly became predominantly dimeric upon the addition of its ligand epidermal growth factor. In cells induced to express an eGFP-tagged form of the serotonin 5-hydroxytryptamine 2C (5-HT2C) receptor, global analysis of construct quantal brightness was consistent with the predominant form of the receptor being dimeric. However, detailed spatial intensity distribution analysis demonstrated the presence of multiple forms ranging from monomers to higher-order oligomers. Furthermore, treatment with chemically distinct 5-HT2C receptor antagonists resulted in a time-dependent change in the quaternary organization to one in which there was a preponderance of receptor monomers. This antagonist-mediated effect was reversible, because washout of the ligand resulted in the regeneration of many of the oligomeric forms of the receptor. PMID:25825490

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

PubMed Central

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

2014-01-01

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

Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia

PubMed Central

Pérez-Flórez, Mauricio; Ocampo, Clara Beatriz; Valderrama-Ardila, Carlos; Alexander, Neal

2016-01-01

The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America. PMID:27355214

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

PubMed

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

2015-09-01

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

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).

Environmental drivers and spatial dependency in wildfire ignition patterns of northwestern Patagonia.

PubMed

Mundo, Ignacio A; Wiegand, Thorsten; Kanagaraj, Rajapandian; Kitzberger, Thomas

2013-07-15

Fire management requires an understanding of the spatial characteristics of fire ignition patterns and how anthropogenic and natural factors influence ignition patterns across space. In this study we take advantage of a recent fire ignition database (855 points) to conduct a comprehensive analysis of the spatial pattern of fire ignitions in the western area of Neuquén province (57,649 km(2)), Argentina, for the 1992-2008 period. The objectives of our study were to better understand the spatial pattern and the environmental drivers of the fire ignitions, with the ultimate aim of supporting fire management. We conducted our analyses on three different levels: statistical "habitat" modelling of fire ignition (natural, anthropogenic, and all causes) based on an information theoretic approach to test several competing hypotheses on environmental drivers (i.e. topographic, climatic, anthropogenic, land cover, and their combinations); spatial point pattern analysis to quantify additional spatial autocorrelation in the ignition patterns; and quantification of potential spatial associations between fires of different causes relative to towns using a novel implementation of the independence null model. Anthropogenic fire ignitions were best predicted by the most complex habitat model including all groups of variables, whereas natural ignitions were best predicted by topographic, climatic and land-cover variables. The spatial pattern of all ignitions showed considerable clustering at intermediate distances (<40 km) not captured by the probability of fire ignitions predicted by the habitat model. There was a strong (linear) and highly significant increase in the density of fire ignitions with decreasing distance to towns (<5 km), but fire ignitions of natural and anthropogenic causes were statistically independent. A two-dimensional habitat model that quantifies differences between ignition probabilities of natural and anthropogenic causes allows fire managers to delineate target areas for consideration of major preventive treatments, strategic placement of fuel treatments, and forecasting of fire ignition. The techniques presented here can be widely applied to situations where a spatial point pattern is jointly influenced by extrinsic environmental factors and intrinsic point interactions. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: A geospatial analysis of five years of police crash and census data

PubMed Central

Dezman, Zachary; de Andrade, Luciano; Vissoci, Joao Ricardo; El-Gabri, Deena; Johnson, Abree; Hirshon, Jon Mark; Staton, Catherine A.

2017-01-01

Introduction Road traffic injuries are a leading killer of youth (aged 15–29) and are projected to be the 7th leading cause of death by 2030. To better understand road traffic crash locations and characteristics in the city of Baltimore, we used police and census data, to describe the epidemiology, hotspots, and modifiable risk factors involved to guide further interventions. Materials and methods Data on all crashes in Baltimore City from 2009 to 2013 were made available from the Maryland Automated Accident Reporting System. Socioeconomic data collected by the US CENSUS 2010 were obtained. A time series analysis was conducted using an ARIMA model. We analyzed the geographical distribution of traffic crashes and hotspots using exploratory spatial data analysis and spatial autocorrelation. Spatial regression was performed to evaluate the impact of socioeconomic indicators on hotspots. Results In Baltimore City, between 2009 and 2013, there were a total of 100,110 crashes reported, with 1% of crashes considered severe. Of all crashes, 7% involved vulnerable road users and 12% had elderly or youth involvement. Reasons for crashes included: distracted driving (31%), speeding (6%), and alcohol or drug use (5%). After 2010, we observed an increasing trend in all crashes especially from March to June. Distracted driving then youth and elderly drivers were consistently the highest risk factors over time. Multivariate spatial regression model including socioeconomic indicators and controlling for age, gender and population size did not show a distinct predictor of crashes explaining only 20% of the road crash variability, indicating crashes are not geographically explained by socioeconomic indicators alone. Conclusion In Baltimore City, road traffic crashes occurred predominantly in the high density center of the city, involved distracted driving and extremes of age with an increase in crashes from March to June. There was no association between socioeconomic variables where crashes occurred and hotspots. In depth analysis of how modifiable risk factors are impacted by geospatial characteristics and the built environment is warranted in Baltimore to tailor interventions. PMID:27614672

Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: A geospatial analysis of five years of police crash and census data.

PubMed

Dezman, Zachary; de Andrade, Luciano; Vissoci, Joao Ricardo; El-Gabri, Deena; Johnson, Abree; Hirshon, Jon Mark; Staton, Catherine A

2016-11-01

Road traffic injuries are a leading killer of youth (aged 15-29) and are projected to be the 7th leading cause of death by 2030. To better understand road traffic crash locations and characteristics in the city of Baltimore, we used police and census data, to describe the epidemiology, hotspots, and modifiable risk factors involved to guide further interventions. Data on all crashes in Baltimore City from 2009 to 2013 were made available from the Maryland Automated Accident Reporting System. Socioeconomic data collected by the US CENSUS 2010 were obtained. A time series analysis was conducted using an ARIMA model. We analyzed the geographical distribution of traffic crashes and hotspots using exploratory spatial data analysis and spatial autocorrelation. Spatial regression was performed to evaluate the impact of socioeconomic indicators on hotspots. In Baltimore City, between 2009 and 2013, there were a total of 100,110 crashes reported, with 1% of crashes considered severe. Of all crashes, 7% involved vulnerable road users and 12% had elderly or youth involvement. Reasons for crashes included: distracted driving (31%), speeding (6%), and alcohol or drug use (5%). After 2010, we observed an increasing trend in all crashes especially from March to June. Distracted driving then youth and elderly drivers were consistently the highest risk factors over time. Multivariate spatial regression model including socioeconomic indicators and controlling for age, gender and population size did not show a distinct predictor of crashes explaining only 20% of the road crash variability, indicating crashes are not geographically explained by socioeconomic indicators alone. In Baltimore City, road traffic crashes occurred predominantly in the high density center of the city, involved distracted driving and extremes of age with an increase in crashes from March to June. There was no association between socioeconomic variables where crashes occurred and hotspots. In depth analysis of how modifiable risk factors are impacted by geospatial characteristics and the built environment is warranted in Baltimore to tailor interventions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Socio-Ecological Risk Factors for Prime-Age Adult Death in Two Coastal Areas of Vietnam

PubMed Central

Kim, Deok Ryun; Ali, Mohammad; Thiem, Vu Dinh; Wierzba, Thomas F.

2014-01-01

Background Hierarchical spatial models enable the geographic and ecological analysis of health data thereby providing useful information for designing effective health interventions. In this study, we used a Bayesian hierarchical spatial model to evaluate mortality data in Vietnam. The model enabled identification of socio-ecological risk factors and generation of risk maps to better understand the causes and geographic implications of prime-age (15 to less than 45 years) adult death. Methods and Findings The study was conducted in two sites: Nha Trang and Hue in Vietnam. The study areas were split into 500×500 meter cells to define neighborhoods. We first extracted socio-demographic data from population databases of the two sites, and then aggregated the data by neighborhood. We used spatial hierarchical model that borrows strength from neighbors for evaluating risk factors and for creating spatially smoothed risk map after adjusting for neighborhood level covariates. The Markov chain Monte Carlo procedure was used to estimate the parameters. Male mortality was more than twice the female mortality. The rates also varied by age and sex. The most frequent cause of mortality was traffic accidents and drowning for men and traffic accidents and suicide for women. Lower education of household heads in the neighborhood was an important risk factor for increased mortality. The mortality was highly variable in space and the socio-ecological risk factors are sensitive to study site and sex. Conclusion Our study suggests that lower education of the household head is an important predictor for prime age adult mortality. Variability in socio-ecological risk factors and in risk areas by sex make it challenging to design appropriate intervention strategies aimed at decreasing prime-age adult deaths in Vietnam. PMID:24587031

Socio-ecological risk factors for prime-age adult death in two coastal areas of Vietnam.

PubMed

Kim, Deok Ryun; Ali, Mohammad; Thiem, Vu Dinh; Wierzba, Thomas F

2014-01-01

Hierarchical spatial models enable the geographic and ecological analysis of health data thereby providing useful information for designing effective health interventions. In this study, we used a Bayesian hierarchical spatial model to evaluate mortality data in Vietnam. The model enabled identification of socio-ecological risk factors and generation of risk maps to better understand the causes and geographic implications of prime-age (15 to less than 45 years) adult death. The study was conducted in two sites: Nha Trang and Hue in Vietnam. The study areas were split into 500×500 meter cells to define neighborhoods. We first extracted socio-demographic data from population databases of the two sites, and then aggregated the data by neighborhood. We used spatial hierarchical model that borrows strength from neighbors for evaluating risk factors and for creating spatially smoothed risk map after adjusting for neighborhood level covariates. The Markov chain Monte Carlo procedure was used to estimate the parameters. Male mortality was more than twice the female mortality. The rates also varied by age and sex. The most frequent cause of mortality was traffic accidents and drowning for men and traffic accidents and suicide for women. Lower education of household heads in the neighborhood was an important risk factor for increased mortality. The mortality was highly variable in space and the socio-ecological risk factors are sensitive to study site and sex. Our study suggests that lower education of the household head is an important predictor for prime age adult mortality. Variability in socio-ecological risk factors and in risk areas by sex make it challenging to design appropriate intervention strategies aimed at decreasing prime-age adult deaths in Vietnam.

Spatio-temporal patterns of hazards and their use in risk assessment and mitigation. Case study of road accidents in Romania

NASA Astrophysics Data System (ADS)

Catalin Stanga, Iulian

2013-04-01

Road accidents are among the leading causes of death in many world countries, partly as an inherent consequence of the increasing mobility of today society. The World Health Organization estimates that 1.3 million people died in road accidents in 2011, which means 186 deaths per million. The tragic picture is completed by millions of peoples experiencing different physical injuries or by the enormous social and economic costs that these events imply. Romania has one of the most unsafe road networks within the European Union, with annual averages of 9400 accidents, 8300 injuries and almost 2680 fatalities (2007-2012). An average of 141 death per million is more than twice the average fatality rate in European Union (about 60 death per million). Other specific indicators (accidents or fatalities reported to the road length, vehicle fleet size, driving license owners or adult population etc.) are even worst in the same European context. Road accidents are caused by a complex series of factors, some of them being a relatively constant premise, while others act as catalyzing factors or triggering agent: road features and quality, vehicle technical state, weather conditions, human related factors etc. All these lead to a complex equation with too many unknown variables, making almost impossible a probabilistic approach. However, the high concentration of accidents in a region or in some road sectors is caused by the existence of a specific context, created by factors with permanent or repetitive character, and leads to the idea of a spatial autocorrelation between locations of different adjoining accident. In the same way, the increasing frequency of road accidents and of their causes repeatability in different periods of the year would allow to identify those black timeframes with higher incidence of road accidents. Identifying and analyzing the road blackspots (hotspots) and black zones would help to improve road safety by acting against the common causes that create the spatial or temporal clustering of crash accidents. Since the 1990's, Geographical Informational Systems (GIS) became a very important tool for traffic and road safety management, allowing not only the spatial and multifactorial analysis, but also graphical and non-graphical outputs. The current paper presents an accessible GIS methodology to study the spatio-temporal pattern of injury related road accidents, to identify the high density accidents zones, to make a cluster analysis, to create multicriterial typologies, to identify spatial and temporal similarities and to explain them. In this purpose, a Geographical Information System was created, allowing a complex analysis that involves not only the events, but also a large set of interrelated and spatially linked attributes. The GIS includes the accidents as georeferenced point elements with a spatially linked attribute database: identification information (date, location details); accident type; main, secondary and aggravating causes; data about driver; vehicle information; consequences (damages, injured peoples and fatalities). Each attribute has its own number code that allows both the statistical analysis and the spatial interrogation. The database includes those road accidents that led to physical injuries and loss of human lives between 2007 and 2012 and the spatial analysis was realized using TNTmips 7.3 software facilities. Data aggregation and processing allowed creating the spatial pattern of injury related road accidents through Kernel density estimation at three different levels (national - Romania; county level - Iasi County; local level - Iasi town). Spider graphs were used to create the temporal pattern or road accidents at three levels (daily, weekly and monthly) directly related to their causes. Moreover the spatial and temporal database relates the natural hazards (glazed frost, fog, and blizzard) with the human made ones, giving the opportunity to evaluate the nature of uncertainties in risk assessment. At the end, this paper provides a clustering methodology based on several environmental indicators intended to classify the spatial and temporal hotspots of road traffic insecurity. The results are a useful guide for planners and decision makers in developing effective road safety strategies and measures.

Using multi-spectral imagery to detect and map stress induced by Russian wheat aphid

NASA Astrophysics Data System (ADS)

Backoulou, Georges Ferdinand

Scope and Method of Study. The rationale of this study was to assess the stress in wheat field induced by the Russian wheat aphid using multispectral imagery. The study was conducted to (a) determine the relationship between RWA and edaphic and topographic factors; (b) identify and quantify the spatial pattern of RWA infestation within wheat fields; (c) differentiate the stress induced by RWA from other stress causing factors. Data used for the analysis included RWA population density from the wheat field in, Texas, Colorado, Wyoming, and Nebraska, Digital Elevation Model from the Unites States Geological Survey (USGS), soil data from the Soil Survey Geographic database (SSURGO), and multispectral imagery acquired in the panhandle of Oklahoma. Findings and Conclusions. The study revealed that the population density of the Russian wheat aphid was related to topographic and edaphic factors. Slope and sand were predictor variables that were positively related to the density of RWA at the field level. The study has also demonstrated that stress induced by the RWA has a specific spatial pattern that can be distinguished from other stress causing factors using a combination of landscape metrics and topographic and edaphic characteristics of wheat fields. Further field-based studies using multispectral imagery and spatial pattern analysis are suggested. The suggestions require acquiring biweekly multispectral imagery and collecting RWA, topographic and edaphic data at the sampling points during the phonological growth development of wheat plants. This is an approach that may pretend to have great potential for site specific technique for the integrated pest management.

Influences of space, soil, nematodes and plants on microbial community composition of chalk grassland soils.

PubMed

Yergeau, Etienne; Bezemer, T Martijn; Hedlund, Katarina; Mortimer, Simon R; Kowalchuk, George A; Van Der Putten, Wim H

2010-08-01

Microbial communities respond to a variety of environmental factors related to resources (e.g. plant and soil organic matter), habitat (e.g. soil characteristics) and predation (e.g. nematodes, protozoa and viruses). However, the relative contribution of these factors on microbial community composition is poorly understood. Here, we sampled soils from 30 chalk grassland fields located in three different chalk hill ridges of Southern England, using a spatially explicit sampling scheme. We assessed microbial communities via phospholipid fatty acid (PLFA) analyses and PCR-denaturing gradient gel electrophoresis (DGGE) and measured soil characteristics, as well as nematode and plant community composition. The relative influences of space, soil, vegetation and nematodes on soil microorganisms were contrasted using variation partitioning and path analysis. Results indicate that soil characteristics and plant community composition, representing habitat and resources, shape soil microbial community composition, whereas the influence of nematodes, a potential predation factor, appears to be relatively small. Spatial variation in microbial community structure was detected at broad (between fields) and fine (within fields) scales, suggesting that microbial communities exhibit biogeographic patterns at different scales. Although our analysis included several relevant explanatory data sets, a large part of the variation in microbial communities remained unexplained (up to 92% in some analyses). However, in several analyses, significant parts of the variation in microbial community structure could be explained. The results of this study contribute to our understanding of the relative importance of different environmental and spatial factors in driving the composition of soil-borne microbial communities. © 2009 Society for Applied Microbiology and Blackwell Publishing Ltd.

Possible Signaling Pathways Mediating Neuronal Calcium Sensor-1-Dependent Spatial Learning and Memory in Mice.

PubMed

Nakamura, Tomoe Y; Nakao, Shu; Nakajo, Yukako; Takahashi, Jun C; Wakabayashi, Shigeo; Yanamoto, Hiroji

2017-01-01

Intracellular Ca2+ signaling regulates diverse functions of the nervous system. Many of these neuronal functions, including learning and memory, are regulated by neuronal calcium sensor-1 (NCS-1). However, the pathways by which NCS-1 regulates these functions remain poorly understood. Consistent with the findings of previous reports, we revealed that NCS-1 deficient (Ncs1-/-) mice exhibit impaired spatial learning and memory function in the Morris water maze test, although there was little change in their exercise activity, as determined via treadmill-analysis. Expression of brain-derived neurotrophic factor (BDNF; a key regulator of memory function) and dopamine was significantly reduced in the Ncs1-/- mouse brain, without changes in the levels of glial cell-line derived neurotrophic factor or nerve growth factor. Although there were no gross structural abnormalities in the hippocampi of Ncs1-/- mice, electron microscopy analysis revealed that the density of large dense core vesicles in CA1 presynaptic neurons, which release BDNF and dopamine, was decreased. Phosphorylation of Ca2+/calmodulin-dependent protein kinase II-α (CaMKII-α, which is known to trigger long-term potentiation and increase BDNF levels, was significantly reduced in the Ncs1-/- mouse brain. Furthermore, high voltage electric potential stimulation, which increases the levels of BDNF and promotes spatial learning, significantly increased the levels of NCS-1 concomitant with phosphorylated CaMKII-α in the hippocampus; suggesting a close relationship between NCS-1 and CaMKII-α. Our findings indicate that NCS-1 may regulate spatial learning and memory function at least in part through activation of CaMKII-α signaling, which may directly or indirectly increase BDNF production.

Validation of the Korean Version of the Spatial Hearing Questionnaire for Assessing the Severity and Symmetry of Hearing Impairment.

PubMed

Kong, Tae Hoon; Park, Yoon Ah; Bong, Jeong Pyo; Park, Sang Yoo

2017-07-01

Spatial hearing refers to the ability to understand speech and identify sounds in various environments. We assessed the validity of the Korean version of the Spatial Hearing Questionnaire (K-SHQ). We performed forward translation of the original English SHQ to Korean and backward translation from the Korean to English. Forty-eight patients who were able to read and understand Korean and received a score of 24 or higher on the Mini-Mental Status Examination were included in the study. Patients underwent pure tone audiometry (PTA) using a standard protocol and completed the K-SHQ. Internal consistency was evaluated using Cronbach's alpha, and factor analysis was performed to prove reliability. Construct validity was tested by comparing K-SHQ scores from patients with normal hearing to those with hearing impairment. Scores were compared between subjects with unilateral or bilateral hearing loss and between symmetrical and asymmetrical hearing impairment. Cronbach's alpha showed good internal consistency (0.982). Two factors were identified by factor analysis: There was a significant difference in K-SHQ scores for patients with normal hearing compared to those with hearing impairment. Patients with asymmetric hearing impairment had higher K-SHQ scores than those with symmetric hearing impairment. This is related to a lower threshold of PTA in the better ear of subjects. The hearing ability of the better ear is correlated with K-SHQ score. The K-SHQ is a reliable and valid tool with which to assess spatial hearing in patients who speak and read Korean. K-SHQ score reflects the severity and symmetry of hearing impairment. © Copyright: Yonsei University College of Medicine 2017

Coupled Effects of Natural and Anthropogenic Controls on Seasonal and Spatial Variations of River Water Quality during Baseflow in a Coastal Watershed of Southeast China

PubMed Central

Huang, Jinliang; Huang, Yaling; Zhang, Zhenyu

2014-01-01

Surface water samples of baseflow were collected from 20 headwater sub-watersheds which were classified into three types of watersheds (natural, urban and agricultural) in the flood, dry and transition seasons during three consecutive years (2010–2012) within a coastal watershed of Southeast China. Integrating spatial statistics with multivariate statistical techniques, river water quality variations and their interactions with natural and anthropogenic controls were examined to identify the causal factors and underlying mechanisms governing spatiotemporal patterns of water quality. Anthropogenic input related to industrial effluents and domestic wastewater, agricultural activities associated with the precipitation-induced surface runoff, and natural weathering process were identified as the potential important factors to drive the seasonal variations in stream water quality for the transition, flood and dry seasons, respectively. All water quality indicators except SRP had the highest mean concentrations in the dry and transition seasons. Anthropogenic activities and watershed characteristics led to the spatial variations in stream water quality in three types of watersheds. Concentrations of NH4 +-N, SRP, K+, CODMn, and Cl− were generally highest in urban watersheds. NO3 –N Concentration was generally highest in agricultural watersheds. Mg2+ concentration in natural watersheds was significantly higher than that in agricultural watersheds. Spatial autocorrelations analysis showed similar levels of water pollution between the neighboring sub-watersheds exhibited in the dry and transition seasons while non-point source pollution contributed to the significant variations in water quality between neighboring sub-watersheds. Spatial regression analysis showed anthropogenic controls played critical roles in variations of water quality in the JRW. Management implications were further discussed for water resource management. This research demonstrates that the coupled effects of natural and anthropogenic controls involved in watershed processes, contribute to the seasonal and spatial variation of headwater stream water quality in a coastal watershed with high spatial variability and intensive anthropogenic activities. PMID:24618771

Interpreting Intra-site Spatial Patterns in Seasonal Contexts: an Ethnoarchaeological Case Study from the Western Alps.

PubMed

Carrer, Francesco

2017-01-01

This paper deals with the ethnoarchaeological analysis of the spatial pattern of artefacts and ecofacts within two traditional pastoral huts (a dwelling and a seasonal dairy) in the uplands of Val Maudagna (Cuneo province, Italian western Alps). The composition of the ethnoarchaeological assemblages of the two huts was studied and compared; point pattern analysis was applied to identify spatial processes mirrored in the interactions between objects; Moran's I correlogram and empirical variogram were used to investigate the effects of trampling on the displacement of objects on the floor. The results were compared with information provided by the herder who still used the huts. The quantitative and ethnographical data enabled inferences to be made that can help in the interpretation of archaeological seasonal sites. The function of a seasonal site can be recognized, as can the impact of delayed curation on the composition of the assemblage and the importance of the intensity of occupation compared with the frequency of occupation. The spatial organization of activities is reflected in the spatial patterns of objects, with clearer identification of activity areas in intensively occupied sites, and there is evidence for the behaviour behind the spatial segregation of activities. Trampling is a crucial post-depositional factor in the displacement of artefacts and ecofacts, especially in non-intensively exploited sites. From a methodological point of view, this research is another example that highlights the importance of integrating quantitative methods (especially spatial analysis and geostatistical methods) and ethnoarchaeological data in order to improve the interpretation of archaeological sites and assemblages.

Accuracy of stream habitat interpolations across spatial scales

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2013-01-01

Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2 . Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2 ) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2 . Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled datasets.

Impact of delay on disease outbreak in a spatial epidemic model

NASA Astrophysics Data System (ADS)

Zhao, Xia-Xia; Wang, Jian-Zhong

2015-04-01

One of the central issues in studying epidemic spreading is the mechanism on disease outbreak. In this paper, we investigate the effects of time delay on disease outbreak in spatial epidemics based on a reaction-diffusion model. By mathematical analysis and numerical simulations, we show that when time delay is more than a critical value, the disease outbreaks. The obtained results show that the time delay is an important factor in the spread of the disease, which may provide new insights on disease control.

Analysis of factors which limited the spatial variation of barley yield on the forest-steppe chernozems of Kursk region

NASA Astrophysics Data System (ADS)

Belik, Anton; Vasenev, Ivan; Jablonskikh, Lidia; Bozhko, Svetlana

2017-04-01

The crop yield is the most important indicator of the efficiency of agricultural production. It is the function that depends on a large number of groups of independent variables, such as the weather, soil fertility and overall culture agriculture. A huge number of combinations of these factors contribute to the formation of high spatial variety of crop yields within small areas, includes the slope agrolandscapes in Kursk region. Spatial variety of yield leads to a significant reduction in the efficiency of agriculture. In this connection, evaluation and analysis of the factors, which limits the yield of field crops is a very urgent proble in agroecology. The research was conducted in the period of 2003-2004 on a representative field. The typical and leached chernozems with the varying thickness and of erosion degree are dominated in soil cover. At the time of field research studied areas were busy by barley. The reseached soils have an average and increased fertility level. Chernozem typical full-face, and the leached contain an average of 4.5-6% humus, close to neutral pH, favorable values of physico-chemical parameters, medium and high content of nutrients. The eroded chernozems differs agrogenic marked declining in fertility parameters. The diversity of meso- and micro-relief in the fields and soil cover influence to significant spatial variety of fertility. For example the content of nutrients in the soil variation can be up to 5-fold level. High spatial heterogeneity of soils fertility ifluence to barley yield variety. During research on the productivity of the field varied in the range of 20-43 c/ha, and 7-44 c/ha (2004). Analysis of the factors, which limited the yield of barley, showed that the first priorities occupy unregulated characterises: slope angle and the classification of soils (subtype and race of chernozem and the difference in the degree of erosion), which determines the development of erosion processes and redistribution available to plants form of moisture. As a rule, the maximum yield of barley is marked on most flat areas covered with chernozem leached and typical with the full profile. The contain of nutrients usually takes 3-4 levels of limitation. The significance of a particular element is determined by the characteristics of the particular agro-ecological homogeneous area. Most, however, the value in the 2003 - 2004's. plants were available forms of phosphorus and potassium Thus, in terms of slope agricultural landscapes of the Kursk region, there is increased spatial varety of fertility and barley yields. This priority among the limiting factors are soils and agro-ecological conditions. Significant influence of agrochemical parameters are shown within the homogeneous agroecological regions. In this regard system of precision agriculture has a great prospects for acquiring practical, and must to imply the adaptation of existing agricultural technologies to change the conditions of cultivation of field crops within fields.

“Spatial Energetics”: Integrating Data From GPS, Accelerometry, and GIS to Address Obesity and Inactivity

PubMed Central

James, Peter; Jankowska, Marta; Marx, Christine; Hart, Jaime E.; Berrigan, David; Kerr, Jacqueline; Hurvitz, Philip M.; Hipp, J. Aaron; Laden, Francine

2016-01-01

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high–spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding, and suggests solutions to move this promising area of research forward. PMID:27528538

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

PubMed

Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

2014-03-20

Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

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.

2017-04-01

This paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set 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). We illustrate its operation and performance with a set of test cases including both synthetic and real integral-field spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of 2. Our analysis reveals that the algorithm prioritizes conservation of all the statistically significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a 'black box' to improve the signal-to-noise ratio, but as a new approach to characterize spatially resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

PubMed

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

From water use to water scarcity footprinting in environmentally extended input-output analysis.

PubMed

Ridoutt, Bradley George; Hadjikakou, Michalis; Nolan, Martin; Bryan, Brett A

2018-05-18

Environmentally extended input-output analysis (EEIOA) supports environmental policy by quantifying how demand for goods and services leads to resource use and emissions across the economy. However, some types of resource use and emissions require spatially-explicit impact assessment for meaningful interpretation, which is not possible in conventional EEIOA. For example, water use in locations of scarcity and abundance is not environmentally equivalent. Opportunities for spatially-explicit impact assessment in conventional EEIOA are limited because official input-output tables tend to be produced at the scale of political units which are not usually well aligned with environmentally relevant spatial units. In this study, spatially-explicit water scarcity factors and a spatially disaggregated Australian water use account were used to develop water scarcity extensions that were coupled with a multi-regional input-output model (MRIO). The results link demand for agricultural commodities to the problem of water scarcity in Australia and globally. Important differences were observed between the water use and water scarcity footprint results, as well as the relative importance of direct and indirect water use, with significant implications for sustainable production and consumption-related policies. The approach presented here is suggested as a feasible general approach for incorporating spatially-explicit impact assessment in EEIOA.

Heredity Factors in Spatial Visualization.

ERIC Educational Resources Information Center

Vandenberg, S. G.

Spatial visualization is not yet clearly understood. Some researchers have concluded that two factors or abilities are involved, spatial orientation and spatial visualization. Different definitions and different tests have been proposed for these two abilities. Several studies indicate that women generally perform more poorly on spatial tests than…

Mammogram registration using the Cauchy-Navier spline

NASA Astrophysics Data System (ADS)

Wirth, Michael A.; Choi, Christopher

2001-07-01

The process of comparative analysis involves inspecting mammograms for characteristic signs of potential cancer by comparing various analogous mammograms. Factors such as the deformable behavior of the breast, changes in breast positioning, and the amount/geometry of compression may contribute to spatial differences between corresponding structures in corresponding mammograms, thereby significantly complicating comparative analysis. Mammogram registration is a process whereby spatial differences between mammograms can be reduced. Presented in this paper is a nonrigid approach to matching corresponding mammograms based on a physical registration model. Many of the earliest approaches to mammogram registration used spatial transformations which were innately rigid or affine in nature. More recently algorithms have incorporated radial basis functions such as the Thin-Plate Spline to match mammograms. The approach presented here focuses on the use of the Cauchy-Navier Spline, a deformable registration model which offers approximate nonrigid registration. The utility of the Cauchy-Navier Spline is illustrated by matching both temporal and bilateral mammograms.

Measuring changes in chemistry, composition, and molecular structure within hair fibers by infrared and Raman spectroscopic imaging.

PubMed

Zhang, Guojin; Senak, Laurence; Moore, David J

2011-05-01

Spatially resolved infrared (IR) and Raman images are acquired from human hair cross sections or intact hair fibers. The full informational content of these spectra are spatially correlated to hair chemistry, anatomy, and structural organization through univariate and multivariate data analysis. Specific IR and Raman images from untreated human hair describing the spatial dependence of lipid and protein distribution, protein secondary structure, lipid chain conformational order, and distribution of disulfide cross-links in hair protein are presented in this study. Factor analysis of the image plane acquired with IR microscopy in hair sections, permits delineation of specific micro-regions within the hair. These data indicate that both IR and Raman imaging of molecular structural changes in a specific region of hair will prove to be valuable tools in the understanding of hair structure, physiology, and the effect of various stresses upon its integrity.

[Spatial distribution pattern and fractal analysis of Larix chinensis populations in Qinling Mountain].

PubMed

Guo, Hua; Wang, Xiaoan; Xiao, Yaping

2005-02-01

In this paper, the fractal characters of Larix chinensis populations in Qinling Mountain were studied by contiguous grid quadrate sampling method and by boxing-counting dimension and information dimension. The results showed that the high boxing-counting dimension (1.8087) and information dimension (1.7931) reflected a higher spatial occupational degree of L. chinensis populations. Judged by the dispersal index and Morisita's pattern index, L. chinensis populations clumped at three different age stages (0-25, 25-50 and over 50 years). From Greig-Smiths' mean variance analysis, the figure of pattern scale showed that L. chinensis populations clumped in 128 m2 and 512 m2, and the different age groups clumped in different scales. The pattern intensities decreased with increasing age, and tended to reduce with increasing area when detected by Kershaw's PI index. The spatial pattern characters of L. chinensis populations may be their responses to environmental factors.

Study of Earthquake Disaster Prediction System of Langfang city Based on GIS

NASA Astrophysics Data System (ADS)

Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei

2017-07-01

In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.

Bayesian spatio-temporal discard model in a demersal trawl fishery

NASA Astrophysics Data System (ADS)

Grazia Pennino, M.; Muñoz, Facundo; Conesa, David; López-Quílez, Antonio; Bellido, José M.

2014-07-01

Spatial management of discards has recently been proposed as a useful tool for the protection of juveniles, by reducing discard rates and can be used as a buffer against management errors and recruitment failure. In this study Bayesian hierarchical spatial models have been used to analyze about 440 trawl fishing operations of two different metiers, sampled between 2009 and 2012, in order to improve our understanding of factors that influence the quantity of discards and to identify their spatio-temporal distribution in the study area. Our analysis showed that the relative importance of each variable was different for each metier, with a few similarities. In particular, the random vessel effect and seasonal variability were identified as main driving variables for both metiers. Predictive maps of the abundance of discards and maps of the posterior mean of the spatial component show several hot spots with high discard concentration for each metier. We argue how the seasonal/spatial effects, and the knowledge about the factors influential to discarding, could potentially be exploited as potential mitigation measures for future fisheries management strategies. However, misidentification of hotspots and uncertain predictions can culminate in inappropriate mitigation practices which can sometimes be irreversible. The proposed Bayesian spatial method overcomes these issues, since it offers a unified approach which allows the incorporation of spatial random-effect terms, spatial correlation of the variables and the uncertainty of the parameters in the modeling process, resulting in a better quantification of the uncertainty and accurate predictions.

BDNF and TNF-α polymorphisms in memory.

PubMed

Yogeetha, B S; Haupt, L M; McKenzie, K; Sutherland, H G; Okolicsyani, R K; Lea, R A; Maher, B H; Chan, R C K; Shum, D H K; Griffiths, L R

2013-09-01

Here, we investigate the genetic basis of human memory in healthy individuals and the potential role of two polymorphisms, previously implicated in memory function. We have explored aspects of retrospective and prospective memory including semantic, short term, working and long-term memory in conjunction with brain derived neurotrophic factor (BDNF) and tumor necrosis factor-alpha (TNF-α). The memory scores for healthy individuals in the population were obtained for each memory type and the population was genotyped via restriction fragment length polymorphism for the BDNF rs6265 (Val66Met) SNP and via pyrosequencing for the TNF-α rs113325588 SNP. Using univariate ANOVA, a significant association of the BDNF polymorphism with visual and spatial memory retention and a significant association of the TNF-α polymorphism was observed with spatial memory retention. In addition, a significant interactive effect between BDNF and TNF-α polymorphisms was observed in spatial memory retention. In practice visual memory involves spatial information and the two memory systems work together, however our data demonstrate that individuals with the Val/Val BDNF genotype have poorer visual memory but higher spatial memory retention, indicating a level of interaction between TNF-α and BDNF in spatial memory retention. This is the first study to use genetic analysis to determine the interaction between BDNF and TNF-α in relation to memory in normal adults and provides important information regarding the effect of genetic determinants and gene interactions on human memory.

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

PubMed

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

2017-04-01

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

Detection Monitoring of Crown Condition in South Carolina: A Case Study

Treesearch

William A. Bechtold; John W. Coulston

2005-01-01

This article presents a case study of how indicators of forest health can be adjusted for natural factors, standardized to a common basis, and subjected to spatial analysis for the purpose of detecting potential problems related to forest health. Two of five Forest Inventory and Analysis inventory panels in South Carolina and surrounding States were completed in 2000...

Large-scale changes in network interactions as a physiological signature of spatial neglect.

PubMed

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L; Callejas, Alicia; Astafiev, Serguei V; Metcalf, Nicholas V; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z; Carter, Alex R; Shulman, Gordon L; Corbetta, Maurizio

2014-12-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n=84) heterogeneous sample of first-ever stroke patients (within 1-2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia

PubMed Central

Morgan, Geoffrey G.; Jalaludin, Bin B.; Bauman, Adrian E.

2018-01-01

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed “disease maps” for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial distress. PMID:29415461

Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia.

PubMed

Mayne, Darren J; Morgan, Geoffrey G; Jalaludin, Bin B; Bauman, Adrian E

2018-02-06

Walkability describes the capacity of the built environment to promote walking, and has been proposed as a potential focus for community-level mental health planning. We evaluated this possibility by examining the contribution of area-level walkability to variation in psychosocial distress in a population cohort at spatial scales comparable to those used for regional planning in Sydney, Australia. Data on psychosocial distress were analysed for 91,142 respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. We fit conditional auto regression models at the postal area level to obtain smoothed "disease maps" for psychosocial distress, and assess its association with area-level walkability after adjusting for individual- and area-level factors. Prevalence of psychosocial distress was 7.8%; similar for low (7.9%), low-medium (7.9%), medium-high (8.0%), and high (7.4%) walkability areas; and decreased with reducing postal area socioeconomic disadvantage: 12.2% (most), 9.3%, 7.5%, 5.9%, and 4.7% (least). Unadjusted disease maps indicated strong geographic clustering of psychosocial distress with 99.0% of excess prevalence due to unobserved and spatially structured factors, which was reduced to 55.3% in fully adjusted maps. Spatial and unstructured variance decreased by 97.3% and 39.8% after adjusting for individual-level factors, and another 2.3% and 4.2% with the inclusions of area-level factors. Excess prevalence of psychosocial distress in postal areas was attenuated in adjusted models but remained spatially structured. Postal area prevalence of high psychosocial distress is geographically clustered in Sydney, but is unrelated to postal area walkability. Area-level socioeconomic disadvantage makes a small contribution to this spatial structure; however, community-level mental health planning will likely deliver greatest benefits by focusing on individual-level contributors to disease burden and inequality associated with psychosocial distress.

Spatial Epidemiology of Plasmodium vivax, Afghanistan

PubMed Central

Leslie, Toby; Kolaczinski, Kate; Mohsen, Engineer; Mehboob, Najeebullah; Saleheen, Sarah; Khudonazarov, Juma; Freeman, Tim; Clements, Archie; Rowland, Mark; Kolaczinski, Jan

2006-01-01

Plasmodium vivax is endemic to many areas of Afghanistan. Geographic analysis helped highlight areas of malaria risk and clarified ecologic risk factors for transmission. Remote sensing enabled development of a risk map, thereby providing a valuable tool to help guide malaria control strategies. PMID:17176583

Nationwide analysis on the impact of socioeconomic land use factors and incidence of urothelial carcinoma.

PubMed

Brandt, Maximilian P; Gust, Kilian M; Mani, Jens; Vallo, Stefan; Höfner, Thomas; Borgmann, Hendrik; Tsaur, Igor; Thomas, Christian; Haferkamp, Axel; Herrmann, Eva; Bartsch, Georg

2018-02-01

Incidence rates for urothelial carcinoma (UC) have been reported to differ between countries within the European Union (EU). Besides occupational exposure to chemicals, other substances such as tobacco and nitrite in groundwater have been identified as risk factors for UC. We investigated if regional differences in UC incidence rates are associated with agricultural, industrial and residential land use. Newly diagnosed cases of UC between 2003 and 2010 were included. Information within 364 administrative districts of Germany from 2004 for land use factors were obtained and calculated as a proportion of the total area of the respective administrative district and as a smoothed proportion. Furthermore, information on smoking habits was included in our analysis. Kulldorff spatial clustering was used to detect different clusters. A negative binomial model was used to test the spatial association between UC incidence as a ratio of observed versus expected incidence rates, land use and smoking habits. We identified 437,847,834 person years with 171,086 cases of UC. Cluster analysis revealed areas with higher incidence of UC than others (p=0.0002). Multivariate analysis including significant pairwise interactions showed that the environmental factors were independently associated with UC (p<0.001). The RR was 1.066 (95% CI 1.052-1.080), 1.066 (95% CI 1.042-1.089) and 1.067 (95% CI 1.045-1.093) for agricultural, industrial and residential areas, respectively, and 0.996 (95% CI 0.869-0.999) for the proportion of never smokers. This study displays regional differences in incidence of UC in Germany. Additionally, results suggest that socioeconomic factors based on agricultural, industrial and residential land use may be associated with UC incidence rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach.

PubMed

Domnich, Alexander; Arata, Lucia; Amicizia, Daniela; Signori, Alessio; Gasparini, Roberto; Panatto, Donatella

2016-11-16

Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km2, and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita (I=0.082) and per km2 (I=0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population, mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km2. The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.

An innovative expression model of human health risk based on the quantitative analysis of soil metals sources contribution in different spatial scales.

PubMed

Zhang, Yimei; Li, Shuai; Wang, Fei; Chen, Zhuang; Chen, Jie; Wang, Liqun

2018-09-01

Toxicity of heavy metals from industrialization poses critical concern, and analysis of sources associated with potential human health risks is of unique significance. Assessing human health risk of pollution sources (factored health risk) concurrently in the whole and the sub region can provide more instructive information to protect specific potential victims. In this research, we establish a new expression model of human health risk based on quantitative analysis of sources contribution in different spatial scales. The larger scale grids and their spatial codes are used to initially identify the level of pollution risk, the type of pollution source and the sensitive population at high risk. The smaller scale grids and their spatial codes are used to identify the contribution of various sources of pollution to each sub region (larger grid) and to assess the health risks posed by each source for each sub region. The results of case study show that, for children (sensitive populations, taking school and residential area as major region of activity), the major pollution source is from the abandoned lead-acid battery plant (ALP), traffic emission and agricultural activity. The new models and results of this research present effective spatial information and useful model for quantifying the hazards of source categories and human health a t complex industrial system in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2013-08-01

Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

Experiments to Evaluate and Implement Passive Tracer Gas Methods to Measure Ventilation Rates in Homes

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

Lunden, Melissa; Faulkner, David; Heredia, Elizabeth

2012-10-01

This report documents experiments performed in three homes to assess the methodology used to determine air exchange rates using passive tracer techniques. The experiments used four different tracer gases emitted simultaneously but implemented with different spatial coverage in the home. Two different tracer gas sampling methods were used. The results characterize the factors of the execution and analysis of the passive tracer technique that affect the uncertainty in the calculated air exchange rates. These factors include uncertainties in tracer gas emission rates, differences in measured concentrations for different tracer gases, temporal and spatial variability of the concentrations, the comparison betweenmore » different gas sampling methods, and the effect of different ventilation conditions.« less

Different Dimensions of Cognitive Style in Typical and Atypical Cognition: New Evidence and a New Measurement Tool.

PubMed

Mealor, Andy D; Simner, Julia; Rothen, Nicolas; Carmichael, Duncan A; Ward, Jamie

2016-01-01

We developed the Sussex Cognitive Styles Questionnaire (SCSQ) to investigate visual and verbal processing preferences and incorporate global/local processing orientations and systemising into a single, comprehensive measure. In Study 1 (N = 1542), factor analysis revealed six reliable subscales to the final 60 item questionnaire: Imagery Ability (relating to the use of visual mental imagery in everyday life); Technical/Spatial (relating to spatial mental imagery, and numerical and technical cognition); Language & Word Forms; Need for Organisation; Global Bias; and Systemising Tendency. Thus, we replicate previous findings that visual and verbal styles are separable, and that types of imagery can be subdivided. We extend previous research by showing that spatial imagery clusters with other abstract cognitive skills, and demonstrate that global/local bias can be separated from systemising. Study 2 validated the Technical/Spatial and Language & Word Forms factors by showing that they affect performance on memory tasks. In Study 3, we validated Imagery Ability, Technical/Spatial, Language & Word Forms, Global Bias, and Systemising Tendency by issuing the SCSQ to a sample of synaesthetes (N = 121) who report atypical cognitive profiles on these subscales. Thus, the SCSQ consolidates research from traditionally disparate areas of cognitive science into a comprehensive cognitive style measure, which can be used in the general population, and special populations.

Different Dimensions of Cognitive Style in Typical and Atypical Cognition: New Evidence and a New Measurement Tool

PubMed Central

Mealor, Andy D.; Simner, Julia; Rothen, Nicolas; Carmichael, Duncan A.; Ward, Jamie

2016-01-01

We developed the Sussex Cognitive Styles Questionnaire (SCSQ) to investigate visual and verbal processing preferences and incorporate global/local processing orientations and systemising into a single, comprehensive measure. In Study 1 (N = 1542), factor analysis revealed six reliable subscales to the final 60 item questionnaire: Imagery Ability (relating to the use of visual mental imagery in everyday life); Technical/Spatial (relating to spatial mental imagery, and numerical and technical cognition); Language & Word Forms; Need for Organisation; Global Bias; and Systemising Tendency. Thus, we replicate previous findings that visual and verbal styles are separable, and that types of imagery can be subdivided. We extend previous research by showing that spatial imagery clusters with other abstract cognitive skills, and demonstrate that global/local bias can be separated from systemising. Study 2 validated the Technical/Spatial and Language & Word Forms factors by showing that they affect performance on memory tasks. In Study 3, we validated Imagery Ability, Technical/Spatial, Language & Word Forms, Global Bias, and Systemising Tendency by issuing the SCSQ to a sample of synaesthetes (N = 121) who report atypical cognitive profiles on these subscales. Thus, the SCSQ consolidates research from traditionally disparate areas of cognitive science into a comprehensive cognitive style measure, which can be used in the general population, and special populations. PMID:27191169

Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities.

PubMed

Pu, Haixia; Luo, Kunli; Wang, Pin; Wang, Shaobin; Kang, Shun

2017-02-01

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai-Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing > building area > coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Shanghai: a study on the spatial growth of population and economy in a Chinese metropolitan area.

PubMed

Zhu, J

1995-01-01

In this study of the growth in population and industry in Shanghai, China, between the 1982 and 1990 censuses, data on administrative divisions was normalized through digitization and spatial analysis. Analysis focused on spatial units, intensity of growth, time period, distance, rate of growth, and direction of spatial growth. The trisection method divided the city into city proper, outskirts, and suburbs. The distance function method considered the distance from center city as a function: exponential, power, trigonometric, logarithmic, and polynomial. Population growth and employment in all sectors increased in the outskirts and suburbs and decreased in the city proper except tertiary sectors. Primary sector employment decreased in all three sections. Employment in the secondary increased faster in the outskirts and suburbs than the total rate of growth of population and employment. In the city secondary sector employment rates decreased faster than total population and employment rates. The tertiary sector had the highest rate of growth in all sections, and employment grew faster than secondary sector rates. Tertiary growth was highest in real estate, finance, and insurance. Industrial growth in the secondary sector was 160.2% in the suburbs, 156.6% in the outskirts, and 80.9% in the city. In the distance function analysis, industry expanded further out than the entire secondary sector. Commerce grew the fastest in areas 15.4 km from center city. Economic growth was faster after economic reforms in 1978. Growth was led by industry and followed by the secondary sector, the tertiary sector, and population. Industrial expansion resulted from inner pressure, political factors controlling size, the social and economic system, and the housing construction and distribution system. Initially sociopsychological factors affected urban concentration.

Multiscale analysis of the spatial variability of heavy metals and organic matter in soils and groundwater across Spain

NASA Astrophysics Data System (ADS)

Luque-Espinar, J. A.; Pardo-Igúzquiza, E.; Grima-Olmedo, J.; Grima-Olmedo, C.

2018-06-01

During the last years there has been an increasing interest in assessing health risks caused by exposure to contaminants found in soil, air, and water, like heavy metals or emerging contaminants. This work presents a study on the spatial patterns and interaction effects among relevant heavy metals (Sb, As and Pb) that may occur together in different minerals. Total organic carbon (TOC) have been analyzed too because it is an essential component in the regulatory mechanisms that control the amount of metal in soils. Even more, exposure to these elements is associated with a number of diseases and environmental problems. These metals can have both natural and anthropogenic origins. A key component of any exposure study is a reliable model of the spatial distribution the elements studied. A geostatistical analysis have been performed in order to show that selected metals are auto-correlated and cross-correlated and type and magnitude of such cross-correlation varies depending on the spatial scale under consideration. After identifying general trends, we analyzed the residues left after subtracting the trend from the raw variables. Three scales of variability were identified (compounds or factors) with scales of 5, 35 and 135 km. The first factor (F1) basically identifies anomalies of natural origin but, in some places, of anthropogenics origin as well. The other two are related to geology (F2 and F3) although F3 represents more clearly geochemical background related to large lithological groups. Likewise, mapping of two major structures indicates that significant faults have influence on the distribution of the studied elements. Finally, influence of soil and lithology on groundwater by means of contingency analysis was assessed.

European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

NASA Astrophysics Data System (ADS)

Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel

2017-10-01

This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.

Identification of the key ecological factors influencing vegetation degradation in semi-arid agro-pastoral ecotone considering spatial scales

NASA Astrophysics Data System (ADS)

Peng, Yu; Wang, Qinghui; Fan, Min

2017-11-01

When assessing re-vegetation project performance and optimizing land management, identification of the key ecological factors inducing vegetation degradation has crucial implications. Rainfall, temperature, elevation, slope, aspect, land use type, and human disturbance are ecological factors affecting the status of vegetation index. However, at different spatial scales, the key factors may vary. Using Helin County, Inner-Mongolia, China as the study site and combining remote sensing image interpretation, field surveying, and mathematical methods, this study assesses key ecological factors affecting vegetation degradation under different spatial scales in a semi-arid agro-pastoral ecotone. It indicates that the key factors are different at various spatial scales. Elevation, rainfall, and temperature are identified as crucial for all spatial extents. Elevation, rainfall and human disturbance are key factors for small-scale quadrats of 300 m × 300 m and 600 m × 600 m, temperature and land use type are key factors for a medium-scale quadrat of 1 km × 1 km, and rainfall, temperature, and land use are key factors for large-scale quadrats of 2 km × 2 km and 5 km × 5 km. For this region, human disturbance is not the key factor for vegetation degradation across spatial scales. It is necessary to consider spatial scale for the identification of key factors determining vegetation characteristics. The eco-restoration programs at various spatial scales should identify key influencing factors according their scales so as to take effective measurements. The new understanding obtained in this study may help to explore the forces which driving vegetation degradation in the degraded regions in the world.

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

PubMed Central

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

2015-01-01

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

Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification.

PubMed

Nyandwi, E; Veldkamp, A; Amer, S; Karema, C; Umulisa, I

2017-10-25

Schistosomiasis mansoni constitutes a significant public health problem in Rwanda. The nationwide prevalence mapping conducted in 2007-2008 revealed that prevalence per district ranges from 0 to 69.5% among school children. In response, mass drug administration campaigns were initiated. However, a few years later some additional small-scale studies revealed the existence of areas of high transmission in districts formerly classified as low endemic suggesting the need for a more accurate methodology for identification of hotspots. This study investigated if confirmed cases of schistosomiasis recorded at health facility level can be used to, next to existing prevalence data, detect geographically more accurate hotspots of the disease and its associated risk factors. A GIS-based spatial and statistical analysis was carried out. Confirmed cases, recorded at primary health facilities level, were combined with demographic data to calculate incidence rates for each of 367 health facility service area. Empirical Bayesian smoothing was used to deal with rate instability. Incidence rates were compared with prevalence data to identify their level of agreement. Spatial autocorrelation of the incidence rates was analyzed using Moran's Index, to check if spatial clustering occurs. Finally, the spatial relationship between schistosomiasis distribution and potential risk factors was assessed using multiple regression. Incidence rates for 2007-2008 were highly correlated with prevalence values (R 2  = 0.79), indicating that in the case of Rwanda incidence data can be used as a proxy for prevalence data. We observed a focal distribution of schistosomiasis with a significant spatial autocorrelation (Moran's I > 0: 0,05-0.20 and p ≤ 0,05), indicating the occurrence of hotspots. Regarding risk factors, it was identified that the spatial pattern of schistosomiasis is significantly associated with wetland conditions and rice cultivation. In Rwanda the high density of health facilities and the standardized microscopic laboratory diagnostic allow the derived data to be used to complement prevalence studies to identify hotspots of schistosomiasis and its associated risk factors. This type of information, in turn, can support disease control interventions and monitoring.

Spatial variations of bacterial community and its relationship with water chemistry in Sanya Bay, South China Sea as determined by DGGE fingerprinting and multivariate analysis.

PubMed

Ling, Juan; Zhang, Yan-Ying; Dong, Jun-De; Wang, You-Shao; Feng, Jing-Bing; Zhou, Wei-Hua

2015-10-01

Bacteria play important roles in the structure and function of marine food webs by utilizing nutrients and degrading the pollutants, and their distribution are determined by surrounding water chemistry to a certain extent. It is vital to investigate the bacterial community's structure and identifying the significant factors by controlling the bacterial distribution in the paper. Flow cytometry showed that the total bacterial abundance ranged from 5.27 × 10(5) to 3.77 × 10(6) cells/mL. Molecular fingerprinting technique, denaturing gradient gel electrophoresis (DGGE) followed by DNA sequencing has been employed to investigate the bacterial community composition. The results were then interpreted through multivariate statistical analysis and tended to explain its relationship to the environmental factors. A total of 270 bands at 83 different positions were detected in DGGE profiles and 29 distinct DGGE bands were sequenced. The predominant bacteria were related to Phyla Protebacteria species (31 %, nine sequences), Cyanobacteria (37.9 %, eleven sequences) and Actinobacteria (17.2 %, five sequences). Other phylogenetic groups identified including Firmicutes (6.9 %, two sequences), Bacteroidetes (3.5 %, one sequences) and Verrucomicrobia (3.5 %, one sequences). Conical correspondence analysis was used to elucidate the relationships between the bacterial community compositions and environmental factors. The results showed that the spatial variations in the bacterial community composition was significantly related to phosphate (P = 0.002, P < 0.01), dissolved organic carbon (P = 0.004, P < 0.01), chemical oxygen demand (P = 0.010, P < 0.05) and nitrite (P = 0.016, P < 0.05). This study revealed the spatial variations of bacterial community and significant environmental factors driving the bacterial composition shift. These results may be valuable for further investigation on the functional microbial structure and expression quantitatively under the polluted environments in the world.

Morphometric Analysis to Prioritize Sub-Watershed for Flood Risk Assessment in Central Karakoram National Park Using Gis/rs Approach

NASA Astrophysics Data System (ADS)

Syed, N. H.; Rehman, A. A.; Hussain, D.; Ishaq, S.; Khan, A. A.

2017-11-01

Morphometric analysis is vital for any watershed investigation and it is inevitable for flood risk assessment in sub-watershed basins. Present study undertaken to carry out critical evaluation and assessment of sub watershed morphological parameters for flood risk assessment of Central Karakorum National Park (CKNP), where Geographical information system and remote sensing (GIS & RS) approach used for quantifying the parameter and mapping of sub watershed units. ASTER DEM used as a geo-spatial data for watershed delineation and stream network. Morphometric analysis carried out using spatial analyst tool of ArcGIS 10.2. The parameters included were bifurcation ratio (Rb), Drainage Texture (Rt), Circulatory ratio (Rc), Elongated ratio (Re), Drainage density (Dd), Stream Length (Lu), Stream order (Su), Slope and Basin length (Lb) have calculated separately. The analysis revealed that the stream order varies from order 1 to 6 and the total numbers of stream segments of all orders were 52. Multi criteria analysis process used to calculate the risk factor. As an accomplished result, map of sub watershed prioritization developed using weighted standardized risk factor. These results helped to understand sensitivity of flush floods in different sub watersheds of the study area and leaded to better management of the mountainous regions in prospect of flush floods.

The effects of context on multidimensional spatial cognitive models. Ph.D. Thesis - Arizona Univ.

NASA Technical Reports Server (NTRS)

Dupnick, E. G.

1979-01-01

Spatial cognitive models obtained by multidimensional scaling represent cognitive structure by defining alternatives as points in a coordinate space based on relevant dimensions such that interstimulus dissimilarities perceived by the individual correspond to distances between the respective alternatives. The dependence of spatial models on the context of the judgments required of the individual was investigated. Context, which is defined as a perceptual interpretation and cognitive understanding of a judgment situation, was analyzed and classified with respect to five characteristics: physical environment, social environment, task definition, individual perspective, and temporal setting. Four experiments designed to produce changes in the characteristics of context and to test the effects of these changes upon individual cognitive spaces are described with focus on experiment design, objectives, statistical analysis, results, and conclusions. The hypothesis is advanced that an individual can be characterized as having a master cognitive space for a set of alternatives. When the context changes, the individual appears to change the dimension weights to give a new spatial configuration. Factor analysis was used in the interpretation and labeling of cognitive space dimensions.

Forest Fires and Post - Fire Regeneration in Algeria Analysis with Satellite Data

NASA Astrophysics Data System (ADS)

Zegrar, Ahmed

2016-07-01

The Algerian forests are characterized by a particularly flammable material and fuel. The wind, the relief and the slope facilitates the propagation of fire. The use of remote sensing data multi-­dates, combined with other types of data of various kinds on the environment and forest burned, opens up interesting perspectives for the management of post-­fire regeneration. In this study the use of multi-­temporal remote sensing image Alsat-­1 and Landsat combined with other types of data concerning both background and burned down forest appears to be promising in evaluating and spatial and temporal effects of post fire regeneration. A spatial analysis taking into consideration the characteristics of the burned down site in the North West of Algeria, allowed to better account new factors to explain the regeneration and its temporal and spatial variation. We intended to show the potential use of remote sensing data from satellite ALSAT-­1, of spatial resolution of 32 m. . This approach allows showing the contribution of the data of Algerian satellite ALSAT in the detection and the well attended some forest fires in Algeria.

Truck crash severity in New York city: An investigation of the spatial and the time of day effects.

PubMed

Zou, Wei; Wang, Xiaokun; Zhang, Dapeng

2017-02-01

This paper investigates the differences between single-vehicle and multi-vehicle truck crashes in New York City. The random parameter models take into account the time of day effect, the heterogeneous truck weight effect and other influencing factors such as crash characteristics, driver and vehicle characteristics, built environment factors and traffic volume attributes. Based on the results from the co-location quotient analysis, a spatial generalized ordered probit model is further developed to investigate the potential spatial dependency among single-vehicle truck crashes. The sample is drawn from the state maintained incident data, the publicly available Smart Location Data, and the BEST Practices Model (BPM) data from 2008 to 2012. The result shows that there exists a substantial difference between factors influencing single-vehicle and multi-vehicle truck crash severity. It also suggests that heterogeneity does exist in the truck weight, and it behaves differently in single-vehicle and multi-vehicle truck crashes. Furthermore, individual truck crashes are proved to be spatially dependent events for both single and multi-vehicle crashes. Last but not least, significant time of day effects were found for PM and night time slots, crashes that occurred in the afternoons and at nights were less severe in single-vehicle crashes, but more severe in multi-vehicle crashes. Copyright © 2016. Published by Elsevier Ltd.

Groundwater productivity potential mapping using evidential belief function.

PubMed

Park, Inhye; Kim, Yongsung; Lee, Saro

2014-09-01

The evidential belief function (EBF) model was applied and validated for analysis of groundwater-productivity potential (GPP) in Boryeong and Pohang cities, agriculture region in Korea using geographic information systems (GIS). Data about related factors, including topography, lineament, geology, forest, soil, and groundwater data were collected and input into a spatial database. Additionally, in the Boryeong area, specific capacity (SPC) data not lower than 4.55 m3 /d/m were collected, corresponding to 300 m3 /d yield from 72 well locations. In the Pohang area, SPC data of ≥ 6.25 m3 /d/m were collected, corresponding to a yield of 500 m3 /d from 44 well locations. By using the constructed spatial database, 19 factors related to groundwater productivity were extracted. The relationships between the well locations and the factors were identified and quantified by using the EBF model. Four relationships were calculated: belief (Bel), disbelief (Dis), uncertainty (Unc), and plausibility (Pls). The relationships were used as factor ratings in the overlay analysis to create GPP indices and maps. The resulting GPP maps showed 83.41% and 77.53% accuracy in Boryeong and Pohang areas, respectively. The EBF model was found to be more effective in terms of prediction accuracy. © 2014, National Ground Water Association.

Imputational Modeling of Spatial Context and Social Environmental Predictors of Walking in an Underserved Community: The PATH Trial

PubMed Central

Ellerbe, Caitlyn; Lawson, Andrew B.; Alia, Kassandra A.; Meyers, Duncan C.; Coulon, Sandra M.; Lawman, Hannah G.

2013-01-01

Background This study examined imputational modeling effects of spatial proximity and social factors of walking in African American adults. Purpose Models were compared that examined relationships between household proximity to a walking trail and social factors in determining walking status. Methods Participants (N=133; 66% female; mean age=55 yrs) were recruited to a police-supported walking and social marketing intervention. Bayesian modeling was used to identify predictors of walking at 12 months. Results Sensitivity analysis using different imputation approaches, and spatial contextual effects, were compared. All the imputation methods showed social life and income were significant predictors of walking, however, the complete data approach was the best model indicating Age (1.04, 95% OR: 1.00, 1.08), Social Life (0.83, 95% OR: 0.69, 0.98) and Income > $10,000 (0.10, 95% OR: 0.01, 0.97) were all predictors of walking. Conclusions The complete data approach was the best model of predictors of walking in African Americans. PMID:23481250

Imputational modeling of spatial context and social environmental predictors of walking in an underserved community: the PATH trial.

PubMed

Wilson, Dawn K; Ellerbe, Caitlyn; Lawson, Andrew B; Alia, Kassandra A; Meyers, Duncan C; Coulon, Sandra M; Lawman, Hannah G

2013-03-01

This study examined imputational modeling effects of spatial proximity and social factors of walking in African American adults. Models were compared that examined relationships between household proximity to a walking trail and social factors in determining walking status. Participants (N=133; 66% female; mean age=55 years) were recruited to a police-supported walking and social marketing intervention. Bayesian modeling was used to identify predictors of walking at 12 months. Sensitivity analysis using different imputation approaches, and spatial contextual effects, were compared. All the imputation methods showed social life and income were significant predictors of walking, however, the complete data approach was the best model indicating Age (1.04, 95% OR: 1.00, 1.08), Social Life (0.83, 95% OR: 0.69, 0.98) and Income <$10,000 (0.10, 95% OR: 0.01, 0.97) were all predictors of walking. The complete data approach was the best model of predictors of walking in African Americans. Copyright © 2012 Elsevier Ltd. All rights reserved.

Spatial Analysis of Ambient PM2.5 Exposure and Bladder Cancer Mortality in Taiwan

PubMed Central

Yeh, Hsin-Ling; Hsu, Shang-Wei; Chang, Yu-Chia; Chan, Ta-Chien; Tsou, Hui-Chen; Chang, Yen-Chen; Chiang, Po-Huang

2017-01-01

Fine particulate matter (PM2.5) is an air pollutant that is receiving intense regulatory attention in Taiwan. In previous studies, the effect of air pollution on bladder cancer has been explored. This study was conducted to elucidate the effect of atmospheric PM2.5 and other local risk factors on bladder cancer mortality based on available 13-year mortality data. Geographically weighted regression (GWR) was applied to estimate and interpret the spatial variability of the relationships between bladder cancer mortality and ambient PM2.5 concentrations, and other variables were covariates used to adjust for the effect of PM2.5. After applying a GWR model, the concentration of ambient PM2.5 showed a positive correlation with bladder cancer mortality in males in northern Taiwan and females in most of the townships in Taiwan. This is the first time PM2.5 has been identified as a risk factor for bladder cancer based on the statistical evidence provided by GWR analysis. PMID:28489042

Road infrastructure, spatial spillover and county economic growth

NASA Astrophysics Data System (ADS)

Hu, Zhenhua; Luo, Shuang

2017-09-01

This paper analyzes the spatial spillover effect of road infrastructure on the economic growth of poverty-stricken counties, based on the spatial Durbin model, by using the panel data of 37 poor counties in Hunan province from 2006 to 2015. The results showed that there is a significant spatial dependence of economic growth in Poor Counties. Road infrastructure has a positive impact on economic growth, and the results will be overestimated without considering spatial factors. Considering the spatial factors, the road infrastructure will promote the economic growth of the surrounding areas through the spillover effect, but the spillover effect is restricted by the distance factor. Capital investment is the biggest factor of economic growth in poor counties, followed by urbanization, labor force and regional openness.

The application of Global Sensitivity Analysis to quantify the dominant input factors for hydraulic model simulations

NASA Astrophysics Data System (ADS)

Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2015-04-01

Predicting flood inundation extents using hydraulic models is subject to a number of critical uncertainties. For a specific event, these uncertainties are known to have a large influence on model outputs and any subsequent analyses made by risk managers. Hydraulic modellers often approach such problems by applying uncertainty analysis techniques such as the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. However, these methods do not allow one to attribute which source of uncertainty has the most influence on the various model outputs that inform flood risk decision making. Another issue facing modellers is the amount of computational resource that is available to spend on modelling flood inundations that are 'fit for purpose' to the modelling objectives. Therefore a balance needs to be struck between computation time, realism and spatial resolution, and effectively characterising the uncertainty spread of predictions (for example from boundary conditions and model parameterisations). However, it is not fully understood how much of an impact each factor has on model performance, for example how much influence changing the spatial resolution of a model has on inundation predictions in comparison to other uncertainties inherent in the modelling process. Furthermore, when resampling fine scale topographic data in the form of a Digital Elevation Model (DEM) to coarser resolutions, there are a number of possible coarser DEMs that can be produced. Deciding which DEM is then chosen to represent the surface elevations in the model could also influence model performance. In this study we model a flood event using the hydraulic model LISFLOOD-FP and apply Sobol' Sensitivity Analysis to estimate which input factor, among the uncertainty in model boundary conditions, uncertain model parameters, the spatial resolution of the DEM and the choice of resampled DEM, have the most influence on a range of model outputs. These outputs include whole domain maximum inundation indicators and flood wave travel time in addition to temporally and spatially variable indicators. This enables us to assess whether the sensitivity of the model to various input factors is stationary in both time and space. Furthermore, competing models are assessed against observations of water depths from a historical flood event. Consequently we are able to determine which of the input factors has the most influence on model performance. Initial findings suggest the sensitivity of the model to different input factors varies depending on the type of model output assessed and at what stage during the flood hydrograph the model output is assessed. We have also found that initial decisions regarding the characterisation of the input factors, for example defining the upper and lower bounds of the parameter sample space, can be significant in influencing the implied sensitivities.

[Ecological vulnerability of coal mining area: a case study of Shengli Coalfield in Xilinguole of Inner Mongolia, China].

PubMed

Quan, Zhan-Jun; Li, Yuan; Li, Jun-Sheng; Han, Yu; Xiao, Neng-Wen; Fu, Meng-Di

2013-06-01

In this paper, an ecological vulnerability evaluation index system for the Shengli Coalfield in Xilinguole of Inner Mongolia was established, which included 16 factors in ecological sensitivity, natural and social pressure, and ecological recovery capacity, respectively. Based on the expert scoring method and analytic hierarchy process (AHP), an ecological vulnerability model was built for the calculation of the regional ecological vulnerability by means of RS and GIS spatial analysis. An analysis of the relationships between land use and ecological vulnerability was also made, and the results were tested by spatial auto-correlation analysis. Overall, the ecological vulnerability of the study area was at medium-high level. The exploitation of four opencast areas in the Coalfield caused a significant increase of ecological vulnerability. Moreover, due to the effects of mine drained water and human activities, the 300 -2000 m around the opencast areas was turning into higher ecologically fragile area. With further exploitation, the whole Coalfield was evolved into moderate and heavy ecological vulnerability area, and the coal resources mining was a key factor in this process. The cluster analysis showed that the spatial distribution of the ecological vulnerability in the study area had reasonable clustering characteristics. To decrease the population density, control the grazing capacity of grassland, and regulate the ratios of construction land and cultivated land could be the optimal ways for resolving the natural and social pressure, and to increase the investment and improve the vegetation recovery coefficient could be the fundamental measures for decreasing the ecological vulnerability of the study area.

Relative impacts of the fragmentation and spatial structure of habitats on freshwater fish distributions: application on French watersheds (Invited)

NASA Astrophysics Data System (ADS)

Le Pichon, C.; Belliard, J.; Talès, E.; Gorges, G.; Clément, F.

2009-12-01

Most of the rivers of the Ile de France region, intimately linked with the megalopolis of Paris, are severely altered and freshwater fishes are exposed to habitat alteration, reduced connectivity and pollution. Several species thus present fragmented distributions and decreasing densities. In this context, the European Water Framework Directive (2000) has goals of hydrosystems rehabilitation and no further damage. In particular, the preservation and restoration of ecological connectivity of river networks is a key element for fish populations. These goals require the identification of natural and anthropological factors which influence the spatial distribution of species. We have proposed a riverscape approach, based on landscape ecology concepts, combined with a set of spatial analysis methods to assess the multiscale relationships between the spatial pattern of fish habitats and processes depending on fish movements. In particular, we used this approach to test the relative roles of spatial arrangement of fish habitats and the presence of physical barriers in explaining fish spatial distributions in a small rural watershed (106 km2). We performed a spatially continuous analysis of fish-habitat relationships. Fish habitats and physical barriers were mapped along the river network (33 km) with a GPS and imported into a GIS. In parallel, a longitudinal electrofishing survey of the distribution and abundance of fishes was made using a point abundance sampling scheme. Longitudinal arrangement of fish habitats were evaluated using spatial analysis methods: patch/distance metrics and moving window analysis. Explanatory models were developed to test the relative contribution of local environmental variables and spatial context in explaining fish presence. We have recorded about 100 physical barriers, on average one every 330 meters; most artificial barriers were road pipe culverts, falls associated with ponds and sluice gates. Contrasted fish communities and densities were observed in the different areas of the watershed, related to various land use (riparian forest or agriculture). The first results of fish-habitat association analysis on a 5 km stream are that longitudinal distribution of fish species was mainly impacted by falls associated with ponds. The impact was both due to the barrier effect and to the modification of aquatic habitats. Abundance distribution of Salmo trutta and Cottus gobio was particularly affected. Spatially continuous analysis of fish-habitat relationships allowed us to identify the relative impacts of habitat alteration and presence of physical barriers to fish movements. These techniques could help prioritize preservation and restoration policies in human-impacted watersheds, in particular, identifying the key physical barriers to remove.

The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health

PubMed Central

2011-01-01

Background Many Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale. At the same time, the conceptualization of place at the local scale is one of the weakest theoretical aspects in health geography. The modifiable areal unit problem (MAUP) raises issues when census tracts are used as neighbourhood proxies, and no other alternate spatial structure is used for sensitivity analysis. In the literature, conclusions on the relationship between NO2 and health outcomes are divided, and this situation may in part be due to the selection of an inappropriate spatial structure for analysis. Here, we undertake an analysis of NO2 and respiratory health in Ottawa, Canada using three different spatial structures in order to elucidate the effects that the spatial unit of analysis can have on analytical results. Results Using three different spatial structures to examine and quantify the relationship between NO2 and respiratory morbidity, we offer three main conclusions: 1) exploratory spatial analytical methods can serve as an indication of the potential effect of the MAUP; 2) OLS regression results differ significantly using different spatial representations, and this could be a contributing factor to the lack of consensus in studies that focus on the relation between NO2 and respiratory health at the area-level; and 3) the use of three spatial representations confirms no measured effect of NO2 exposure on respiratory health in Ottawa. Conclusions Area units used in population health studies should be delineated so as to represent the a priori scale of the expected scale interaction between neighbourhood processes and health. A thorough understanding of the role of the MAUP in the study of the relationship between NO2 and respiratory health is necessary for research into disease pathways based on statistical models, and for decision-makers to assess the scale at which interventions will have maximum benefit. In general, more research on the role of spatial representation in health studies is needed. PMID:22040001

City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France.

PubMed

Padilla, Cindy M; Kihal-Talantikit, Wahida; Vieira, Verónica M; Deguen, Séverine

2016-06-22

Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease-a leading cause of infant mortality.

City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France

PubMed Central

Padilla, Cindy M.; Kihal-Talantikit, Wahida; Vieira, Verónica M.; Deguen, Séverine

2016-01-01

Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease—a leading cause of infant mortality. PMID:27338439

Environmental and Risk Factors of Leptospirosis: A Spatial Analysis in Semarang City

NASA Astrophysics Data System (ADS)

Nur Fajriyah, Silviana; Udiyono, Ari; Saraswati, Lintang Dian

2017-02-01

Leptospirosis is zoonotic potentially epidemic with clinical manifestations from mild to severe and can cause death. The incidence of leptospirosis in Indonesia tends to increase by the year. The case fatality rate in Semarang was greater than the national’s (9.38%). The purpose of this study was to describe the environmental risk factors of leptospirosis in Semarang spatially. The study design was descriptive observational with cross sectional approach. The population and samples in this study were confirmed leptospirosis in Semarang from January 2014 until May 2015, 88 respondents in 61 villages of 15 sub-districts in Semarang. The variables were environmental conditions, the presence of rats, wastewater disposal, waste disposal facilities, the presence of pets, the presence of rivers, flood’s profile, tidal inundation profile, vegetation, contact with rats, and Protected Personal Equipment/PPE utilization. Based on the spatial analysis, variables that found in the big half area of Semarang are environmental conditions, the presence of rats, wastewater disposal, waste disposal facilities, contact with rats, and PPE utilization. The presence of pets at risk, the presence of rivers, flood’s profile, inundation profile, and vegetation were found only in small half of Semarang area. People are expected to maintain their personal and environmental hygiene to prevent the transmission.

Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment

PubMed Central

Guo, Xueru; Zuo, Rui; Meng, Li; Wang, Jinsheng; Teng, Yanguo; Liu, Xin; Chen, Minhua

2018-01-01

Globally, groundwater resources are being deteriorated by rapid social development. Thus, there is an urgent need to assess the combined impacts of natural and enhanced anthropogenic sources on groundwater chemistry. The aim of this study was to identify seasonal characteristics and spatial variations in anthropogenic and natural effects, to improve the understanding of major hydrogeochemical processes based on source apportionment. 34 groundwater points located in a riverside groundwater resource area in northeast China were sampled during the wet and dry seasons in 2015. Using principal component analysis and factor analysis, 4 principal components (PCs) were extracted from 16 groundwater parameters. Three of the PCs were water-rock interaction (PC1), geogenic Fe and Mn (PC2), and agricultural pollution (PC3). A remarkable difference (PC4) was organic pollution originating from negative anthropogenic effects during the wet season, and geogenic F enrichment during the dry season. Groundwater exploitation resulted in dramatic depression cone with higher hydraulic gradient around the water source area. It not only intensified dissolution of calcite, dolomite, gypsum, Fe, Mn and fluorine minerals, but also induced more surface water recharge for the water source area. The spatial distribution of the PCs also suggested the center of the study area was extremely vulnerable to contamination by Fe, Mn, COD, and F−. PMID:29415516

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.

Nonideal transport of reactive solutes in heterogeneous porous media: 4. Analysis of the Cape Cod Natural‐Gradient Field Experiment

USGS Publications Warehouse

Brusseau, Mark L.; Srivastava, Rajesh

1999-01-01

One of the largest field studies of reactive‐solute transport is the natural‐gradient experiment conducted at Cape Cod from 1985 to 1988. Major findings regarding the transport behavior of the reactive solute (lithium) were that the rate of plume displacement decreased with time (temporal increase in effective retardation), the degree of longitudinal spreading was much greater than that observed for bromide for an equivalent travel distance, and the plume was asymmetric, with maximum concentrations located near the leading edges. The objective of our work was to quantitatively analyze the transport of lithium and to attempt to identify the factor or factors that contributed significantly to its observed nonideal transport. We used a mathematical model that accounted for several transport factors, including spatially variable hydraulic conductivity and spatially variable, nonlinear, rate‐limited sorption, with all parameter values obtained independently. The transport behavior observed during the first 250 days, corresponding to a transport distance of 60 m, was predicted reasonably well by the simulation that incorporated spatially variable hydraulic conductivity; nonlinear, rate‐limited, spatially variable sorption; and uniform water chemistry. However, the larger degree of deceleration observed during the latter stage of the experiment (the filial 20 m) was not. The larger deceleration was successfully simulated by increasing 3‐fold the mean sorption capacity of the latter portion of the transport domain. Such a change in sorption capacity is consistent with the potential impact on lithium sorption of measured changes in water chemistry (e.g.,pH increase, reduction in resident Zn)at occur in the zone through which the lithium plume traversed. The results of the analyses suggest that nonlinear sorption and variable water chemistry may have btors responsible for the nonuniform displacement of the lithium plume, with rate‐limited sorption/desorption having minimal impact. In addition, the asymmetry of the plume appears to have been caused primarily by nonlinear sorption, whereas the enhanced longitudinal spreading appears to have been caused by the combined influences of spatially variable hydraulic conductivity and sorption, nonlinear sorption, and rate‐limited sorption/desorption. A comparison of the results of this analysis to those we obtained from an analysis of the Borden natural‐gradient study reveals several similarities regarding the transport of reactive contaminants at the field scale.

Analysing magnetism using scanning SQUID microscopy.

PubMed

Reith, P; Renshaw Wang, X; Hilgenkamp, H

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

Analysing magnetism using scanning SQUID microscopy

NASA Astrophysics Data System (ADS)

Reith, P.; Renshaw Wang, X.; Hilgenkamp, H.

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

PubMed

Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

2015-01-01

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

Validity and reliability of the Persian version of spatial hearing questionnaire

PubMed Central

Delphi, Maryam; Zamiri Abdolahi, Farzaneh; Tyler, Richard; Bakhit, Mahsa; Saki, Nader; Nazeri, Ahmad Reza

2015-01-01

Background: Our hearing ability in space is critical for hearing speech in noisy environment and localization. The Spatial Hearing Questionnaire (SHQ) has been devised to focus only on spatial haring tasks (e.g., lateralization, distance detection and binaural detection). The aim of the present study was to determine the reliability and validity of the Persian translation of the SHQ (Spatial Hearing Questionnaire). Methods: Translation and back-translation, reliability, content and construct validity were investigated. Eighty patients with sensory neural hearing loss (SNHL) (52.50% female and 47.5 % male) with the mean±SD age of 49.02±13.60 years completed SHQ, and they were categorized into mild, moderate, moderate to severe and severe groups based on their hearing threshold. Inclusion criteria in this study were the MMSE questionnaire score of higher than 21, good general health, no history of psychiatric disorders, dizziness or vertigo, dementia or alcohol abuse. Results: The reliability was assessed by Cronbach’s alpha and found to be 0.99. Item-total correlation was between r= 0.84 and 0.92. There was a significant difference between the mean score of PSHQ in the four groups. Based on the factor analysis, two factors were extracted from the questions in P-SHQ: sound localization; and music and speech understanding in noise and quiet. These factors could explain 82.1% and 9.3% of the total variance, respectively. Conclusion: The present study proved the reliability and validity of the Persian version of SHQ (PSHQ). This provides a suitable tool for spatial hearing assessment in clinical/research environments. PMID:26793624

Cultural background shapes spatial reference frame proclivity

PubMed Central

Goeke, Caspar; Kornpetpanee, Suchada; Köster, Moritz; Fernández-Revelles, Andrés B.; Gramann, Klaus; König, Peter

2015-01-01

Spatial navigation is an essential human skill that is influenced by several factors. The present study investigates how gender, age, and cultural background account for differences in reference frame proclivity and performance in a virtual navigation task. Using an online navigation study, we recorded reaction times, error rates (confusion of turning axis), and reference frame proclivity (egocentric vs. allocentric reference frame) of 1823 participants. Reaction times significantly varied with gender and age, but were only marginally influenced by the cultural background of participants. Error rates were in line with these results and exhibited a significant influence of gender and culture, but not age. Participants’ cultural background significantly influenced reference frame selection; the majority of North-Americans preferred an allocentric strategy, while Latin-Americans preferred an egocentric navigation strategy. European and Asian groups were in between these two extremes. Neither the factor of age nor the factor of gender had a direct impact on participants’ navigation strategies. The strong effects of cultural background on navigation strategies without the influence of gender or age underlines the importance of socialized spatial cognitive processes and argues for socio-economic analysis in studies investigating human navigation. PMID:26073656

3D tensor-based blind multispectral image decomposition for tumor demarcation

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Peršin, Antun

2010-03-01

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Factors affecting plant species composition of hedgerows: relative importance and hierarchy

NASA Astrophysics Data System (ADS)

Deckers, Bart; Hermy, Martin; Muys, Bart

2004-07-01

Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.

Spatial Characteristics and Driving Factors of Provincial Wastewater Discharge in China.

PubMed

Chen, Kunlun; Liu, Xiaoqiong; Ding, Lei; Huang, Gengzhi; Li, Zhigang

2016-12-09

Based on the increasing pressure on the water environment, this study aims to clarify the overall status of wastewater discharge in China, including the spatio-temporal distribution characteristics of wastewater discharge and its driving factors, so as to provide reference for developing "emission reduction" strategies in China and discuss regional sustainable development and resources environment policies. We utilized the Exploratory Spatial Data Analysis (ESDA) method to analyze the characteristics of the spatio-temporal distribution of the total wastewater discharge among 31 provinces in China from 2002 to 2013. Then, we discussed about the driving factors, affected the wastewater discharge through the Logarithmic Mean Divisia Index (LMDI) method and classified those driving factors. Results indicate that: (1) the total wastewater discharge steadily increased, based on the social economic development, with an average growth rate of 5.3% per year; the domestic wastewater discharge is the main source of total wastewater discharge, and the amount of domestic wastewater discharge is larger than the industrial wastewater discharge. There are many spatial differences of wastewater discharge among provinces via the ESDA method. For example, provinces with high wastewater discharge are mainly the developed coastal provinces such as Jiangsu Province and Guangdong Province. Provinces and their surrounding areas with low wastewater discharge are mainly the undeveloped ones in Northwest China; (2) The dominant factors affecting wastewater discharge are the economy and technological advance; The secondary one is the efficiency of resource utilization, which brings about the unstable effect; population plays a less important role in wastewater discharge. The dominant driving factors affecting wastewater discharge among 31 provinces are divided into three types, including two-factor dominant type, three-factor leading type and four-factor antagonistic type. In addition, the proposals aimed at reducing the wastewater discharge are provided on the basis of these three types.

Modelling typhoid risk in Dhaka Metropolitan Area of Bangladesh: the role of socio-economic and environmental factors

PubMed Central

2013-01-01

Background Developing countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as Cholera, Typhoid and Paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and Principal Axis Factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a Quality of Life index, which in turn is used in a regression model of typhoid occurrence and risk. Results The three Principal Factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using Ordinary Least Squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the Principal factors. The use of Geographically Weighted Regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike Information Criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%. Conclusions The typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination. PMID:23497202

Modelling typhoid risk in Dhaka metropolitan area of Bangladesh: the role of socio-economic and environmental factors.

PubMed

Corner, Robert J; Dewan, Ashraf M; Hashizume, Masahiro

2013-03-16

Developing countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as cholera, typhoid and paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and principal axis factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a quality of life index, which in turn is used in a regression model of typhoid occurrence and risk. The three principal factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using ordinary least squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the principal factors. The use of geographically weighted regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike information criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%. The typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka metropolitan area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination.

Diverse Responses of Global Vegetation to Climate Changes: Spatial Patterns and Time-lag Effects

NASA Astrophysics Data System (ADS)

Wu, D.; Zhao, X.; Zhou, T.; Huang, K.; Xu, W.

2014-12-01

Global climate changes have enormous influences on vegetation growth, meanwhile, response of vegetation to climate express space diversity and time-lag effects, which account for spatial-temporal disparities of climate change and spatial heterogeneity of ecosystem. Revelation of this phenomenon will help us further understanding the impact of climate change on vegetation. Assessment and forecast of global environmental change can be also improved under further climate change. Here we present space diversity and time-lag effects patterns of global vegetation respond to three climate factors (temperature, precipitation and solar radiation) based on quantitative analysis of satellite data (NDVI) and Climate data (Climate Research Unit). We assessed the time-lag effects of global vegetation to main climate factors based on the great correlation fitness between NDVI and the three climate factors respectively among 0-12 months' temporal lags. On this basis, integrated response model of NDVI and the three climate factors was built to analyze contribution of different climate factors to vegetation growth with multiple regression model and partial correlation model. In the result, different vegetation types have distinct temporal lags to the three climate factors. For the precipitation, temporal lags of grasslands are the shortest while the evergreen broad-leaf forests are the longest, which means that grasslands are more sensitive to precipitation than evergreen broad-leaf forests. Analysis of different climate factors' contribution to vegetation reveal that vegetation are dominated by temperature in the high northern latitudes; they are mainly restricted by precipitation in arid and semi-arid areas (Australia, Western America); in humid areas of low and intermediate latitudes (Amazon, Eastern America), vegetation are mainly influenced by solar radiation. Our results reveal the time-lag effects and major driving factors of global vegetation growth and explain the spatiotemporal variations of global vegetation in last 30 years. Significantly, it is as well as in forecasting and assessing the influences of future climate change on the vegetation dynamics. This work was supported by the High Technology Research and Development Program of China (Grant NO.2013AA122801).

Patterns of fish community composition along a river affected by agricultural and urban disturbance in south-central Chile

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

Orrego, Rodrigo; Barra, Ricardo; Chiang, Gustavo

2008-03-01

Patterns of fish community composition in a south-central Chile river were investigated along the altitudinal-spatial and environmental gradient and as a function of anthropogenic factors. The spatial pattern of fish communities in different biocoenotic zones of the Chillan River is influenced by both natural factors such a hydrologic features, habitat, and feeding types, and also by water quality variables which can reduce the diversity and abundance of sensitive species. A principal component analysis incorporating both water quality parameters and biomarker responses of representative fish species was used to evaluate the status of fish communities along the spatial gradient of themore » stream. The abundance and diversity of the fish community changed from a low in the upper reaches where the low pollution-tolerant species such as salmonid dominated, to a reduced diversity in the lower reaches of the river where tolerant browser species such as cypriniformes dominated. Even though the spatial pattern of fish community structure is similar to that found for the Chilean Rivers, the structure of these communities is highly influenced by human disturbance, particularly along the lower reaches of the river.« less

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.

New spatially continuous indices of redlining and racial bias in mortgage lending: links to survival after breast cancer diagnosis and implications for health disparities research.

PubMed

Beyer, Kirsten M M; Zhou, Yuhong; Matthews, Kevin; Bemanian, Amin; Laud, Purushottam W; Nattinger, Ann B

2016-07-01

Racial health disparities continue to be a serious problem in the United States and have been linked to contextual factors, including racial segregation. In some cases, including breast cancer survival, racial disparities appear to be worsening. Using the Home Mortgage Disclosure Act (HMDA) database, we extend current spatial analysis methodology to derive new, spatially continuous indices of (1) racial bias in mortgage lending and (2) redlining. We then examine spatial patterns of these indices and the association between these new measures and breast cancer survival among Black/African American women in the Milwaukee, Wisconsin metropolitan area. These new measures can be used to examine relationships between mortgage discrimination and patterns of disease throughout the United States. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effect of occlusion on the semantics of projective spatial terms: a case study in grounding language in perception.

PubMed

Kelleher, John D; Ross, Robert J; Sloan, Colm; Mac Namee, Brian

2011-02-01

Although data-driven spatial template models provide a practical and cognitively motivated mechanism for characterizing spatial term meaning, the influence of perceptual rather than solely geometric and functional properties has yet to be systematically investigated. In the light of this, in this paper, we investigate the effects of the perceptual phenomenon of object occlusion on the semantics of projective terms. We did this by conducting a study to test whether object occlusion had a noticeable effect on the acceptance values assigned to projective terms with respect to a 2.5-dimensional visual stimulus. Based on the data collected, a regression model was constructed and presented. Subsequent analysis showed that the regression model that included the occlusion factor outperformed an adaptation of Regier & Carlson's well-regarded AVS model for that same spatial configuration.

Domain-Specific and Domain-General Learning Factors are Expressed in Genetically Heterogeneous CD-1 mice

PubMed Central

Kolata, Stefan; Light, Kenneth; Matzel, Louis D.

2008-01-01

It has been established that both domain-specific (e.g. spatial) as well as domain-general (general intelligence) factors influence human cognition. However, the separation of these processes has rarely been attempted in studies using laboratory animals. Previously, we have found that the performances of outbred mice across a wide range of learning tasks correlate in such a way that a single factor can explain 30– 44% of the variance between animals. This general learning factor is in some ways qualitatively and quantitatively analogous to general intelligence in humans. The complete structure of cognition in mice, however, has not been explored due to the limited sample sizes of our previous analyses. Here we report a combined analysis from 241 CD-1 mice tested in five primary learning tasks, and a subset of mice tested in two additional learning tasks. At least two (possibly three) of the seven learning tasks placed explicit demands on spatial and/or hippocampus-dependent processing abilities. Consistent with previous findings, we report a robust general factor influencing learning in mice that accounted for 38% of the variance across tasks. In addition, a domain-specific factor was found to account for performance on that subset of tasks that shared a dependence on hippocampal and/or spatial processing. These results provide further evidence for a general learning/cognitive factor in genetically heterogeneous mice. Furthermore (and similar to human cognitive performance), these results suggest a hierarchical structure to cognitive processes in this genetically heterogeneous species. PMID:19129932

Evaluation of the Actuator Line Model with coarse resolutions

NASA Astrophysics Data System (ADS)

Draper, M.; Usera, G.

2015-06-01

The aim of the present paper is to evaluate the Actuator Line Model (ALM) in spatial resolutions coarser than what is generally recommended, also using larger time steps. To accomplish this, the ALM has been implemented in the open source code caffa3d.MBRi and validated against experimental measurements of two wind tunnel campaigns (stand alone wind turbine and two wind turbines in line, case A and B respectively), taking into account two spatial resolutions: R/8 and R/15 (R is the rotor radius). A sensitivity analysis in case A was performed in order to get some insight into the influence of the smearing factor (3D Gaussian distribution) and time step size in power and thrust, as well as in the wake, without applying a tip loss correction factor (TLCF), for one tip speed ratio (TSR). It is concluded that as the smearing factor is larger or time step size is smaller the power is increased, but the velocity deficit is not as much affected. From this analysis, a smearing factor was obtained in order to calculate precisely the power coefficient for that TSR without applying TLCF. Results with this approach were compared with another simulation choosing a larger smearing factor and applying Prandtl's TLCF, for three values of TSR. It is found that applying the TLCF improves the power estimation and weakens the influence of the smearing factor. Finally, these 2 alternatives were tested in case B, confirming that conclusion.

Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

2016-06-01

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

Identifying Flood-Related Infectious Diseases in Anhui Province, China: A Spatial and Temporal Analysis

PubMed Central

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Jiang, Baofa

2016-01-01

The aim of this study was to explore infectious diseases related to the 2007 Huai River flood in Anhui Province, China. The study was based on the notified incidences of infectious diseases between June 29 and July 25 from 2004 to 2011. Daily incidences of notified diseases in 2007 were compared with the corresponding daily incidences during the same period in the other years (from 2004 to 2011, except 2007) by Poisson regression analysis. Spatial autocorrelation analysis was used to test the distribution pattern of the diseases. Spatial regression models were then performed to examine the association between the incidence of each disease and flood, considering lag effects and other confounders. After controlling the other meteorological and socioeconomic factors, malaria (odds ratio [OR] = 3.67, 95% confidence interval [CI] = 1.77–7.61), diarrhea (OR = 2.16, 95% CI = 1.24–3.78), and hepatitis A virus (HAV) infection (OR = 6.11, 95% CI = 1.04–35.84) were significantly related to the 2007 Huai River flood both from the spatial and temporal analyses. Special attention should be given to develop public health preparation and interventions with a focus on malaria, diarrhea, and HAV infection, in the study region. PMID:26903612

Spatial analysis of leprosy incidence and associated socioeconomic factors.

PubMed

Cury, Maria Rita de Cassia Oliveira; Paschoal, Vania Del'Arco; Nardi, Susilene Maria Tonelli; Chierotti, Ana Patrícia; Rodrigues Júnior, Antonio Luiz; Chiaravalloti-Neto, Francisco

2012-02-01

To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors. Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics. While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services. The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.

Spatial Analysis in Determining Physical Factors of Pedestrian Space Livability, Case Study: Pedestrian Space on Jalan Kemasan, Yogyakarta

NASA Astrophysics Data System (ADS)

Fauzi, A. F.; Aditianata, A.

2018-02-01

The existence of street as a place to perform various human activities becomes an important issue nowadays. In the last few decades, cars and motorcycles dominate streets in various cities in the world. On the other hand, human activity on the street is the determinant of the city livability. Previous research has pointed out that if there is lots of human activity in the street, then the city will be interesting. Otherwise, if the street has no activity, then the city will be boring. Learning from that statement, now various cities in the world are developing the concept of livable streets. Livable streets shown by diversity of human activities conducted in the streets’ pedestrian space. In Yogyakarta, one of the streets shown diversity of human activities is Jalan Kemasan. This study attempts to determine the physical factors of pedestrian space affecting the livability in Jalan Kemasan Yogyakarta through spatial analysis. Spatial analysis was performed by overlay technique between liveable point (activity diversity) distribution map and variable distribution map. Those physical pedestrian space research variable included element of shading, street vendors, building setback, seat location, divider between street and pedestrian way, and mixed use building function. More diverse the activity of one variable, then those variable are more affected then others. Overlay result then strengthened by field observation to qualitatively ensure the deduction. In the end, this research will provide valuable input for street and pedestrian space planning that is comfortable for human activities.

GIS-supported epidemiological analysis on canine Angiostrongylus vasorum and Crenosoma vulpis infections in Germany.

PubMed

Maksimov, Pavlo; Hermosilla, Carlos; Taubert, Anja; Staubach, Christoph; Sauter-Louis, Carola; Conraths, Franz J; Vrhovec, Majda Globokar; Pantchev, Nikola

2017-02-28

Angiostrongylus vasorum infections are the cause of severe cardiopulmonary diseases in dogs. In the past, canine angiostrongylosis has largely been neglected in Europe, although some recent studies indicated an expansion of historically known endemic areas, a phenomenon that might also apply to Crenosoma vulpis. The aim of the present study was to analyse temporal and spatial trends of canine A. vasorum and C. vulpis infections and to perform GIS-supported risk factor analysis to evaluate the role of landscape, age and seasonality in the life-cycle of these nematodes. A total of 12,682 faecal samples from German dogs (collected in 2003-2015) with clinical suspicion for lungworm infection were examined for the presence of A. vasorum and C. vulpis larvae by the Baermann funnel technique and respective epidemiological data (location and age of the sampled dogs, date of sampling) were subjected to GIS-supported risk factor analysis. Overall, A. vasorum and C. vulpis larvae were detected in 288 (2.3%) and 285 (2.2%) faecal samples, respectively. In general, both lungworm infections were found to be widely spread in Germany. GIS-supported analyses demonstrate spatial differences in the occurrence of canine A. vasorum and C. vulpis infections in Germany. also, risk factor analyses revealed an overlap but also diverging risk and protective factors for A. vasorum and C. vulpis infections. The current data also indicate a significant increase of A. vasorum and C. vulpis prevalences from 2003 to 2015 and from 2008 until 2015, respectively, and a potential spread of A. vasorum endemic areas to the northeastern part of Germany. The results of the present study show an insight into the epidemiological situation of lungworm infections (A. vasorum and C. vulpis) of the past 13 years in Germany. The data clearly demonstrate an increase of diagnosed A. vasorum prevalence in the tested dog population between 2003 and 2015 as well as spatial differences in the occurrence of diagnosed A. vasorum and C. vulpis infections of dogs in Germany. Risk factor analyses suggest possible differences in the biology of these parasites, presumably at the intermediate host level.

Spatial patterns of March and September streamflow trends in Pacific Northwest Streams, 1958-2008

USGS Publications Warehouse

Chang, Heejun; Jung, Il-Won; Steele, Madeline; Gannett, Marshall

2012-01-01

Summer streamflow is a vital water resource for municipal and domestic water supplies, irrigation, salmonid habitat, recreation, and water-related ecosystem services in the Pacific Northwest (PNW) in the United States. This study detects significant negative trends in September absolute streamflow in a majority of 68 stream-gauging stations located on unregulated streams in the PNW from 1958 to 2008. The proportion of March streamflow to annual streamflow increases in most stations over 1,000 m elevation, with a baseflow index of less than 50, while absolute March streamflow does not increase in most stations. The declining trends of September absolute streamflow are strongly associated with seven-day low flow, January–March maximum temperature trends, and the size of the basin (19–7,260 km2), while the increasing trends of the fraction of March streamflow are associated with elevation, April 1 snow water equivalent, March precipitation, center timing of streamflow, and October–December minimum temperature trends. Compared with ordinary least squares (OLS) estimated regression models, spatial error regression and geographically weighted regression (GWR) models effectively remove spatial autocorrelation in residuals. The GWR model results show spatial gradients of local R 2 values with consistently higher local R 2 values in the northern Cascades. This finding illustrates that different hydrologic landscape factors, such as geology and seasonal distribution of precipitation, also influence streamflow trends in the PNW. In addition, our spatial analysis model results show that considering various geographic factors help clarify the dynamics of streamflow trends over a large geographical area, supporting a spatial analysis approach over aspatial OLS-estimated regression models for predicting streamflow trends. Results indicate that transitional rain–snow surface water-dominated basins are likely to have reduced summer streamflow under warming scenarios. Consequently, a better understanding of the relationships among summer streamflow, precipitation, snowmelt, elevation, and geology can help water managers predict the response of regional summer streamflow to global warming.

A systematic intercomparison of regional flood frequency analysis models in a simulation framework

NASA Astrophysics Data System (ADS)

Ganora, Daniele; Laio, Francesco; Claps, Pierluigi

2015-04-01

Regional frequency analysis (RFA) is a well-established methodology to provide an estimate of the flood frequency curve (or other discharge-related variables), based on the fundamental concept of substituting temporal information at a site (no data or short time series) by exploiting observations at other sites (spatial information). Different RFA paradigms exist, depending on the way the information is transferred to the site of interest. Despite the wide use of such methodology, a systematic comparison between these paradigms has not been performed. The aim of this study is to provide a framework wherein carrying out the intercomparison: we thus synthetically generate data through Monte Carlo simulations for a number of (virtual) stations, following a GEV parent distribution; different scenarios can be created to represent different spatial heterogeneity patterns by manipulating the parameters of the parent distribution at each station (e.g. with a linear variation in space of the shape parameter of the GEV). A special case is the homogeneous scenario where each station record is sampled from the same parent distribution. For each scenario and each simulation, different regional models are applied to evaluate the 200-year growth factor at each station. Results are than compared to the exact growth factor of each station, which is known in our virtual world. Considered regional approaches include: (i) a single growth curve for the whole region; (ii) a multiple-region model based on cluster analysis which search for an adequate number of homogeneous subregions; (iii) a Region-of-Influence model which defines a homogeneous subregion for each site; (iv) a spatially-smooth estimation procedure based on linear regressions.. A further benchmark model is the at-site estimate based on the analysis of the local record. A comprehensive analysis of the results of the simulations shows that, if the scenario is homogeneous (no spatial variability), all the regional approaches have comparable performances. Moreover, as expected, regional estimates are much more reliable than the at-site estimates. If the scenario is heterogeneous, the performances of the regional models depend on the pattern of heterogeneity; in general, however, the spatially-smooth regional approach performs better than the others, and its performances improve for increasing record lengths. For heterogeneous scenarios, the at-site estimates appear to be comparably more efficient than in the homogeneous case, and in general less biased than the regional estimates.

Interaction of Soil Heavy Metal Pollution with Industrialisation and the Landscape Pattern in Taiyuan City, China

PubMed Central

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. PMID:25251460

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China.

PubMed

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Urban Heat Island in the city of Bari (Italy) ant its relationship with morphological features

NASA Astrophysics Data System (ADS)

Ceppi, C.; Balena, P.; Loconte, P.; Mancini, F.

2012-04-01

The investigation of an Urban Heat Island (UHI) and its relationship with the wide range of factors able to explain its behavior is a very difficult task: the main trouble is represented by the spatial variability of the urban temperature due to the extreme heterogeneousness of the urban coverage and morphological features. In literature it is known that the local surface temperatures are influenced by the changing characteristics in urban surface and modification of land surface processes affecting the surface energy balance and the shape of boundary layer. The whole processes could lead to distinct urban climates. This work is mainly focused on the mechanisms which are actually connecting the urban morphology with the surface temperature as derived by satellite data provided from the ASTER sensor. Urban morphology could be described by several factors depending on the selected scale of analysis. At the macroscale the UHI is more related to the land-use, environmental context and boundary conditions. At the microscale the surface characteristics, urban density, ratio between green and built areas and, construction and built typology are more involved in addition to the composite indicators such as the Sky View factor and the elevation of the built texture. The case study of the city of Bari is faced. It is a medium sized city in the southern Italy, characterized by the presence of a pervasive waterfront and presence of "lame", a natural erosive furrows shallow that are typical of the Apulia country side. Such ephemeral streams convey the stormwater from the plateau of the hilly Murgia areas to the sea. Moreover, the urban complexity of the city exacerbates the spatial variability of the phenomenon. The first step aim at the investigating of the relationship between the thermal behavior and the above mentioned factors by the construction of a set of homogeneous morphological units. The classification is built both in the urban and rural zone. The second step focuses on the development of a spatial statistical analysis based on qualitative and quantitative indicators able to link the classes of urban morphology with the satellite-based surface temperature. The relationships highlighted by such a spatial analysis can be used to model the urban climate and, consequently, develop a new kind of planning more addressed towards the mitigation of the UHI phenomenon.

Temporal and Spatial Analysis of Neural Tube Defects and Detection of Geographical Factors in Shanxi Province, China

PubMed Central

He, Lei; Wang, Jinfeng; Liu, Xin; Zhang, Ningxu; Xu, Bing

2016-01-01

Background Neural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world’s highest rate of NTDs. Since the 1990s, China’s government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world’s average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area. Methods In this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley’s k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads. Results In both Heshun County and Yuanping County, the incidence of NTDs was significantly (P<0.05) reduced after intervention. The results from spatial analysis showed that significant spatial heterogeneity existed in both counties. NTD clusters were still identified in areas close to coal sites and main roads after interventions. This study also revealed that the elevation, fault and soil types always had a larger influence on the incidence of NTDs in our study areas. In addition, distance to the river was a risk factor of NTDs in areas close to the coal sites and main roads. Conclusion The existing interventions may have played an important role to reduce the incidence of NTDs. However, there is still spatial heterogeneity in both counties after using the traditional intervention methods. The government needs to take more measures to strengthen the environmental restoration to prevent the occurrence of NTDs, especially those areas close to coal sites and main roads. The outcome of this research provides an important theoretical basis and technical support for the government to prevent the occurrence of NTDs. PMID:27100082

[Spatial pattern of forest biomass and its influencing factors in the Great Xing'an Mountains, Heilongjiang Province, China].

PubMed

Wang, Xiao-Li; Chang, Yu; Chen, Hong-Wei; Hu, Yuan-Man; Jiao, Lin-Lin; Feng, Yu-Ting; Wu, Wen; Wu, Hai-Feng

2014-04-01

Based on field inventory data and vegetation index EVI (enhanced vegetation index), the spatial pattern of the forest biomass in the Great Xing'an Mountains, Heilongjiang Province was quantitatively analyzed. Using the spatial analysis and statistics tools in ArcGIS software, the impacts of climatic zone, elevation, slope, aspect and vegetation type on the spatial pattern of forest biomass were explored. The results showed that the forest biomass in the Great Xing'an Mountains was 350 Tg and spatially aggregated with great increasing potentials. Forest biomass density in the cold temperate humid zone (64.02 t x hm(-2)) was higher than that in the temperate humid zone (60.26 t x hm(-2)). The biomass density of each vegetation type was in the order of mixed coniferous forest (65.13 t x hm(-2)) > spruce-fir forest (63.92 t x hm(-2)) > Pinus pumila-Larix gmelinii forest (63.79 t x hm(-2)) > Pinus sylvestris var. mongolica forest (61.97 t x hm(-2)) > Larix gmelinii forest (61.40 t x hm(-2)) > deciduous broadleaf forest (58.96 t x hm(-2)). With the increasing elevation and slope, the forest biomass density first decreased and then increased. The forest biomass density in the shady slopes was greater than that in the sunny slopes. The spatial pattern of forest biomass in the Great Xing' an Mountains exhibited a heterogeneous pattern due to the variation of climatic zone, vegetation type and topographical factor. This spatial heterogeneity needs to be accounted when evaluating forest biomass at regional scales.

Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

PubMed

Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

2014-02-01

In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City.

PubMed

Shahid, Rizwan; Bertazzon, Stefania

2015-01-01

Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow's adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the integration of GWR and simulation modeling is effective, and the proposed framework can assist in designing local interventions to control and prevent childhood obesity.

Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

NASA Astrophysics Data System (ADS)

Tsai, F.; Hwang, J.-H.; Chen, L.-C.; Lin, T.-H.

2010-10-01

On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.

Spatial-Temporal Dynamics of Urban Fire Incidents: a Case Study of Nanjing, China

NASA Astrophysics Data System (ADS)

Yao, J.; Zhang, X.

2016-06-01

Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safer living environment. Well understanding spatial-temporal dynamics of fire incidents can offer insights for potential determinants of various fire events and enable better fire risk estimation, assisting future allocation of prevention resources and strategic planning of mitigation programs. Using a 12-year (2002-2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatial-temporal dynamics of urban fire incidents. A range of exploratory spatial data analysis (ESDA) approaches and tools, such as spatial kernel density and co-maps, are employed to examine the spatial, temporal and spatial-temporal variations of the fire events. Particular attention has been paid to two types of fire incidents: residential properties and local facilities, due to their relatively higher occurrence frequencies. The results demonstrated that the amount of urban fire has greatly increased in the last decade and spatial-temporal distribution of fire events vary among different incident types, which implies varying impact of potential influencing factors for further investigation.

A computer software system for integration and analysis of grid-based remote sensing data with other natural resource data. Remote Sensing Project

NASA Technical Reports Server (NTRS)

Tilmann, S. E.; Enslin, W. R.; Hill-Rowley, R.

1977-01-01

A computer-based information system is described designed to assist in the integration of commonly available spatial data for regional planning and resource analysis. The Resource Analysis Program (RAP) provides a variety of analytical and mapping phases for single factor or multi-factor analyses. The unique analytical and graphic capabilities of RAP are demonstrated with a study conducted in Windsor Township, Eaton County, Michigan. Soil, land cover/use, topographic and geological maps were used as a data base to develope an eleven map portfolio. The major themes of the portfolio are land cover/use, non-point water pollution, waste disposal, and ground water recharge.

[Multiple time scales analysis of spatial differentiation characteristics of non-point source nitrogen loss within watershed].

PubMed

Liu, Mei-bing; Chen, Xing-wei; Chen, Ying

2015-07-01

Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.

Land quality, sustainable development and environmental degradation in agricultural districts: A computational approach based on entropy indexes

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

Zambon, Ilaria, E-mail: ilaria.zambon@unitus.it; Colantoni, Andrea; Carlucci, Margherita

Land Degradation (LD) in socio-environmental systems negatively impacts sustainable development paths. This study proposes a framework to LD evaluation based on indicators of diversification in the spatial distribution of sensitive land. We hypothesize that conditions for spatial heterogeneity in a composite index of land sensitivity are more frequently associated to areas prone to LD than spatial homogeneity. Spatial heterogeneity is supposed to be associated with degraded areas that act as hotspots for future degradation processes. A diachronic analysis (1960–2010) was performed at the Italian agricultural district scale to identify environmental factors associated with spatial heterogeneity in the degree of landmore » sensitivity to degradation based on the Environmentally Sensitive Area Index (ESAI). In 1960, diversification in the level of land sensitivity measured using two common indexes of entropy (Shannon's diversity and Pielou's evenness) increased significantly with the ESAI, indicating a high level of land sensitivity to degradation. In 2010, surface area classified as “critical” to LD was the highest in districts with diversification in the spatial distribution of ESAI values, confirming the hypothesis formulated above. Entropy indexes, based on observed alignment with the concept of LD, constitute a valuable base to inform mitigation strategies against desertification. - Highlights: • Spatial heterogeneity is supposed to be associated with degraded areas. • Entropy indexes can inform mitigation strategies against desertification. • Assessing spatial diversification in the degree of land sensitivity to degradation. • Mediterranean rural areas have an evident diversity in agricultural systems. • A diachronic analysis carried out at the Italian agricultural district scale.« less

Using a Systematic Approach in the Analysis of the Factors That Influence On a Form Formation of Buildings of Higher Educational Establishments

NASA Astrophysics Data System (ADS)

Martyniv, Oleksandra; Kinasz, Roman

2017-10-01

This material covers the row of basic factors that influence on architectonically-spatial solution formation of building of Higher educational establishments (hereinafter universities). For this purpose, the systematization process of factors that influence on the university architecture was conducted and presented. The conclusion of this article was the proposed concept of considering universities as a hierarchical system, elements of which act as factors of influence, which in the process of alternating influence lead to the main goal, namely the formation of a new university building.

The Profile of Memory Function in Children With Autism

PubMed Central

Williams, Diane L.; Goldstein, Gerald; Minshew, Nancy J.

2007-01-01

A clinical memory test was administered to 38 high-functioning children with autism and 38 individually matched normal controls, 8–16 years of age. The resulting profile of memory abilities in the children with autism was characterized by relatively poor memory for complex visual and verbal information and spatial working memory with relatively intact associative learning ability, verbal working memory, and recognition memory. A stepwise discriminant function analysis of the subtests found that the Finger Windows subtest, a measure of spatial working memory, discriminated most accurately between the autism and normal control groups. A principal components analysis indicated that the factor structure of the subtests differed substantially between the children with autism and controls, suggesting differing organizations of memory ability. PMID:16460219

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Epidemiologic evaluation of diarrhea in dogs in an animal shelter.

PubMed

Sokolow, Susanne H; Rand, Courtney; Marks, Stanley L; Drazenovich, Niki L; Kather, Elizabeth J; Foley, Janet E

2005-06-01

To determine associations among infectious pathogens and diarrheal disease in dogs in an animal shelter and demonstrate the use of geographic information systems (GISs) for tracking spatial distributions of diarrheal disease within shelters. Feces from 120 dogs. Fresh fecal specimens were screened for bacteria and bacterial toxins via bacteriologic culture and ELISA, parvovirus via ELISA, canine coronavirus via nested polymerase chain reaction assay, protozoal cysts and oocysts via a direct fluorescent antibody technique, and parasite ova and larvae via microscopic examination of direct wet mounts and zinc sulfate centrifugation flotation. Salmonella enterica and Brachyspira spp were not common, whereas other pathogens such as canine coronavirus and Helicobacter spp were common among the dogs that were surveyed. Only intestinal parasites and Campylobacterjejuni infection were significant risk factors for diarrhea by univariate odds ratio analysis. Giardia lamblia was significantly underestimated by fecal flotation, compared with a direct fluorescent antibody technique. Spatial analysis of case specimens by use of GIS indicated that diarrhea was widespread throughout the entire shelter, and spatial statistical analysis revealed no evidence of spatial clustering of case specimens. This study provided an epidemiologic overview of diarrhea and interacting diarrhea-associated pathogens in a densely housed, highly predisposed shelter population of dogs. Several of the approaches used in this study, such as use of a spatial representation of case specimens and considering multiple etiologies simultaneously, were novel and illustrate an integrated approach to epidemiologic investigations in shelter populations.

Multilevel Analysis of Trachomatous Trichiasis and Corneal Opacity in Nigeria: The Role of Environmental and Climatic Risk Factors on the Distribution of Disease.

PubMed

Smith, Jennifer L; Sivasubramaniam, Selvaraj; Rabiu, Mansur M; Kyari, Fatima; Solomon, Anthony W; Gilbert, Clare

2015-01-01

The distribution of trachoma in Nigeria is spatially heterogeneous, with large-scale trends observed across the country and more local variation within areas. Relative contributions of individual and cluster-level risk factors to the geographic distribution of disease remain largely unknown. The primary aim of this analysis is to assess the relationship between climatic factors and trachomatous trichiasis (TT) and/or corneal opacity (CO) due to trachoma in Nigeria, while accounting for the effects of individual risk factors and spatial correlation. In addition, we explore the relative importance of variation in the risk of trichiasis and/or corneal opacity (TT/CO) at different levels. Data from the 2007 National Blindness and Visual Impairment Survey were used for this analysis, which included a nationally representative sample of adults aged 40 years and above. Complete data were available from 304 clusters selected using a multi-stage stratified cluster-random sampling strategy. All participants (13,543 individuals) were interviewed and examined by an ophthalmologist for the presence or absence of TT and CO. In addition to field-collected data, remotely sensed climatic data were extracted for each cluster and used to fit Bayesian hierarchical logistic models to disease outcome. The risk of TT/CO was associated with factors at both the individual and cluster levels, with approximately 14% of the total variation attributed to the cluster level. Beyond established individual risk factors (age, gender and occupation), there was strong evidence that environmental/climatic factors at the cluster-level (lower precipitation, higher land surface temperature, higher mean annual temperature and rural classification) were also associated with a greater risk of TT/CO. This study establishes the importance of large-scale risk factors in the geographical distribution of TT/CO in Nigeria, supporting anecdotal evidence that environmental conditions are associated with increased risk in this context and highlighting their potential use in improving estimates of disease burden at large scales.

ANALYSIS OF ONE-DIMENSIONAL SOLUTE TRANSPORT THROUGH POROUS MEDIA WITH SPATIALLY VARIABLE RETARDATION FACTOR. (R825689C037)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

A Neighborhood Analysis of Public Library Use in New York City

ERIC Educational Resources Information Center

Japzon, Andrea C.; Gong, Hongmian

2005-01-01

The use of 200 public libraries in New York City was analyzed according to their neighborhood characteristics. In addition to demographic, economic, and cultural factors traditionally considered, the social and spatial interactions within a neighborhood were related to public library use. Correlation and regression analyses were implemented for…

Multisite-multivariable sensitivity analysis of distributed watershed models: enhancing the perceptions from computationally frugal methods

USDA-ARS?s Scientific Manuscript database

This paper assesses the impact of different likelihood functions in identifying sensitive parameters of the highly parameterized, spatially distributed Soil and Water Assessment Tool (SWAT) watershed model for multiple variables at multiple sites. The global one-factor-at-a-time (OAT) method of Morr...

Relationship between Russian wheat aphid abundance and edaphic and topographic characteristics of wheat fields

USDA-ARS?s Scientific Manuscript database

This study explores the spatial relationship between Russian wheat aphid population density and variation in edaphic or topographic factors within wheat fields. Multiple regression analysis was applied to data collected from six wheat fields located in three States, Colorado, Wyoming, and Nebraska....

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

PubMed

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

2016-10-01

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

Spatial elements of mortality risk in old-growth forests

USGS Publications Warehouse

Das, Adrian; Battles, John; van Mantgem, Phillip J.; Stephenson, Nathan L.

2008-01-01

For many species of long-lived organisms, such as trees, survival appears to be the most critical vital rate affecting population persistence. However, methods commonly used to quantify tree death, such as relating tree mortality risk solely to diameter growth, almost certainly do not account for important spatial processes. Our goal in this study was to detect and, if present, to quantify the relevance of such processes. For this purpose, we examined purely spatial aspects of mortality for four species, Abies concolor, Abies magnifica, Calocedrus decurrens, and Pinus lambertiana, in an old-growth conifer forest in the Sierra Nevada of California, USA. The analysis was performed using data from nine fully mapped long-term monitoring plots.In three cases, the results unequivocally supported the inclusion of spatial information in models used to predict mortality. For Abies concolor, our results suggested that growth rate may not always adequately capture increased mortality risk due to competition. We also found evidence of a facilitative effect for this species, with mortality risk decreasing with proximity to conspecific neighbors. For Pinus lambertiana, mortality risk increased with density of conspecific neighbors, in keeping with a mechanism of increased pathogen or insect pressure (i.e., a Janzen-Connell type effect). Finally, we found that models estimating risk of being crushed were strongly improved by the inclusion of a simple index of spatial proximity.Not only did spatial indices improve models, those improvements were relevant for mortality prediction. For P. lambertiana, spatial factors were important for estimation of mortality risk regardless of growth rate. For A. concolor, although most of the population fell within spatial conditions in which mortality risk was well described by growth, trees that died occurred outside those conditions in a disproportionate fashion. Furthermore, as stands of A. concolor become increasingly dense, such spatial factors are likely to become increasingly important. In general, models that fail to account for spatial pattern are at risk of failure as conditions change.

Dissecting the multi-scale spatial relationship of earthworm assemblages with soil environmental variability.

PubMed

Jiménez, Juan J; Decaëns, Thibaud; Lavelle, Patrick; Rossi, Jean-Pierre

2014-12-05

Studying the drivers and determinants of species, population and community spatial patterns is central to ecology. The observed structure of community assemblages is the result of deterministic abiotic (environmental constraints) and biotic factors (positive and negative species interactions), as well as stochastic colonization events (historical contingency). We analyzed the role of multi-scale spatial component of soil environmental variability in structuring earthworm assemblages in a gallery forest from the Colombian "Llanos". We aimed to disentangle the spatial scales at which species assemblages are structured and determine whether these scales matched those expressed by soil environmental variables. We also tested the hypothesis of the "single tree effect" by exploring the spatial relationships between root-related variables and soil nutrient and physical variables in structuring earthworm assemblages. Multivariate ordination techniques and spatially explicit tools were used, namely cross-correlograms, Principal Coordinates of Neighbor Matrices (PCNM) and variation partitioning analyses. The relationship between the spatial organization of earthworm assemblages and soil environmental parameters revealed explicitly multi-scale responses. The soil environmental variables that explained nested population structures across the multi-spatial scale gradient differed for earthworms and assemblages at the very-fine- (<10 m) to medium-scale (10-20 m). The root traits were correlated with areas of high soil nutrient contents at a depth of 0-5 cm. Information on the scales of PCNM variables was obtained using variogram modeling. Based on the size of the plot, the PCNM variables were arbitrarily allocated to medium (>30 m), fine (10-20 m) and very fine scales (<10 m). Variation partitioning analysis revealed that the soil environmental variability explained from less than 1% to as much as 48% of the observed earthworm spatial variation. A large proportion of the spatial variation did not depend on the soil environmental variability for certain species. This finding could indicate the influence of contagious biotic interactions, stochastic factors, or unmeasured relevant soil environmental variables.

Functional resilience of microbial ecosystems in soil: How important is a spatial analysis?

NASA Astrophysics Data System (ADS)

König, Sara; Banitz, Thomas; Centler, Florian; Frank, Karin; Thullner, Martin

2015-04-01

Microbial life in soil is exposed to fluctuating environmental conditions influencing the performance of microbially mediated ecosystem services such as biodegradation of contaminants. However, as this environment is typically very heterogeneous, spatial aspects can be expected to play a major role for the ability to recover from a stress event. To determine key processes for functional resilience, simple scenarios with varying stress intensities were simulated within a microbial simulation model and the biodegradation rate in the recovery phase monitored. Parameters including microbial growth and dispersal rates were varied over a typical range to consider microorganisms with varying properties. Besides an aggregated temporal monitoring, the explicit observation of the spatio-temporal dynamics proved essential to understand the recovery process. For a mechanistic understanding of the model system, scenarios were also simulated with selected processes being switched-off. Results of the mechanistic and the spatial view show that the key factors for functional recovery with respect to biodegradation after a simple stress event depend on the location of the observed habitats. The limiting factors near unstressed areas are spatial processes - the mobility of the bacteria as well as substrate diffusion - the longer the distance to the unstressed region the more important becomes the process growth. Furthermore, recovery depends on the stress intensity - after a low stress event the spatial configuration has no influence on the key factors for functional resilience. To confirm these results, we repeated the stress scenarios but this time including an additional dispersal network representing a fungal network in soil. The system benefits from an increased spatial performance due to the higher mobility of the degrading microorganisms. However, this effect appears only in scenarios where the spatial distribution of the stressed area plays a role. With these simulations we show that spatial aspects play a main role for recovering after a severe stress event in a highly heterogeneous environment such as soil, and thus the relevance of the exact distribution of the stressed area. In consequence a spatial-mechanistic view is necessary for examining the functional resilience as the aggregated temporal view alone could not have led to these conclusions. Further research should explore the importance of a spatial view for quantifying the recovery of the ecosystem service also after more complex stress regimes.

Geographical variations and contextual effects on age of initiation of sexual intercourse among women in Nigeria: a multilevel and spatial analysis.

PubMed

Uthman, Olalekan A

2008-05-30

The age of initiation of sexual intercourse is an increasingly important issue to study given that sexually active young women are at risk of multiple outcomes including early pregnancies, vesico-vaginal fistula, and sexually transmitted infections. Much research has focused on the demographic, familial, and social factors associated with sexual initiation and reasons adolescents begin having consensual intercourse. Less is known, however, about the geographical and contextual factors associated with age of initiation of sexual intercourse. Therefore, the purpose of this study was to examine the extent of regional and state disparities in age of initiation of sexual intercourse and to examine individual- and community-level predictors of early sexual debut. Multilevel logistic regression models were applied to data on 5531 ever or currently married women who had participated in 2003 Nigeria Demographic and Health Survey. Coital debut at 15 years or younger was used to define early sexual debut. Exploratory spatial data analysis methods were used to study geographic variation in age at first sexual intercourse. The median age at first sexual intercourse for all women included in the study was 15 years (range; 14 - 19). North West and North East had the highest proportion of women who had reported early sexual debut (61% - 78%). The spatial distribution of age of initiation of sexual intercourse was nonrandom and clustered with a Moran's I = 0.635 (p = .001). There was significant positive spatial relationship between median age of marriage and spatial lag of median age of sexual debut (Bivariate Moran's I = 0.646, (p = .001). After adjusting for both individual-level and contextual factors, the probability of starting sex at an earlier age was associated with respondents' current age, education attainment, ethnicity, region, and community median age of marriage. The study found that individual-level and community contextual characteristics were independently associated with early sexual debut, suggesting that interventions to reduce adolescent high-risk sexual behaviour should focus on high-risk places as well as high-risk groups of people.

Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

PubMed Central

2012-01-01

Background In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. Methods We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. Results This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. Conclusion This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic. PMID:22708576

Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study.

PubMed

Chien, Lung-Chang; Lin, Ro-Ting; Liao, Yunqi; Sy, Francisco S; Pérez, Adriana

2018-04-17

Zika virus (ZIKV) infection is a pandemic and a public health emergency. It is transmitted by mosquitoes, primarily the Aedes genus. In light of no treatment currently, it is crucial to develop effective vector control programs to prevent the spread of ZIKV infection earlier when observing possible risk factors, such as weather conditions enhancing mosquito breeding and surviving. This study collected daily meteorological measurements and weekly ZIKV infectious cases among 32 departments of Colombia from January 2015-December 2016. This study applied the distributed lag nonlinear model to estimate the association between the number of ZIKA virus infection and meteorological measurements, controlling for spatial and temporal variations. We examined at most three meteorological factors with 20 lags in weeks in the model. Average humidity, total rainfall, and maximum temperature were more predictable of ZIKV infection outbreaks than other meteorological factors. Our models can detect significantly lagged effects of average humidity, total rainfall, and maximum temperature on outbreaks up to 15, 14, and 20 weeks, respectively. The spatial analysis identified 12 departments with a significant threat of ZIKV, and eight of those high-risk departments were located between the Equator and 6°N. The outbreak prediction also performed well in identified high-risk departments. Our results demonstrate that meteorological factors could be used for predicting ZIKV epidemics. Building an early warning surveillance system is important for preventing ZIKV infection, particularly in endemic areas.

Cognitive functioning is more closely related to real-life mobility than to laboratory-based mobility parameters.

PubMed

Giannouli, Eleftheria; Bock, Otmar; Zijlstra, Wiebren

2018-03-01

Increasing evidence indicates that mobility depends on cognitive resources, but the exact relationships between various cognitive functions and different mobility parameters still need to be investigated. This study examines the hypothesis that cognitive functioning is more closely related to real-life mobility performance than to mobility capacity as measured with standardized laboratory tests. The final sample used for analysis consisted of 66 older adults (72.3 ± 5.6 years). Cognition was assessed by measures of planning (HOTAP test), spatial working memory (Grid-Span test) and visuospatial attention (Attention Window test). Mobility capacity was assessed by an instrumented version of the Timed Up-and-Go test (iTUG). Mobility performance was assessed with smartphones which collected accelerometer and GPS data over one week to determine the spatial extent and temporal duration of real-life activities. Data analyses involved an exploratory factor analysis and correlation analyses. Mobility measures were reduced to four orthogonal factors: the factor 'real-life mobility' correlated significantly with most cognitive measures (between r   =  .229 and r   =  .396); factors representing 'sit-to-stand transition' and 'turn' correlated with fewer cognitive measures (between r   =  .271 and r   =  .315 and between r   =  .210 and r   =  .316, respectively), and the factor representing straight gait correlated with only one cognitive measure ( r   =  .237). Among the cognitive functions tested, visuospatial attention was associated with most mobility measures, executive functions with fewer and spatial working memory with only one mobility measure. Capacity and real-life performance represent different aspects of mobility. Real-life mobility is more closely associated with cognition than mobility capacity, and in our data this association is most pronounced for visuospatial attention. The close link between real-life mobility and visuospatial attention should be considered by interventions targeting mobility in old age.

Quantifying Fast and Slow Responses of Terrestrial Carbon Exchange across a Water Availability Gradient in North American Flux Sites

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Scott, R. L.; Goulden, M.

2014-12-01

Climate change is predicted to increase the frequency and severity of water limitation, altering terrestrial ecosystems and their carbon exchange with the atmosphere. Here we compare site-level temporal sensitivity of annual carbon fluxes to interannual variations in water availability against cross-site spatial patterns over a network of 19 eddy covariance flux sites. This network represents one order of magnitude in mean annual productivity and includes western North American desert shrublands and grasslands, savannahs, woodlands, and forests with continuous records of 4 to 12 years. Our analysis reveals site-specific patterns not identifiable in prior syntheses that pooled sites. We interpret temporal variability as an indicator of ecosystem response to annual water availability due to fast-changing factors such as leaf stomatal response and microbial activity, while cross-site spatial patterns are used to infer ecosystem adjustment to climatic water availability through slow-changing factors such as plant community and organic carbon pools. Using variance decomposition, we directly quantify how terrestrial carbon balance depends on slow- and fast-changing components of gross ecosystem production (GEP) and total ecosystem respiration (TER). Slow factors explain the majority of variance in annual net ecosystem production (NEP) across the dataset, and their relative importance is greater at wetter, forest sites than desert ecosystems. Site-specific offsets from spatial patterns of GEP and TER explain one third of NEP variance, likely due to slow-changing factors not directly linked to water, such as disturbance. TER and GEP are correlated across sites as previously shown, but our site-level analysis reveals surprisingly consistent linear relationships between these fluxes in deserts and savannahs, indicating fast coupling of TER and GEP in more arid ecosystems. Based on the uncertainty associated with slow and fast factors, we suggest a framework for improved prediction of terrestrial carbon balance. We will also present results of ongoing work to quantify fast and slow contributions to the relationship between evapotranspiration and precipitation across a precipitation gradient.

Spatiotemporal Patterns of the Use of Urban Green Spaces and External Factors Contributing to Their Use in Central Beijing

PubMed Central

Li, Fangzheng; Zhang, Fen; Li, Xiong; Wang, Peng; Liang, Junhui; Mei, Yuting; Cheng, Wenwen; Qian, Yun

2017-01-01

Urban green spaces encourage outdoor activity and social communication that contribute to the health of local residents. Examining the relationship between the use of urban green spaces and factors influencing their utilization can provide essential references for green space site selection in urban planning. In contrast to previous studies that focused on internal factors, this study highlights the external factors (traffic convenience, population density and commercial facilities) contributing to the use of urban green spaces. We conducted a spatiotemporal analysis of the distribution of visitors in 208 selected green spaces in central Beijing. We examined the relationship between the spatial pattern of visitor distribution within urban green spaces and external factors, using the Gini coefficient, kernel density estimation, and geographical detectors. The results of the study were as follows. The spatial distribution of visitors within central Beijing’s green spaces was concentrated, forming different agglomerations. The three examined external factors are all associated with the use of green spaces. Among them, commercial facilities are the important external factor associated with the use of green spaces. For the selection of sites for urban green spaces, we recommend consideration of external factors in order to balance urban green space utilization. PMID:28264451

Spatiotemporal Patterns of the Use of Urban Green Spaces and External Factors Contributing to Their Use in Central Beijing.

PubMed

Li, Fangzheng; Zhang, Fen; Li, Xiong; Wang, Peng; Liang, Junhui; Mei, Yuting; Cheng, Wenwen; Qian, Yun

2017-02-27

Urban green spaces encourage outdoor activity and social communication that contribute to the health of local residents. Examining the relationship between the use of urban green spaces and factors influencing their utilization can provide essential references for green space site selection in urban planning. In contrast to previous studies that focused on internal factors, this study highlights the external factors (traffic convenience, population density and commercial facilities) contributing to the use of urban green spaces. We conducted a spatiotemporal analysis of the distribution of visitors in 208 selected green spaces in central Beijing. We examined the relationship between the spatial pattern of visitor distribution within urban green spaces and external factors, using the Gini coefficient, kernel density estimation, and geographical detectors. The results of the study were as follows. The spatial distribution of visitors within central Beijing's green spaces was concentrated, forming different agglomerations. The three examined external factors are all associated with the use of green spaces. Among them, commercial facilities are the important external factor associated with the use of green spaces. For the selection of sites for urban green spaces, we recommend consideration of external factors in order to balance urban green space utilization.

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

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Factorial Kriging analysis and sources of heavy metals in soils of different land-use types in the Yangtze River Delta of Eastern China.

PubMed

Zhou, Jie; Feng, Ke; Li, Yinju; Zhou, Yang

2016-08-01

The objectives of this study are to analyse the pollution status and spatial correlation of soil heavy metals and identify natural and anthropogenic sources of these heavy metals at different spatial scales. Two hundred and twenty-four soil samples (0-20 cm) were collected and analysed for eight heavy metals (Cd, Hg, As, Cu, Pb, Cr, Zn and Ni) in soils of different land-use types in the Yangtze River Delta of Eastern China. The multivariate methods and factorial Kriging analysis were used to achieve the research objectives. The results indicated that the human and natural effects of different land-use types on the contents of soil heavy metals were different. The Cd, Hg, Cu, Pb and Zn in soils of industrial area were affected by human activities, and the pollution level of these heavy metals in this area was moderate. The Pb in soils of traffic area was affected by human activities, and eight heavy metals in soils of residential area and farmland area were affected by natural factor. The ecological risk status of eight heavy metals in soils of the whole study area was light. The heavy metals in soils showed three spatial scales (nugget effect, short range and long range). At the nugget effect and short range scales, the Cd, Hg, Cu, Pb and Zn in soils were affected by human and natural factors. At three spatial scales, the As, Cr and Ni in soils were affected by soil parent materials.

Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia.

PubMed

Schmidt, Thomas L; Filipović, Igor; Hoffmann, Ary A; Rašić, Gordana

2018-05-01

The endosymbiotic bacterium Wolbachia suppresses the capacity for arbovirus transmission in the mosquito Aedes aegypti, and can spread spatially through wild mosquito populations following local introductions. Recent introductions in Cairns, Australia have demonstrated slower than expected spatial spread. Potential reasons for this include: (i) barriers to Ae. aegypti dispersal; (ii) higher incidence of long-range dispersal; and (iii) intergenerational loss of Wolbachia. We investigated these three potential factors using genome-wide single-nucleotide polymorphisms (SNPs) and an assay for the Wolbachia infection wMel in 161 Ae. aegypti collected from Cairns in 2015. We detected a small but significant barrier effect of Cairns highways on Ae. aegypti dispersal using distance-based redundancy analysis and patch-based simulation analysis. We detected a pair of putative full-siblings in ovitraps 1312 m apart, indicating long-distance female movement likely mediated by human transport. We also found a pair of full-siblings of different infection status, indicating intergenerational loss of Wolbachia in the field. These three factors are all expected to contribute to the slow spread of Wolbachia through Ae. aegypti populations, though from our results it is unclear whether Wolbachia loss and long-distance movement are sufficiently common to reduce the speed of spatial spread appreciably. Our findings inform the strategic deployment of Wolbachia-infected mosquitoes during releases, and show how parameter estimates from laboratory studies may differ from those estimated using field data. Our landscape genomics approach can be extended to other host/symbiont systems that are being considered for biocontrol.

Smal-Scale Spatial Differences in Supply-Side Ecology of Barnacle Larvae Involves a Complex Suite of Factors (Including Surface Tide, Internal Tides And Surface Winds) in Baja California

NASA Astrophysics Data System (ADS)

Valencia, A.; Ladah, L. B.

2016-02-01

The objective of this study was to quantify and compare the daily settlement rate of barnacle larvae of Chthamalus spp. at small spatial scales ( 1 km) at three sites with unique geomorphology. Simultaneously, water-column temperature, currents, and coastal winds were measured to detect potential physical transport mechanisms responsible for supply of planktonic larvae to the coast. Autocorrelation artifacts in the environmental and settlement time series were removed with the Autoregressive Integrated Moving Average (ARIMA) and their residuals were used to perform a Principal Component Analysis (PCA). This analysis was carried out to determine the independent modes of variability in the environmental forcing mechanisms that may explain the settlement patterns. We found synchronous settlement pulses occurring throughout the study. Settlement at the wave exposed site was only associated to the wind-forcing mode and not to internal waves, which had not been detected previously and was surprising, considering the strong semidiurnal internal tide at this site. Settlement at both the reef-bounded site and the inside-bay site associated to vertical isotherm displacements, thereby suggesting the importance of internal waves for supply-side ecology at these more southern sites. Our results suggest that a complex suite of factors may interact to result in larval supply at the same site, and that larval supply at nearby sites may be forced by different factors due to differences in geomorphology and/or bathymetry, explaining spatial heterogeneity often detected in larval supply and settlement.

Influence of land use and meteorological factors on the spatial distribution of Toxocara canis and Toxocara cati eggs in soil in urban areas.

PubMed

Gao, Xiang; Wang, Hongbin; Li, Jianxin; Qin, Hongyu; Xiao, Jianhua

2017-01-15

Soil which has been contaminated by Toxocara spp. eggs is considered as one of the main infection sources of Toxocariasis in animals and humans. The present study conducted a detailed investigation into the spatial patterns of Toxocara canis (T. canis) and Toxocara cati (T. cati) eggs in soil in urban area of northeastern Mainland China, and assessed the inter-relationships between meteorological factors, land use and the distribution of the Toxocara spp. eggs. Polymerase chain reaction (PCR) was used for the determination of T. canis and T. cati eggs contamination in soil samples. Between April 2014 and May 2015, 9420 soil samples were subjected to PCR examination and 7027 sheep (74.6%) were determined to be positive for T. canis and T. cati eggs. Subsequently, we evaluated the effect of land use, and meteorological factors on the spatial distribution of T. canis and T. cati eggs based on a maximum entropy model. Jackknife analysis revealed that the area of residential land, wood and grass land and precipitation may influence the occurrence of T. canis and T. cati eggs in soil. Our findings indicate that land use and meteorological factors may be important variables affecting transmission of Toxocariasis and should be taken into account in the development of future surveillance programmes for Toxocariasis. Copyright © 2016 Elsevier B.V. All rights reserved.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

Characterization of a normal control group: are they healthy?

PubMed

Aine, C J; Sanfratello, L; Adair, J C; Knoefel, J E; Qualls, C; Lundy, S L; Caprihan, A; Stone, D; Stephen, J M

2014-01-01

We examined the health of a control group (18-81years) in our aging study, which is similar to control groups used in other neuroimaging studies. The current study was motivated by our previous results showing that one third of the elder control group had moderate to severe white matter hyperintensities and/or cortical volume loss which correlated with poor performance on memory tasks. Therefore, we predicted that cardiovascular risk factors (e.g., hypertension, high cholesterol) within the control group would account for significant variance on working memory task performance. Fifty-five participants completed 4 verbal and spatial working memory tasks, neuropsychological exams, diffusion tensor imaging (DTI), and blood tests to assess vascular risk. In addition to using a repeated measures ANOVA design, a cluster analysis was applied to the vascular risk measures as a data reduction step to characterize relationships between conjoint risk factors. The cluster groupings were used to predict working memory performance. The results show that higher levels of systolic blood pressure were associated with: 1) poor spatial working memory accuracy; and 2) lower fractional anisotropy (FA) values in multiple brain regions. In contrast, higher levels of total cholesterol corresponded with increased accuracy in verbal working memory. An association between lower FA values and higher cholesterol levels were identified in different brain regions from those associated with systolic blood pressure. The conjoint risk analysis revealed that Risk Cluster Group 3 (the group with the greatest number of risk factors) displayed: 1) the poorest performance on the spatial working memory tasks; 2) the longest reaction times across both spatial and verbal memory tasks; and 3) the lowest FA values across widespread brain regions. Our results confirm that a considerable range of vascular risk factors are present in a typical control group, even in younger individuals, which have robust effects on brain anatomy and function. These results present a new challenge to neuroimaging studies both for defining a cohort from which to characterize 'normative' brain circuitry and for establishing a control group to compare with other clinical populations. © 2013.

Collective behavior in the spatial spreading of obesity

PubMed Central

Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

2012-01-01

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies. PMID:22822425

Collective behavior in the spatial spreading of obesity

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

2012-06-01

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies.

Resilience of aging populations after devastating earthquake event and its determinants - A case study of the Chi-Chi earthquake in Taiwan

NASA Astrophysics Data System (ADS)

Hung, Chih-Hsuan; Hung, Hung-Chih

2016-04-01

1.Background Major portions of urban areas in Asia are highly exposed and vulnerable to devastating earthquakes. Many studies identify ways to reduce earthquake risk by concentrating more on building resilience for the particularly vulnerable populations. By 2020, as the United Nations' warning, many Asian countries would become 'super-aged societies', such as Taiwan. However, local authorities rarely use resilience approach to frame earthquake disaster risk management and land use strategies. The empirically-based research about the resilience of aging populations has also received relatively little attention. Thus, a challenge arisen for decision-makers is how to enhance resilience of aging populations within the context of risk reduction. This study aims to improve the understanding of the resilience of aging populations and its changes over time in the aftermath of a destructive earthquake at the local level. A novel methodology is proposed to assess the resilience of aging populations and to characterize their changes of spatial distribution patterns, as well as to examine their determinants. 2.Methods and data An indicator-based assessment framework is constructed with the goal of identifying composite indicators (including before, during and after a disaster) that could serve as proxies for attributes of the resilience of aging populations. Using the recovery process of the Chi-Chi earthquake struck central Taiwan in 1999 as a case study, we applied a method combined a geographical information system (GIS)-based spatial statistics technique and cluster analysis to test the extent of which the resilience of aging populations is spatially autocorrelated throughout the central Taiwan, and to explain why clustering of resilient areas occurs in specific locations. Furthermore, to scrutinize the affecting factors of resilience, we develop an aging population resilience model (APRM) based on existing resilience theory. Using the APRM, we applied a multivariate regression analysis to identify and examine how various factors connect to the resilience of aging populations. To illustrate the proposed methodology, the study collected data on the resilience attributes, the disaster impacts and damages due to the Chi-Chi earthquake. The data were offered by the National Science and Technology Center for Disaster Reduction, Taiwan, as well as collected from the National Land Use Investigation, official census statistics and questionnaire surveys. 3.Results Integrating cluster analysis with GIS-based spatial statistical analysis, the resilience of aging populations were divided into five clusters of distribution patterns over the 10 years after the Chi-Chi earthquake. It shows that both population and elderly distributions were highly heterogeneous and spatial correlated across the study areas. We also demonstrated the 'hot spots' areas of the highly concentrated aging population across central Taiwan. Results of regression analysis disclosed the major factors that caused low resilience and changes of aging population distributions over time. These factors included the levels of seismic damage, infrastructure investments, as well as the land-use and socioeconomic attributes associated with the disaster areas. Finally, our findings provide stakeholders and policy-makers with better adaptive options to design and synthesize appropriate patchworks of planning measures for different types of resilience areas to reduce earthquake disaster risk.

On Bi-Grid Local Mode Analysis of Solution Techniques for 3-D Euler and Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

Ibraheem, S. O.; Demuren, A. O.

1994-01-01

A procedure is presented for utilizing a bi-grid stability analysis as a practical tool for predicting multigrid performance in a range of numerical methods for solving Euler and Navier-Stokes equations. Model problems based on the convection, diffusion and Burger's equation are used to illustrate the superiority of the bi-grid analysis as a predictive tool for multigrid performance in comparison to the smoothing factor derived from conventional von Neumann analysis. For the Euler equations, bi-grid analysis is presented for three upwind difference based factorizations, namely Spatial, Eigenvalue and Combination splits, and two central difference based factorizations, namely LU and ADI methods. In the former, both the Steger-Warming and van Leer flux-vector splitting methods are considered. For the Navier-Stokes equations, only the Beam-Warming (ADI) central difference scheme is considered. In each case, estimates of multigrid convergence rates from the bi-grid analysis are compared to smoothing factors obtained from single-grid stability analysis. Effects of grid aspect ratio and flow skewness are examined. Both predictions are compared with practical multigrid convergence rates for 2-D Euler and Navier-Stokes solutions based on the Beam-Warming central scheme.

Assessing regional environmental quality by integrated use of remote sensing, GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration.

PubMed

Rahman, Md Rejaur; Shi, Z H; Chongfa, Cai

2014-11-01

This study was an attempt to analyse the regional environmental quality with the application of remote sensing, geographical information system, and spatial multiple criteria decision analysis and, to project a quantitative method applicable to identify the status of the regional environment of the study area. Using spatial multi-criteria evaluation (SMCE) approach with expert knowledge in this study, an integrated regional environmental quality index (REQI) was computed and classified into five levels of regional environment quality viz. worse, poor, moderate, good, and very good. During the process, a set of spatial criteria were selected (here, 15 criterions) together with the degree of importance of criteria in sustainability of the regional environment. Integrated remote sensing and GIS technique and models were applied to generate the necessary factors (criterions) maps for the SMCE approach. The ranking, along with expected value method, was used to standardize the factors and on the other hand, an analytical hierarchy process (AHP) was applied for calculating factor weights. The entire process was executed in the integrated land and water information system (ILWIS) software tool that supports SMCE. The analysis showed that the overall regional environmental quality of the area was at moderate level and was partly determined by elevation. Areas under worse and poor quality of environment indicated that the regional environmental status showed decline in these parts of the county. The study also revealed that the human activities, vegetation condition, soil erosion, topography, climate, and soil conditions have serious influence on the regional environment condition of the area. Considering the regional characteristics of environmental quality, priority, and practical needs for environmental restoration, the study area was further regionalized into four priority areas which may serve as base areas of decision making for the recovery, rebuilding, and protection of the environment.

Addressing uncertainty in modelling cumulative impacts within maritime spatial planning in the Adriatic and Ionian region.

PubMed

Gissi, Elena; Menegon, Stefano; Sarretta, Alessandro; Appiotti, Federica; Maragno, Denis; Vianello, Andrea; Depellegrin, Daniel; Venier, Chiara; Barbanti, Andrea

2017-01-01

Maritime spatial planning (MSP) is envisaged as a tool to apply an ecosystem-based approach to the marine and coastal realms, aiming at ensuring that the collective pressure of human activities is kept within acceptable limits. Cumulative impacts (CI) assessment can support science-based MSP, in order to understand the existing and potential impacts of human uses on the marine environment. A CI assessment includes several sources of uncertainty that can hinder the correct interpretation of its results if not explicitly incorporated in the decision-making process. This study proposes a three-level methodology to perform a general uncertainty analysis integrated with the CI assessment for MSP, applied to the Adriatic and Ionian Region (AIR). We describe the nature and level of uncertainty with the help of expert judgement and elicitation to include all of the possible sources of uncertainty related to the CI model with assumptions and gaps related to the case-based MSP process in the AIR. Next, we use the results to tailor the global uncertainty analysis to spatially describe the uncertainty distribution and variations of the CI scores dependent on the CI model factors. The results show the variability of the uncertainty in the AIR, with only limited portions robustly identified as the most or the least impacted areas under multiple model factors hypothesis. The results are discussed for the level and type of reliable information and insights they provide to decision-making. The most significant uncertainty factors are identified to facilitate the adaptive MSP process and to establish research priorities to fill knowledge gaps for subsequent planning cycles. The method aims to depict the potential CI effects, as well as the extent and spatial variation of the data and scientific uncertainty; therefore, this method constitutes a suitable tool to inform the potential establishment of the precautionary principle in MSP.

Identifying the principal driving factors of water ecosystem dependence and the corresponding indicator species in a pilot City, China

NASA Astrophysics Data System (ADS)

Zhao, C. S.; Shao, N. F.; Yang, S. T.; Xiang, H.; Lou, H. Z.; Sun, Y.; Yang, Z. Y.; Zhang, Y.; Yu, X. Y.; Zhang, C. B.; Yu, Q.

2018-01-01

The world's aquatic ecosystems yield numerous vital services, which are essential to human existence but have deteriorated seriously in recent years. By studying the mechanisms of interaction between ecosystems and habitat processes, the constraining factors can be identified, and this knowledge can be used to improve the success rate of ecological restoration initiatives. At present, there is insufficient data on the link between hydrological, water quality factors and the changes in the structure of aquatic communities to allow any meaningful study of driving factors of aquatic ecosystems. In this study, the typical monitoring stations were selected by fuzzy clustering analysis based on the spatial and temporal distribution characteristics of water ecology in Jinan City, the first pilot city for the construction of civilized aquatic ecosystems in China. The dominant species identification model was used to identify the dominant species of the aquatic community. The driving effect of hydrological and water quality factors on dominant species was analyzed by Canonical Correspondence Analysis. Then, the principal factors of aquatic ecosystem dependence were selected. The results showed that there were 10 typical monitoring stations out of 59 monitoring sites, which were representative of aquatic ecosystems, 9 dominant fish species, and 20 dominant invertebrate species. The selection of factors for aquatic ecosystem dependence in Jinan were highly influenced by its regional conditions. Chemical environmental parameters influence the temporal and spatial variation of invertebrate much more than that of fish in Jinan City. However, the methodologies coupling typical monitoring stations selection, dominant species determination and driving factors identification were certified to be a cost-effective way, which can provide in-deep theoretical and technical directions for the restoration of aquatic ecosystems elsewhere.

Visuo-spatial ability in colonoscopy simulator training.

PubMed

Luursema, Jan-Maarten; Buzink, Sonja N; Verwey, Willem B; Jakimowicz, J J

2010-12-01

Visuo-spatial ability is associated with a quality of performance in a variety of surgical and medical skills. However, visuo-spatial ability is typically assessed using Visualization tests only, which led to an incomplete understanding of the involvement of visuo-spatial ability in these skills. To remedy this situation, the current study investigated the role of a broad range of visuo-spatial factors in colonoscopy simulator training. Fifteen medical trainees (no clinical experience in colonoscopy) participated in two psycho-metric test sessions to assess four visuo-spatial ability factors. Next, participants trained flexible endoscope manipulation, and navigation to the cecum on the GI Mentor II simulator, for four sessions within 1 week. Visualization, and to a lesser degree Spatial relations were the only visuo-spatial ability factors to correlate with colonoscopy simulator performance. Visualization additionally covaried with learning rate for time on task on both simulator tasks. High Visualization ability indicated faster exercise completion. Similar to other endoscopic procedures, performance in colonoscopy is positively associated with Visualization, a visuo-spatial ability factor characterized by the ability to mentally manipulate complex visuo-spatial stimuli. The complexity of the visuo-spatial mental transformations required to successfully perform colonoscopy is likely responsible for the challenging nature of this technique, and should inform training- and assessment design. Long term training studies, as well as studies investigating the nature of visuo-spatial complexity in this domain are needed to better understand the role of visuo-spatial ability in colonoscopy, and other endoscopic techniques.

Spatial patterns of the congenital heart disease prevalence among 0- to 14-year-old children in Sichuan Basin, P. R China, from 2004 to 2009

PubMed Central

2014-01-01

Background Congenital heart disease (CHD) is the most common type of major birth defects in Sichuan, the most populous province in China. The detailed etiology of CHD is unknown but some environmental factors are suspected as the cause of this disease. However, the geographical variations in CHD prevalence would be highly valuable in providing a clue on the role of the environment in CHD etiology. Here, we investigate the spatial patterns and geographic differences in CHD prevalence among 0- to 14-year-old children, discuss the possible environmental risk factors that might be associated with CHD prevalence in Sichuan Basin from 2004 to 2009. Methods The hierarchical Bayesian model was used to estimate CHD prevalence at the township level. Spatial autocorrelation statistics were performed, and a hot-spot analysis with different distance thresholds was used to identify the spatial pattern of CHD prevalence. Distribution and clustering maps were drawn using geographic information system tools. Results CHD prevalence was significantly clustered in Sichuan Basin in different spatial scale. Typical hot/cold clusters were identified, and possible CHD causes were discussed. The association between selected hypothetical environmental factors of maternal exposure and CHD prevalence was evaluated. Conclusions The largest hot-spot clustering phenomena and the CHD prevalence clustering trend among 0- to 14-year-old children in the study area showed a plausibly close similarity with those observed in the Tuojiang River Basin. The high ecological risk of heavy metal(Cd, As, and Pb)sediments in the middle and lower streams of the Tuojiang River watershed and ammonia–nitrogen pollution may have contribution to the high prevalence of CHD in this area. PMID:24924350

Distribution patterns in the native vascular flora of Iceland.

PubMed

Wasowicz, Pawel; Pasierbiński, Andrzej; Przedpelska-Wasowicz, Ewa Maria; Kristinsson, Hörður

2014-01-01

The aim of our study was to reveal biogeographical patterns in the native vascular flora of Iceland and to define ecological factors responsible for these patterns. We analysed dataset of more than 500,000 records containing information on the occurrence of vascular plants. Analysis of ecological factors included climatic (derived from WORLDCLIM data), topographic (calculated from digital elevation model) and geological (bedrock characteristics) variables. Spherical k-means clustering and principal component analysis were used to detect biogeographical patterns and to study the factors responsible for them. We defined 10 biotic elements exhibiting different biogeographical patterns. We showed that climatic (temperature-related) and topographic variables were the most important factors contributing to the spatial patterns within the Icelandic vascular flora and that these patterns are almost completely independent of edaphic factors (bedrock type). Our study is the first one to analyse the biogeographical differentiation of the native vascular flora of Iceland.

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model

PubMed Central

Wu, Liang; Deng, Fei; Xie, Zhong; Hu, Sheng; Shen, Shu; Shi, Junming; Liu, Dan

2016-01-01

Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010–2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies. PMID:27845737

Estimates of spatial and temporal variation of energy crops biomass yields in the US

NASA Astrophysics Data System (ADS)

Song, Y.; Jain, A. K.; Landuyt, W.; Kheshgi, H. S.

2013-12-01

Perennial grasses, such as switchgrass (Panicum viragatum) and Miscanthus (Miscanthus x giganteus) have been identified for potential use as biomass feedstocks in the US. Current research on perennial grass biomass production has been evaluated on small-scale plots. However, the extent to which this potential can be realized at a landscape-scale will depend on the biophysical potential to grow these grasses with minimum possible amount of land that needs to be diverted from food to fuel production. To assess this potential three questions about the biomass yield for these grasses need to be answered: (1) how the yields for different grasses are varied spatially and temporally across the US; (2) whether the yields are temporally stable or not; and (3) how the spatial and temporal trends in yields of these perennial grasses are controlled by limiting factors, including soil type, water availability, climate, and crop varieties. To answer these questions, the growth processes of the perennial grasses are implemented into a coupled biophysical, physiological and biogeochemical model (ISAM). The model has been applied to quantitatively investigate the spatial and temporal trends in biomass yields for over the period 1980 -2010 in the US. The bioenergy grasses considered in this study include Miscanthus, Cave-in-Rock switchgrass and Alamo switchgrass. The effects of climate, soil and topography on the spatial and temporal trends of biomass yields are quantitatively analyzed using principal component analysis and GIS based geographically weighted regression. The spatial temporal trend results are evaluated further to classify each part of the US into four homogeneous potential yield zones: high and stable yield zone (HS), high but unstable yield zone (HU), low and stable yield zone (LS) and low but unstable yield zone (LU). Our preliminary results indicate that the yields for perennial grasses among different zones are strongly related to the different controlling factors. For example, the yield in HS zone is depended on soil and topography factors. However, the yields in HU zone are more controlled by climate factors, leading to a large uncertainty in yield potential of bioenergy grasses under future climate change.

Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model.

PubMed

Paul, Mathilde; Tavornpanich, Saraya; Abrial, David; Gasqui, Patrick; Charras-Garrido, Myriam; Thanapongtharm, Weerapong; Xiao, Xiangming; Gilbert, Marius; Roger, Francois; Ducrot, Christian

2010-01-01

Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the "second wave" of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1. To limit the spread of future outbreaks, efforts to control the movement of poultry products must be sustained. INRA, EDP Sciences, 2010.

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

PubMed

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

2011-01-01

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

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

Spatial vulnerability assessments by regression kriging

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

2016-04-01

Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

The estimation of probable maximum precipitation: the case of Catalonia.

PubMed

Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel

2008-12-01

A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

Spatial Variation of Surface Soil Available Phosphorous and Its Relation with Environmental Factors in the Chaohu Lake Watershed

PubMed Central

Gao, Yongnian; Gao, Junfeng; Chen, Jiongfeng

2011-01-01

The study presented in this paper attempts to evaluate the spatial pattern of soil available phosphorus, as well as the relation between soil available phosphorus and environment factors including elevation, slope, precipitation, percentage of cultivated land, percentage of forest land, percentage of construction land and NDVI using statistical methods and GIS spatial analysis techniques. The results showed that the Spline Tension method performed the best in the prediction of soil available phosphorus in the Chaohu Lake watershed. The spatial variation of surface soil available phosphorus was high in Chaohu Lake watershed and the upstream regions around Chaohu Lake, including the west of Chaohu lake (e.g., southwest of Feixi county, east of Shucheng county and north of Lujiang county) and to the north of Chaohu Lake (e.g., south of Hefei city, south of Feidong county, southwest of Juchao district), had the highest soil available phosphorus content. The mean and standard deviation of soil available phosphorus content gradually decreased as the elevation or slope increased. The cultivated land comprised 60.11% of the watershed and of that land 65.63% belonged to the medium to very high SAP level classes, and it played a major role in SAP availability within the watershed and a potential source of phosphorus to Chaohu Lake resulting in eutrophication. Among the land use types, paddy fields have some of the highest maximum values and variation of coefficients. Subwatershed scale soil available phosphorus was significantly affected by elevation, slope, precipitation, percentage of cultivated land and percentage of forest land and was decided by not only these environmental factors but also some other factors such as artificial phosphorus fertilizer application. PMID:21909308

Geospatial data to support analysis of water-quality conditions in basin-fill aquifers in the southwestern United States

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2009-01-01

The Southwest Principal Aquifers study area consists of most of California and Nevada and parts of Utah, Arizona, New Mexico, and Colorado; it is about 409,000 square miles. The Basin-fill aquifers extend through about 201,000 square miles of the study area and are the primary source of water for cities and agricultural communities in basins in the arid and semiarid southwestern United States (Southwest). The demand on limited ground-water resources in areas in the southwestern United States has increased significantly. This increased demand underscores the importance of understanding factors that affect the water quality in basin-fill aquifers in the region, which are being studied through the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. As a part of this study, spatial datasets of natural and anthropogenic factors that may affect ground-water quality of the basin-fill aquifers in the southwestern United States were developed. These data include physical characteristics of the region, such as geology, elevation, and precipitation, as well as anthropogenic factors, including population, land use, and water use. Spatial statistics for the alluvial basins in the Southwest have been calculated using the datasets. This information provides a foundation for the development of conceptual and statistical models that relate natural and anthropogenic factors to ground-water quality across the Southwest. A geographic information system (GIS) was used to determine and illustrate the spatial distribution of these basin-fill variables across the region. One hundred-meter resolution raster data layers that represent the spatial characteristics of the basins' boundaries, drainage areas, population densities, land use, and water use were developed for the entire Southwest.

Geographic Health's Way to Prevention of Diseases: A Case Study on Arsenic Spatial Dispersion and Dyspnea in Isfahan Province

PubMed Central

Rashidi, Maasoume; Poursafa, Parinaz

2014-01-01

Background: As geographic science discusses the analysis of environment, human beings and their mutual relations, thus the field of medical geography consists of being inspired from the relations between these two factors, analyzing environmental factors, their identification them and the state of their effects on human health, as well as determining the location of these factors. Some hazards that threat human health are the results of environmental factors and the relevant pollutions. Some important categories of diseases including (Shortness of Breath or, Dyspnea) have considerable differences in various places, as observed in their spatial prevalence and distribution maps. Methods: The record of patients with Dyspnea diseases were prepared for this descriptive research, for the period of 2009-2011, from the provincial health center, with the questionnaires were excluded patients with a family history of disease and the spatial diagram for disease prevalence was drawn according to the prepared data. The arsenic geographical distribution diagram in Isfahan province was also prepared and then the relation between an element of Arsenic in the province and the Dyspnea diseases were analyzed. Results: The analyses showed that the highest rate of Arsenic is entered the soil via fertilizers to come eventually into the food cycle of humans. By analyzing the amount of used fertilizers in Isfahan province and the dispersion diagram of Arsenic in Isfahan province, it was found that the highest frequency of Arsenic is in places having agricultural base. The spatial dispersion of Dyspnea diseases also showed that the spreading of Dyspnea diseases is greater in places with higher scale of Arsenic. Conclusions: This study is a logical justification between the two diagrams to confirm the hypothesis regarding the effect of arsenic on Dyspnea. PMID:25538832

Relative Importance of Biotic and Abiotic Forces on the Composition and Dynamics of a Soft-Sediment Intertidal Community

PubMed Central

Barbeau, Myriam A.

2016-01-01

Top-down, bottom-up, middle-out and abiotic factors are usually viewed as main forces structuring biological communities, although assessment of their relative importance, in a single study, is rarely done. We quantified, using multivariate methods, associations between abiotic and biotic (top-down, bottom-up and middle-out) variables and infaunal population/community variation on intertidal mudflats in the Bay of Fundy, Canada, over two years. Our analysis indicated that spatial structural factors like site and plot accounted for most of the community and population variation. Although we observed a significant relationship between the community/populations and the biotic and abiotic variables, most were of minor importance relative to the structural factors. We suggest that community and population structure were relatively uncoupled from the structuring influences of biotic and abiotic factors in this system because of high concentrations of resources that sustain high densities of infauna and limit exploitative competition. Furthermore, we hypothesize that the infaunal community primarily reflects stochastic spatial events, namely a “first come, first served” process. PMID:26790098

Factor analysis as a tool for spectral line component separation 21cm emission in the direction of L1780

NASA Technical Reports Server (NTRS)

Toth, L. V.; Mattila, K.; Haikala, L.; Balazs, L. G.

1992-01-01

The spectra of the 21cm HI radiation from the direction of L1780, a small high-galactic latitude dark/molecular cloud, were analyzed by multivariate methods. Factor analysis was performed on HI (21cm) spectra in order to separate the different components responsible for the spectral features. The rotated, orthogonal factors explain the spectra as a sum of radiation from the background (an extended HI emission layer), and from the L1780 dark cloud. The coefficients of the cloud-indicator factors were used to locate the HI 'halo' of the molecular cloud. Our statistically derived 'background' and 'cloud' spectral profiles, as well as the spatial distribution of the HI halo emission distribution were compared to the results of a previous study which used conventional methods analyzing nearly the same data set.

Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zu, Jiaxing; Zhang, Yangjian; Huang, Ke; Liu, Yaojie; Chen, Ning; Cong, Nan

2018-07-01

Climate change is receiving mounting attentions from various fields and phenology is a commonly used indicator signaling vegetation responses to climate change. Previous phenology studies have mostly focused on vegetation greening-up and its climatic driving factors, while autumn phenology has been barely touched upon. In this study, vegetation phenological metrics were extracted from MODIS NDVI data and their temporal and spatial patterns were explored on the Tibetan Plateau (TP). The results showed that the start of season (SOS) has significantly earlier trend in the first decade, while the end of season (EOS) has slightly (not significant) earlier trend. In the spatial dimension, similar patterns were also identified. The SOS plays a more significant role in regulating vegetation growing season length than EOS does. The EOS and driving effects from each factor exhibited spatially heterogeneous patterns. Biological factor is the dominant factor regulating the spatial pattern of EOS, while climate factors control its inter-annual variation.

Using solute and heat tracers for aquifer characterization in a strongly heterogeneous alluvial aquifer

NASA Astrophysics Data System (ADS)

Sarris, Theo S.; Close, Murray; Abraham, Phillip

2018-03-01

A test using Rhodamine WT and heat as tracers, conducted over a 78 day period in a strongly heterogeneous alluvial aquifer, was used to evaluate the utility of the combined observation dataset for aquifer characterization. A highly parameterized model was inverted, with concentration and temperature time-series as calibration targets. Groundwater heads recorded during the experiment were boundary dependent and were ignored during the inversion process. The inverted model produced a high resolution depiction of the hydraulic conductivity and porosity fields. Statistical properties of these fields are in very good agreement with estimates from previous studies at the site. Spatially distributed sensitivity analysis suggests that both solute and heat transport were most sensitive to the hydraulic conductivity and porosity fields and less sensitive to dispersivity and thermal distribution factor, with sensitivity to porosity greatly reducing outside the monitored area. The issues of model over-parameterization and non-uniqueness are addressed through identifiability analysis. Longitudinal dispersivity and thermal distribution factor are highly identifiable, however spatially distributed parameters are only identifiable near the injection point. Temperature related density effects became observable for both heat and solute, as the temperature anomaly increased above 12 degrees centigrade, and affected down gradient propagation. Finally we demonstrate that high frequency and spatially dense temperature data cannot inform a dual porosity model in the absence of frequent solute concentration measurements.

Geographic information analysis and web-based geoportals to explore malnutrition in Sub-Saharan Africa: a systematic review of approaches.

PubMed

Marx, Sabrina; Phalkey, Revati; Aranda-Jan, Clara B; Profe, Jörn; Sauerborn, Rainer; Höfle, Bernhard

2014-11-20

Childhood malnutrition is a serious challenge in Sub-Saharan Africa (SSA) and a major underlying cause of death. It is the result of a dynamic and complex interaction between political, social, economic, environmental and other factors. As spatially oriented research has been established in health sciences in recent years, developments in Geographic Information Science (GIScience) provide beneficial tools to get an improved understanding of malnutrition. In order to assess the current state of knowledge regarding the use of geoinformation analyses for exploring malnutrition in SSA, a systematic literature review of peer-reviewed literature is conducted using Scopus, ISI Web of Science and PubMed. As a supplement to the review, we carry on to investigate the establishment of web-based geoportals for providing freely accessible malnutrition geodata to a broad community. Based on these findings, we identify current limitations and discuss how new developments in GIScience might help to overcome impending barriers. 563 articles are identified from the searches, from which a total of nine articles and eight geoportals meet inclusion criteria. The review suggests that the spatial dimension of malnutrition is analyzed most often at the regional and national level using geostatistical analysis methods. Therefore, heterogeneous geographic information at different spatial scales and from multiple sources is combined by applying geoinformation analysis methods such as spatial interpolation, aggregation and downscaling techniques. Geocoded malnutrition data from the Demographic and Health Survey Program are the most common information source to quantify the prevalence of malnutrition on a local scale and are frequently combined with regional data on climate, population, agriculture and/or infrastructure. Only aggregated geoinformation about malnutrition prevalence is freely accessible, mostly displayed via web map visualizations or downloadable map images. The lack of detailed geographic data at household and local level is a major limitation for an in-depth assessment of malnutrition and links to potential impact factors. We propose that the combination of malnutrition-related studies with most recent GIScience developments such as crowd-sourced geodata collection, (web-based) interoperable spatial health data infrastructures as well as (dynamic) information fusion approaches are beneficial to deepen the understanding of this complex phenomenon.

Evaluation of comprehensive environmental effect about coastal zone development activities in Liaoning Province and management advice.

PubMed

Wang, Wei-Wei; Cai, Yue-Yin; Sun, Yong-Guang; Ma, Hong-Wei

2015-07-01

Using spatial analysis function of Arcgis software, the present study investigated the building environment impact evaluation index system of coastal development in Liaoning Province. The factors of it included of current state of environmental quality, environmental impact of marine development and marine environmental disaster. Weighted factor analysis and comprehensive index method were utilized. At the end, comprehensive environment effect of coastal development in Liaoning Province were evaluated successfully. The result showed that the environmental effect of development activity were most serious, along the Zhao Jiatun coast in north of Zhimao bay and coast of Mianhua island in Dalian bay.

Capturing spatial heterogeneity of soil organic carbon under changing climate

NASA Astrophysics Data System (ADS)

Mishra, U.; Fan, Z.; Jastrow, J. D.; Matamala, R.; Vitharana, U.

2015-12-01

The spatial heterogeneity of the land surface affects water, energy, and greenhouse gas exchanges with the atmosphere. Designing observation networks that capture land surface spatial heterogeneity is a critical scientific challenge. Here, we present a geospatial approach to capture the existing spatial heterogeneity of soil organic carbon (SOC) stocks across Alaska, USA. We used the standard deviation of 556 georeferenced SOC profiles previously compiled in Mishra and Riley (2015, Biogeosciences, 12:3993-4004) to calculate the number of observations that would be needed to reliably estimate Alaskan SOC stocks. This analysis indicated that 906 randomly distributed observation sites would be needed to quantify the mean value of SOC stocks across Alaska at a confidence interval of ± 5 kg m-2. We then used soil-forming factors (climate, topography, land cover types, surficial geology) to identify the locations of appropriately distributed observation sites by using the conditioned Latin hypercube sampling approach. Spatial correlation and variogram analyses demonstrated that the spatial structures of soil-forming factors were adequately represented by these 906 sites. Using the spatial correlation length of existing SOC observations, we identified 484 new observation sites would be needed to provide the best estimate of the present status of SOC stocks in Alaska. We then used average decadal projections (2020-2099) of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change to investigate whether the location of identified observation sites will shift/change under future climate. Our results showed 12-41 additional observation sites (depending on emission scenarios) will be required to capture the impact of projected climatic conditions by 2100 on the spatial heterogeneity of Alaskan SOC stocks. Our results represent an ideal distribution of observation sites across Alaska that captures the land surface spatial heterogeneity and can be used in efforts to quantify SOC stocks, monitor greenhouse gas emissions, and benchmark Earth System Model results.

Transport and spatial energy deposition of relativistic electrons in copper-doped fast ignition plasmas

DOE PAGES

Jarrott, L. C.; McGuffey, C.; Beg, F. N.; ...

2017-10-24

Fast electron transport and spatial energy deposition are investigated in integrated cone-guided Fast Ignition experiments by measuring fast electron induced copper K-shell emission using a copper tracer added to deuterated plastic shells with a geometrically reentrant gold cone. Experiments were carried out at the Laboratory for Laser Energetics on the OMEGA/OMEGA-EP Laser where the plastic shells were imploded using 54 of the 60 OMEGA60 beams (3ω, 20 kJ), while the high intensity OMEGA-EP (BL2) beam (1 ω, 10 ps, 500 J, I peak > 10 19 W/cm 2) was focused onto the inner cone tip. Here, a retrograde analysis usingmore » the hybrid-PIC electron transport code, ZUMA, is performed to examine the sensitivity of the copper Kα spatial profile on the laser-produced fast electrons, facilitating the optimization of new target point designs and laser configurations to improve the compressed core areal density by a factor of 4 and the fast electron energy coupling by a factor of 3.5.« less

Spatiotemporal variation of bacterial community composition and possible controlling factors in tropical shallow lagoons.

PubMed

Laque, Thaís; Farjalla, Vinicius F; Rosado, Alexandre S; Esteves, Francisco A

2010-05-01

Bacterial community composition (BCC) has been extensively related to specific environmental conditions. Tropical coastal lagoons present great temporal and spatial variation in their limnological conditions, which, in turn, should influence the BCC. Here, we sought for the limnological factors that influence, in space and time, the BCC in tropical coastal lagoons (Rio de Janeiro State, Brazil). The Visgueiro lagoon was sampled monthly for 1 year and eight lagoons were sampled once for temporal and spatial analysis, respectively. BCC was evaluated by bacteria-specific PCR-DGGE methods. Great variations were observed in limnological conditions and BCC on both temporal and spatial scales. Changes in the BCC of Visgueiro lagoon throughout the year were best related to salinity and concentrations of NO (3) (-) , dissolved phosphorus and chlorophyll-a, while changes in BCC between lagoons were best related to salinity and dissolved phosphorus concentration. Salinity has a direct impact on the integrity of the bacterial cell, and it was previously observed that phosphorus is the main limiting nutrient to bacterial growth in these lagoons. Therefore, we conclude that great variations in limnological conditions of coastal lagoons throughout time and space resulted in different BCCs and salinity and nutrient concentration, particularly dissolved phosphorus, are the main limnological factors influencing BCC in these tropical coastal lagoons.

Approximation and spatial regionalization of rainfall erosivity based on sparse data in a mountainous catchment of the Yangtze River in Central China.

PubMed

Schönbrodt-Stitt, Sarah; Bosch, Anna; Behrens, Thorsten; Hartmann, Heike; Shi, Xuezheng; Scholten, Thomas

2013-10-01

In densely populated countries like China, clean water is one of the most challenging issues of prospective politics and environmental planning. Water pollution and eutrophication by excessive input of nitrogen and phosphorous from nonpoint sources is mostly linked to soil erosion from agricultural land. In order to prevent such water pollution by diffuse matter fluxes, knowledge about the extent of soil loss and the spatial distribution of hot spots of soil erosion is essential. In remote areas such as the mountainous regions of the upper and middle reaches of the Yangtze River, rainfall data are scarce. Since rainfall erosivity is one of the key factors in soil erosion modeling, e.g., expressed as R factor in the Revised Universal Soil Loss Equation model, a methodology is needed to spatially determine rainfall erosivity. Our study aims at the approximation and spatial regionalization of rainfall erosivity from sparse data in the large (3,200 km(2)) and strongly mountainous catchment of the Xiangxi River, a first order tributary to the Yangtze River close to the Three Gorges Dam. As data on rainfall were only obtainable in daily records for one climate station in the central part of the catchment and five stations in its surrounding area, we approximated rainfall erosivity as R factors using regression analysis combined with elevation bands derived from a digital elevation model. The mean annual R factor (R a) amounts for approximately 5,222 MJ mm ha(-1) h(-1) a(-1). With increasing altitudes, R a rises up to maximum 7,547 MJ mm ha(-1) h(-1) a(-1) at an altitude of 3,078 m a.s.l. At the outlet of the Xiangxi catchment erosivity is at minimum with approximate R a=1,986 MJ mm ha(-1) h(-1) a(-1). The comparison of our results with R factors from high-resolution measurements at comparable study sites close to the Xiangxi catchment shows good consistance and allows us to calculate grid-based R a as input for a spatially high-resolution and area-specific assessment of soil erosion risk.

Spatial and temporal variance in fatty acid and stable isotope signatures across trophic levels in large river systems

USGS Publications Warehouse

Fritts, Andrea; Knights, Brent C.; Lafrancois, Toben D.; Bartsch, Lynn; Vallazza, Jon; Bartsch, Michelle; Richardson, William B.; Karns, Byron N.; Bailey, Sean; Kreiling, Rebecca

2018-01-01

Fatty acid and stable isotope signatures allow researchers to better understand food webs, food sources, and trophic relationships. Research in marine and lentic systems has indicated that the variance of these biomarkers can exhibit substantial differences across spatial and temporal scales, but this type of analysis has not been completed for large river systems. Our objectives were to evaluate variance structures for fatty acids and stable isotopes (i.e. δ13C and δ15N) of seston, threeridge mussels, hydropsychid caddisflies, gizzard shad, and bluegill across spatial scales (10s-100s km) in large rivers of the Upper Mississippi River Basin, USA that were sampled annually for two years, and to evaluate the implications of this variance on the design and interpretation of trophic studies. The highest variance for both isotopes was present at the largest spatial scale for all taxa (except seston δ15N) indicating that these isotopic signatures are responding to factors at a larger geographic level rather than being influenced by local-scale alterations. Conversely, the highest variance for fatty acids was present at the smallest spatial scale (i.e. among individuals) for all taxa except caddisflies, indicating that the physiological and metabolic processes that influence fatty acid profiles can differ substantially between individuals at a given site. Our results highlight the need to consider the spatial partitioning of variance during sample design and analysis, as some taxa may not be suitable to assess ecological questions at larger spatial scales.

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

USGS Publications Warehouse

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

Executive Function, Visual Attention and the Cocktail Party Problem in Musicians and Non-Musicians.

PubMed

Clayton, Kameron K; Swaminathan, Jayaganesh; Yazdanbakhsh, Arash; Zuk, Jennifer; Patel, Aniruddh D; Kidd, Gerald

2016-01-01

The goal of this study was to investigate how cognitive factors influence performance in a multi-talker, "cocktail-party" like environment in musicians and non-musicians. This was achieved by relating performance in a spatial hearing task to cognitive processing abilities assessed using measures of executive function (EF) and visual attention in musicians and non-musicians. For the spatial hearing task, a speech target was presented simultaneously with two intelligible speech maskers that were either colocated with the target (0° azimuth) or were symmetrically separated from the target in azimuth (at ±15°). EF assessment included measures of cognitive flexibility, inhibition control and auditory working memory. Selective attention was assessed in the visual domain using a multiple object tracking task (MOT). For the MOT task, the observers were required to track target dots (n = 1,2,3,4,5) in the presence of interfering distractor dots. Musicians performed significantly better than non-musicians in the spatial hearing task. For the EF measures, musicians showed better performance on measures of auditory working memory compared to non-musicians. Furthermore, across all individuals, a significant correlation was observed between performance on the spatial hearing task and measures of auditory working memory. This result suggests that individual differences in performance in a cocktail party-like environment may depend in part on cognitive factors such as auditory working memory. Performance in the MOT task did not differ between groups. However, across all individuals, a significant correlation was found between performance in the MOT and spatial hearing tasks. A stepwise multiple regression analysis revealed that musicianship and performance on the MOT task significantly predicted performance on the spatial hearing task. Overall, these findings confirm the relationship between musicianship and cognitive factors including domain-general selective attention and working memory in solving the "cocktail party problem".

Executive Function, Visual Attention and the Cocktail Party Problem in Musicians and Non-Musicians

PubMed Central

Clayton, Kameron K.; Swaminathan, Jayaganesh; Yazdanbakhsh, Arash; Zuk, Jennifer; Patel, Aniruddh D.; Kidd, Gerald

2016-01-01

The goal of this study was to investigate how cognitive factors influence performance in a multi-talker, “cocktail-party” like environment in musicians and non-musicians. This was achieved by relating performance in a spatial hearing task to cognitive processing abilities assessed using measures of executive function (EF) and visual attention in musicians and non-musicians. For the spatial hearing task, a speech target was presented simultaneously with two intelligible speech maskers that were either colocated with the target (0° azimuth) or were symmetrically separated from the target in azimuth (at ±15°). EF assessment included measures of cognitive flexibility, inhibition control and auditory working memory. Selective attention was assessed in the visual domain using a multiple object tracking task (MOT). For the MOT task, the observers were required to track target dots (n = 1,2,3,4,5) in the presence of interfering distractor dots. Musicians performed significantly better than non-musicians in the spatial hearing task. For the EF measures, musicians showed better performance on measures of auditory working memory compared to non-musicians. Furthermore, across all individuals, a significant correlation was observed between performance on the spatial hearing task and measures of auditory working memory. This result suggests that individual differences in performance in a cocktail party-like environment may depend in part on cognitive factors such as auditory working memory. Performance in the MOT task did not differ between groups. However, across all individuals, a significant correlation was found between performance in the MOT and spatial hearing tasks. A stepwise multiple regression analysis revealed that musicianship and performance on the MOT task significantly predicted performance on the spatial hearing task. Overall, these findings confirm the relationship between musicianship and cognitive factors including domain-general selective attention and working memory in solving the “cocktail party problem”. PMID:27384330

Chromatic aberration of light focusing in hyperbolic anisotropic metamaterial made of metallic slit array.

PubMed

Guo, Kai; Liu, Jianlong; Zhang, Yan; Liu, Shutian

2012-12-17

The dispersion of a hyperbolic anisotropic metamaterial (HAM) and the chromatic aberration of light focusing in this kind of HAM are studied. The HAM is formed by alternately stacking metal and dielectric layers. The rules of materials and filling factors affecting the optical property of HAM are given. The chromatic aberration of light focusing is demonstrated both theoretically and numerically. By comparing the theory with the simulation results, the factors influencing the focal length, including the heat loss of material and low spatial frequency modes, are discussed. The investigation emphasizes the anomalous properties, such as chromatic aberration and low spatial frequency modes influencing focus position, of HAM compared with that in conventional lens. Based on the analysis, the possibility of using HAM to focus light with two different wavelengths at the same point is studied.

Control factors and scale analysis of annual river water, sediments and carbon transport in China.

PubMed

Song, Chunlin; Wang, Genxu; Sun, Xiangyang; Chang, Ruiying; Mao, Tianxu

2016-05-11

Under the context of dramatic human disturbances on river system, the processes that control the transport of water, sediment, and carbon from river basins to coastal seas are not completely understood. Here we performed a quantitative synthesis for 121 sites across China to find control factors of annual river exports (Rc: runoff coefficient; TSSC: total suspended sediment concentration; TSSL: total suspended sediment loads; TOCL: total organic carbon loads) at different spatial scales. The results indicated that human activities such as dam construction and vegetation restoration might have a greater influence than climate on the transport of river sediment and carbon, although climate was a major driver of Rc. Multiple spatial scale analyses indicated that Rc increased from the small to medium scale by 20% and then decreased at the sizable scale by 20%. TSSC decreased from the small to sizeable scale but increase from the sizeable to large scales; however, TSSL significantly decreased from small (768 g·m(-2)·a(-1)) to medium spatial scale basins (258 g·m(-2)·a(-1)), and TOCL decreased from the medium to large scale. Our results will improve the understanding of water, sediment and carbon transport processes and contribute better water and land resources management strategies from different spatial scales.

The influence of camouflage, obstruction, familiarity and spatial ability on target identification from an unmanned ground vehicle.

PubMed

Fincannon, Thomas; Keebler, Joseph R; Jentsch, Florian; Curtis, Michael

2013-01-01

The purpose of this study was to examine the effects of environmental and cognitive factors on the identification of targets from an unmanned ground vehicle (UGV). This was accomplished by manipulating obstruction, camouflage and familiarity of objects in the environment, while also measuring spatial ability. The effects of these variables on target identification were studied by measuring performance of participants that observed pre-recorded video from a 1:35 scaled military operations in urban terrain facility. Analyses indicated that a combination of camouflage and obstruction caused the most detrimental effects on performance, and that there were differences in the recognition of familiar and unfamiliar targets. Further analysis indicated that these detrimental effects could only be overcome with a combination of target familiarity and spatial ability. The findings highlight the degree to which environmental factors hinder performance and the need for a multidimensional approach for improving performance under these conditions. Areas in need of future research are also discussed. Cognitive theory is applied to the problem of perception from UGVs. Results from an experimental study indicate that a combination of camouflage and obstruction caused the most detrimental effects on performance, with differences in the recognition of both familiar and unfamiliar targets. Familiarity and spatial ability interacted to predict the performance.

Modeling and predicting urban growth pattern of the Tokyo metropolitan area based on cellular automata

NASA Astrophysics Data System (ADS)

Zhao, Yaolong; Zhao, Junsan; Murayama, Yuji

2008-10-01

The period of high economic growth in Japan which began in the latter half of the 1950s led to a massive migration of population from rural regions to the Tokyo metropolitan area. This phenomenon brought about rapid urban growth and urban structure changes in this area. Purpose of this study is to establish a constrained CA (Cellular Automata) model with GIS (Geographical Information Systems) to simulate urban growth pattern in the Tokyo metropolitan area towards predicting urban form and landscape for the near future. Urban land-use is classified into multi-categories for interpreting the effect of interaction among land-use categories in the spatial process of urban growth. Driving factors of urban growth pattern, such as land condition, railway network, land-use zoning, random perturbation, and neighborhood interaction and so forth, are explored and integrated into this model. These driving factors are calibrated based on exploratory spatial data analysis (ESDA), spatial statistics, logistic regression, and "trial and error" approach. The simulation is assessed at both macro and micro classification levels in three ways: visual approach; fractal dimension; and spatial metrics. Results indicate that this model provides an effective prototype to simulate and predict urban growth pattern of the Tokyo metropolitan area.

Modelling the spatial behaviour of a tropical tuna purse seine fleet.

PubMed

Davies, Tim K; Mees, Chris C; Milner-Gulland, E J

2014-01-01

Industrial tuna fisheries operate in the Indian, Atlantic and Pacific Oceans, but concerns over sustainability and environmental impacts of these fisheries have resulted in increased scrutiny of how they are managed. An important but often overlooked factor in the success or failure of tuna fisheries management is the behaviour of fishers and fishing fleets. Uncertainty in how a fishing fleet will respond to management or other influences can be reduced by anticipating fleet behaviour, although to date there has been little research directed at understanding and anticipating the human dimension of tuna fisheries. The aim of this study was to address gaps in knowledge of the behaviour of tuna fleets, using the Indian Ocean tropical tuna purse seine fishery as a case study. We use statistical modelling to examine the factors that influence the spatial behaviour of the purse seine fleet at broad spatiotemporal scales. This analysis reveals very high consistency between years in the use of seasonal fishing grounds by the fleet, as well as a forcing influence of biophysical ocean conditions on the distribution of fishing effort. These findings suggest strong inertia in the spatial behaviour of the fleet, which has important implications for predicting the response of the fleet to natural events or management measures (e.g., spatial closures).

GENERALIZED TAYLOR-ARIS MOMENT ANALYSIS OF THE TRANSPORT OF SORBING SOLUTES THROUGH POROUS MEDIA WITH SPATIALLY PERIODIC RETARDATION FACTOR. (R825689C037)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

When What You See Isn’t What You Get: How Viewshed Analysis is Impacted by Digital Surface Model (DSM) Resolution

EPA Science Inventory

A key factor for improving models of ecosystem benefits is the availability of high quality spatial data. High resolution LIDAR data are now commonly available and can be used to produce more accurate model outputs. However, increased resolution leads to higher computer resource...

School Quality, Clustering and Government Subsidy in Post-Apartheid South Africa

ERIC Educational Resources Information Center

Yamauchi, Futoshi

2011-01-01

This paper examines a range of historical and geographic factors that determine the quality of public school education in post-apartheid South Africa. Empirical analysis shows, first, that population groups are still spatially segregated due to the legacy of apartheid, which implies that, given the positive correlation between school quality and…

Proximal Association of Land Management Preferences: Evidence from Family Forest Owners

Treesearch

Francisco X. Aguilar; Zhen Cai; Brett Butler

2017-01-01

Individual behavior is influenced by factors intrinsic to the decision-maker but also associated with other individuals and their ownerships with such relationship intensified by geographic proximity. The land management literature is scarce in the spatially integrated analysis of biophysical and socio-economic data. Localized land management decisions are likely...

Sequential and Simultaneous Processing in Children with Learning Disabilities: An Attempted Replication.

ERIC Educational Resources Information Center

Bain, Sherry K.

1993-01-01

Analysis of Kaufman Assessment Battery for Children (K-ABC) Sequential and Simultaneous Processing scores of 94 children (ages 6-12) with learning disabilities produced factor patterns generally supportive of the traditional K-ABC Mental Processing structure with the exception of Spatial Memory. The sample exhibited relative processing strengths…

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Understanding planning ability measured by the Tower of London: an evaluation of its internal structure by latent variable modeling.

PubMed

Koppenol-Gonzalez, Gabriela V; Bouwmeester, Samantha; Boonstra, A Marije

2010-12-01

The Tower of London (TOL) is a widely used instrument for assessing planning ability. Inhibition and (spatial) working memory are assumed to contribute to performance on the TOL, but findings about the relationship between these cognitive processes are often inconsistent. Moreover, the influence of specific properties of TOL problems on cognitive processes and difficulty level is often not taken into account. Furthermore, it may be expected that several planning strategies can be distinguished that cannot be extracted from the total score. In this study, a factor analysis and a latent class regression analysis were performed to address these issues. The results showed that 4 strategy groups that differed with respect to preplanning time could be distinguished. The effect of problem properties also differed for the 4 groups. Additional analyses showed that the groups differed on average planning performance but that there were no significant differences between inhibition and spatial working memory performance. Finally, it seemed that multiple factors influence performance on the TOL, the most important ones being the score measurements, the problem properties, and strategy use.

Population changes in residential clusters in Japan.

PubMed

Sekiguchi, Takuya; Tamura, Kohei; Masuda, Naoki

2018-01-01

Population dynamics in urban and rural areas are different. Understanding factors that contribute to local population changes has various socioeconomic and political implications. In the present study, we use population census data in Japan to examine contributors to the population growth of residential clusters between years 2005 and 2010. The data set covers the entirety of Japan and has a high spatial resolution of 500 × 500 m2, enabling us to examine population dynamics in various parts of the country (urban and rural) using statistical analysis. We found that, in addition to the area, population density, and age, the shape of the cluster and the spatial distribution of inhabitants within the cluster are significantly related to the population growth rate of a residential cluster. Specifically, the population tends to grow if the cluster is "round" shaped (given the area) and the population is concentrated near the center rather than periphery of the cluster. Combination of the present results and analysis framework with other factors that have been omitted in the present study, such as migration, terrain, and transportation infrastructure, will be fruitful.

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics.

PubMed

Islam, Abu Reza Md Towfiqul; Ahmed, Nasir; Bodrud-Doza, Md; Chu, Ronghao

2017-12-01

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO 3 - , then SO 4 2- , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

Climate and Human Pressure Constraints Co-Explain Regional Plant Invasion at Different Spatial Scales

PubMed Central

García-Baquero, Gonzalo; Caño, Lidia; Biurrun, Idoia; García-Mijangos, Itziar; Loidi, Javier; Herrera, Mercedes

2016-01-01

Alien species invasion represents a global threat to biodiversity and ecosystems. Explaining invasion patterns in terms of environmental constraints will help us to assess invasion risks and plan control strategies. We aim to identify plant invasion patterns in the Basque Country (Spain), and to determine the effects of climate and human pressure on that pattern. We modeled the regional distribution of 89 invasive plant species using two approaches. First, distance-based Moran’s eigenvector maps were used to partition variation in the invasive species richness, S, into spatial components at broad and fine scales; redundancy analysis was then used to explain those components on the basis of climate and human pressure descriptors. Second, we used generalized additive mixed modeling to fit species-specific responses to the same descriptors. Climate and human pressure descriptors have different effects on S at different spatial scales. Broad-scale spatially structured temperature and precipitation, and fine-scale spatially structured human population density and percentage of natural and semi-natural areas, explained altogether 38.7% of the total variance. The distribution of 84% of the individually tested species was related to either temperature, precipitation or both, and 68% was related to either population density or natural and semi-natural areas, displaying similar responses. The spatial pattern of the invasive species richness is strongly environmentally forced, mainly by climate factors. Since individual species responses were proved to be both similarly constrained in shape and explained variance by the same environmental factors, we conclude that the pattern of invasive species richness results from individual species’ environmental preferences. PMID:27741276

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.

Assessment of rural ecosystem health and type classification in Jiangsu province, China.

PubMed

Meng, Lingran; Huang, Jiu; Dong, Jihong

2018-02-15

Quantitative analysis of rural ecosystem health (REH) is required to comprehend the spatial differentiation of rural landscape and promote rural sustainable development under the pressure of urbanization and industrialization, especially those with dramatic changes in rural ecology of China and other developing countries. In this study, taking Jiangsu province as the case study, appropriate indicators were selected in the perspective of compound ecosystem and the rural ecosystem health index (REHI) was developed including four rural ecological subsystems of resource, environmental, social and economic. The comprehensive indicator assessment models and geographic information system (GIS) spatial methods were used to analyze the REH status and spatial differentiation of 57 counties in Jiangsu province. The REH scores of 57 rural counties were in a higher range of 0.686-0.882 and fluctuating increased from north to south, indicating that the rural ecosystem in Jiangsu province was at a relatively healthy level and counties in southern Jiangsu were healthier than those in central and northern regions. The spatial concentration of REH in Jiangsu was poor and the spatial distribution of four subsystems health levels were significantly different by spatial Gini coefficient analysis. The REH of 57 counties in Jiangsu province were classified into 13 types according to the identification of the health levels and quantity of four subsystems. Moreover, we analyzed the influencing factors of each type and proposed paths to promote the development and management of rural ecosystem. Copyright © 2017 Elsevier B.V. All rights reserved.

Investigation of the Dominant Factors Influencing the ERA15 Temperature Increments at the Subtropical and Temperate Belts with a Focus over the Eastern Mediterranean Region

NASA Astrophysics Data System (ADS)

Alpert, Pinhas; Hirsch-Eshkol, Tali; Baharad, Anat

2015-04-01

A Stepwise Multi Regression-based statistics was employed for prioritizing the influence of several factors, both anthropogenic and natural, on the ERA15 temperature increments. The 5 factors which are defined as predictors are;topography, aerosol index (TOMS-AI), atmospheric vertical velocity along with two anthropogenic factors population density and land use changes (LUCI and NDVI trends). The seismic hazard assessment factor was also chosen as the "dummy variable", for validity. Special focus was given to the land use change factor, which was based on two different data sets; HITE data of historical land use/ land cover data and of NDVI trends during 1982- 1991. The Increment Analysis Updates of temperature (IAU(T)), the predicted data, was obtained from the ERA15 (1979-1993) reanalysis. The research consists of both spatial and vertical analyses as well as potential synergies of the selected variables. The spatial geographic analysis is divided into three categories; (a) Coarse region (b) Sub regions analysis and (c) A "small cell" of 4°X4° analysis. It is shown that the following three factors;Topography, TOMS-AI and NDVI are statistically significant (at p<0.05 level) in being the most effective predictors of IAU(T), especially at the 700mb level during March - June. In contrast, the 850mb presents the weakest contribution to IAU(T)probably due to contradictive influence of the various variables at this level. The land use as expressed by the NDVI trends factor, shows a very clear dependency with height, i.e. decreasing, and is one of the most influential factors over the Eastern Mediterranean, which explains up to 20% of the temperature increments in January at 700mb. Moreover, its influence is significant (p<0.05) through all research stages and the different combinations of the multiple regression runs. A major finding not quantified earlier. Reference: T. Hirsch-Eshkol, A. Baharad and P. Alpert, "Investigation of the dominant factors influencing the ERA15 temperature increments at the subtropical and temperate belts with a focus over the Eastern Mediterranean region", Land, 3, 1015-1036; doi:10.3390/land3031015, 2014.

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.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

2017-12-01

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Analysis of Plasma-Sprayed Thermal Barrier Coatings With Homogeneous and Heterogeneous Bond Coats Under Spatially Uniform Cyclic Thermal Loading

NASA Technical Reports Server (NTRS)

Arnold, Steven M.; Pindera, Marek-Jerzy; Aboudi, Jacob

2003-01-01

This report summarizes the results of a numerical investigation into the spallation mechanism in plasma-sprayed thermal barrier coatings observed under spatially-uniform cyclic thermal loading. The analysis focuses on the evolution of local stress and inelastic strain fields in the vicinity of the rough top/bond coat interface during thermal cycling, and how these fields are influenced by the presence of an oxide film and spatially uniform and graded distributions of alumina particles in the metallic bond coat aimed at reducing the top/bond coat thermal expansion mismatch. The impact of these factors on the potential growth of a local horizontal delamination at the rough interface's crest is included. The analysis is conducted using the Higher-Order Theory for Functionally Graded Materials with creep/relaxation constituent modeling capabilities. For two-phase bond coat microstructures, both the actual and homogenized properties are employed in the analysis. The results reveal the important contributions of both the normal and shear stress components to the delamination growth potential in the presence of an oxide film, and suggest mixed-mode crack propagation. The use of bond coats with uniform or graded microstructures is shown to increase the potential for delamination growth by increasing the magnitude of the crack-tip shear stress component.

[Geographical distribution of left ventricular Tei index based on principal component analysis].

PubMed

Xu, Jinhui; Ge, Miao; He, Jinwei; Xue, Ranyin; Yang, Shaofang; Jiang, Jilin

2014-11-01

To provide a scientific standard of left ventricular Tei index for healthy people from various region of China, and to lay a reliable foundation for the evaluation of left ventricular diastolic and systolic function. The correlation and principal component analysis were used to explore the left ventricular Tei index, which based on the data of 3 562 samples from 50 regions of China by means of literature retrieval. Th e nine geographical factors were longitude(X₁), latitude(X₂), altitude(X₃), annual sunshine hours (X₄), the annual average temperature (X₅), annual average relative humidity (X₆), annual precipitation (X₇), annual temperature range (X₈) and annual average wind speed (X₉). ArcGIS soft ware was applied to calculate the spatial distribution regularities of left ventricular Tei index. There is a significant correlation between the healthy people's left ventricular Tei index and geographical factors, and the correlation coefficients were -0.107 (r₁), -0.301 (r₂), -0.029 (r₃), -0.277 (r₄), -0.256(r₅), -0.289(r₆), -0.320(r₇), -0.310 (r₈) and -0.117 (r₉), respectively. A linear equation between the Tei index and the geographical factor was obtained by regression analysis based on the three extracting principal components. The geographical distribution tendency chart for healthy people's left Tei index was fitted out by the ArcGIS spatial interpolation analysis. The geographical distribution for left ventricular Tei index in China follows certain pattern. The reference value in North is higher than that in South, while the value in East is higher than that in West.

Methodological Aspects of the Potential Use of Dendrochronological Techniques When Analyzing the Long-Term Impact of Tourism on Protected Areas.

PubMed

Ciapała, Szymon; Adamski, Paweł

2015-01-01

Intensification of pedestrian tourism causes damage to trees near tourist tracks, and likewise changes the soil structure. As a result, one may expect that annual amount of trees growing near tracks is significantly lower than deeper in the forest. However, during the study of the long-term impact of tourism on the environment (determined from tree increment dynamics), some methodological problems may occur. It is particularly important in protected areas where law and administrative regulations related to nature conservation force research to be conducted using small samples. In this paper we have analyzed the data collected in the Polish part of the Tatra National Park in the two study plots divided into two zones each: the area directly under the influence of the tourist's trampling and the control group. The aim of such analyses was to present the potential effects of the factors which may affect the results of dendrochronological analysis: (i) small size of samples that affects their representativeness, (ii) spatial differences in the rates of the process, as a result of spatial variability of environmental factors and (iii) temporal differences in the rates of the process. This study confirms that the factors mentioned above could significantly influence the results and should be taken into consideration during the analysis.

Spatial structure and nest demography reveal the influence of competition, parasitism and habitat quality on slavemaking ants and their hosts

PubMed Central

2011-01-01

Background Natural communities are structured by intra-guild competition, predation or parasitism and the abiotic environment. We studied the relative importance of these factors in two host-social parasite ecosystems in three ant communities in Europe (Bavaria) and North America (New York, West Virginia). We tested how these factors affect colony demography, life-history and the spatial pattern of colonies, using a large sample size of more than 1000 colonies. The strength of competition was measured by the distance to the nearest competitor. Distance to the closest social parasite colony was used as a measure of parasitism risk. Nest sites (i.e., sticks or acorns) are limited in these forest ecosystems and we therefore included nest site quality as an abiotic factor in the analysis. In contrast to previous studies based on local densities, we focus here on the positioning and spatial patterns and we use models to compare our predictions to random expectations. Results Colony demography was universally affected by the size of the nest site with larger and more productive colonies residing in larger nest sites of higher quality. Distance to the nearest competitor negatively influenced host demography and brood production in the Bavarian community, pointing to an important role of competition, while social parasitism was less influential in this community. The New York community was characterized by the highest habitat variability, and productive colonies were clustered in sites of higher quality. Colonies were clumped on finer spatial scales, when we considered only the nearest neighbors, but more regularly distributed on coarser scales. The analysis of spatial positioning within plots often produced different results compared to those based on colony densities. For example, while host and slavemaker densities are often positively correlated, slavemakers do not nest closer to potential host colonies than expected by random. Conclusions The three communities are differently affected by biotic and abiotic factors. Some of the differences can be attributed to habitat differences and some to differences between the two slavemaking-host ecosystems. The strong effect of competition in the Bavarian community points to the scarcity of resources in this uniform habitat compared to the other more diverse sites. The decrease in colony aggregation with scale indicates fine-scale resource hotspots: colonies are locally aggregated in small groups. Our study demonstrates that species relationships vary across scales and spatial patterns can provide important insights into species interactions. These results could not have been obtained with analyses based on local densities alone. Previous studies focused on social parasitism and its effect on host colonies. The broader approach taken here, considering several possible factors affecting colony demography and not testing each one in isolation, shows that competition and environmental variability can have a similar strong impact on demography and life-history of hosts. We conclude that the effects of parasites or predators should be studied in parallel to other ecological influences. PMID:21443778

Spatial structure and nest demography reveal the influence of competition, parasitism and habitat quality on slavemaking ants and their hosts.

PubMed

Scharf, Inon; Fischer-Blass, Birgit; Foitzik, Susanne

2011-03-28

Natural communities are structured by intra-guild competition, predation or parasitism and the abiotic environment. We studied the relative importance of these factors in two host-social parasite ecosystems in three ant communities in Europe (Bavaria) and North America (New York, West Virginia). We tested how these factors affect colony demography, life-history and the spatial pattern of colonies, using a large sample size of more than 1000 colonies. The strength of competition was measured by the distance to the nearest competitor. Distance to the closest social parasite colony was used as a measure of parasitism risk. Nest sites (i.e., sticks or acorns) are limited in these forest ecosystems and we therefore included nest site quality as an abiotic factor in the analysis. In contrast to previous studies based on local densities, we focus here on the positioning and spatial patterns and we use models to compare our predictions to random expectations. Colony demography was universally affected by the size of the nest site with larger and more productive colonies residing in larger nest sites of higher quality. Distance to the nearest competitor negatively influenced host demography and brood production in the Bavarian community, pointing to an important role of competition, while social parasitism was less influential in this community. The New York community was characterized by the highest habitat variability, and productive colonies were clustered in sites of higher quality. Colonies were clumped on finer spatial scales, when we considered only the nearest neighbors, but more regularly distributed on coarser scales. The analysis of spatial positioning within plots often produced different results compared to those based on colony densities. For example, while host and slavemaker densities are often positively correlated, slavemakers do not nest closer to potential host colonies than expected by random. The three communities are differently affected by biotic and abiotic factors. Some of the differences can be attributed to habitat differences and some to differences between the two slavemaking-host ecosystems. The strong effect of competition in the Bavarian community points to the scarcity of resources in this uniform habitat compared to the other more diverse sites. The decrease in colony aggregation with scale indicates fine-scale resource hotspots: colonies are locally aggregated in small groups. Our study demonstrates that species relationships vary across scales and spatial patterns can provide important insights into species interactions. These results could not have been obtained with analyses based on local densities alone. Previous studies focused on social parasitism and its effect on host colonies. The broader approach taken here, considering several possible factors affecting colony demography and not testing each one in isolation, shows that competition and environmental variability can have a similar strong impact on demography and life-history of hosts. We conclude that the effects of parasites or predators should be studied in parallel to other ecological influences.

Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis

NASA Astrophysics Data System (ADS)

Afzal, Peyman; Mirzaei, Misagh; Yousefi, Mahyar; Adib, Ahmad; Khalajmasoumi, Masoumeh; Zarifi, Afshar Zia; Foster, Patrick; Yasrebi, Amir Bijan

2016-07-01

Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C-N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area.

Surface plasmon enhanced cell microscopy with blocked random spatial activation

NASA Astrophysics Data System (ADS)

Son, Taehwang; Oh, Youngjin; Lee, Wonju; Yang, Heejin; Kim, Donghyun

2016-03-01

We present surface plasmon enhanced fluorescence microscopy with random spatial sampling using patterned block of silver nanoislands. Rigorous coupled wave analysis was performed to confirm near-field localization on nanoislands. Random nanoislands were fabricated in silver by temperature annealing. By analyzing random near-field distribution, average size of localized fields was found to be on the order of 135 nm. Randomly localized near-fields were used to spatially sample F-actin of J774 cells (mouse macrophage cell-line). Image deconvolution algorithm based on linear imaging theory was established for stochastic estimation of fluorescent molecular distribution. The alignment between near-field distribution and raw image was performed by the patterned block. The achieved resolution is dependent upon factors including the size of localized fields and estimated to be 100-150 nm.

[Geographic patterns and ecological factors correlates of snake species richness in China].

PubMed

Cai, Bo; Huang, Yong; Chen, Yue-Ying; Hu, Jun-Hua; Guo, Xian-Guang; Wang, Yue-Zhao

2012-08-01

Understanding large-scale geographic patterns of species richness as well its underlying mechanisms are among the most significant objectives of macroecology and biogeography. The ecological hypothesis is one of the most accepted explanations of this mechanism. Here, we studied the geographic patterns of snakes and investigated the relationships between species richness and ecological factors in China at a spatial resolution of 100 km×100 km. We obtained the eigenvector-based spatial filters by Principal Coordinates Neighbor Matrices, and then analyzed ecological factors by multiple regression analysis. The results indicated several things: (1) species richness of snakes showed multi-peak patterns along both the latitudinal and longitudinal gradient. The areas of highest richness of snake are tropics and subtropical areas of Oriental realm in China while the areas of lowest richness are Qinghai-Tibet Plateau, the grasslands and deserts in northern China, Yangtze-Huai Plain, Two-lake Plain, and the Poyang-lake Plain; (2) results of multiple regression analysis explained a total of 56.5% variance in snake richness. Among ecological factors used to explore the species richness patterns, we found the best factors were the normalized difference vegetation index, precipitation in the coldest quarter and temperature annual range ; (3) our results indicated that the model based on the significant variables that (P<0.05) uses a combination of precipitation of coldest quarter, normalized difference vegetation index and temperature annual range is the most parsimonious model for explaining the mechanism of snake richness in China. This finding demonstrates that different ecological factors work together to affect the geographic distribution of snakes in China. Studying the mechanisms that underlie these geographic patterns are complex, so we must carefully consider the choice of impact-factors and the influence of human activities.

Definition of management zones for enhancing cultivated land conservation using combined spatial data.

PubMed

Li, Yan; Shi, Zhou; Wu, Hao-Xiang; Li, Feng; Li, Hong-Yi

2013-10-01

The loss of cultivated land has increasingly become an issue of regional and national concern in China. Definition of management zones is an important measure to protect limited cultivated land resource. In this study, combined spatial data were applied to define management zones in Fuyang city, China. The yield of cultivated land was first calculated and evaluated and the spatial distribution pattern mapped; the limiting factors affecting the yield were then explored; and their maps of the spatial variability were presented using geostatistics analysis. Data were jointly analyzed for management zone definition using a combination of principal component analysis with a fuzzy clustering method, two cluster validity functions were used to determine the optimal number of cluster. Finally one-way variance analysis was performed on 3,620 soil sampling points to assess how well the defined management zones reflected the soil properties and productivity level. It was shown that there existed great potential for increasing grain production, and the amount of cultivated land played a key role in maintaining security in grain production. Organic matter, total nitrogen, available phosphorus, elevation, thickness of the plow layer, and probability of irrigation guarantee were the main limiting factors affecting the yield. The optimal number of management zones was three, and there existed significantly statistical differences between the crop yield and field parameters in each defined management zone. Management zone I presented the highest potential crop yield, fertility level, and best agricultural production condition, whereas management zone III lowest. The study showed that the procedures used may be effective in automatically defining management zones; by the development of different management zones, different strategies of cultivated land management and practice in each zone could be determined, which is of great importance to enhance cultivated land conservation, stabilize agricultural production, promote sustainable use of cultivated land and guarantee food security.

Analysis of spatial distribution and transmission characters for highly pathogenic avian influenza in Chinese mainland in 2004

NASA Astrophysics Data System (ADS)

Liu, Y. L.; Wei, C. J.; Yan, L.; Chi, T. H.; Wu, X. B.; Xiao, C. S.

2006-03-01

After the outbreak of highly pathogenic Avian Influenza (HPAI) in South Korea in the end of year 2003, estimates of the impact of HPAI in affected countries vary greatly, the total direct losses are about 3 billion US dollars, and it caused 15 million birds and poultry flocks death. It is significant to understand the spatial distribution and transmission characters of HPAI for its prevention and control. According to 50 outbreak cases for HPAI in Chinese mainland during 2004, this paper introduces the approach of spatial distribution and transmission characters for HPAI and its results. Its approach is based on remote sensing and GIS techniques. Its supporting data set involves normalized difference vegetation index (NDVI) and land surface temperature (Ts) derived from a time-series of remote sensing data of 1 kilometer-resolution NOAA/AVHRR, birds' migration routes, topology geographic map, lake and wetland maps, and meteorological observation data. In order to analyze synthetically using these data, a supporting platform for analysis Avian Influenza epidemic situation (SPAS/AI) was developed. Supporting by SPAS/AI, the integrated information from multi-sources can be easily used to the analysis of the spatial distribution and transmission character of HPAI. The results show that the range of spatial distribution and transmission of HPAI in China during 2004 connected to environment factors NDVI, Ts and the distributions of lake and wetland, and especially to bird migration routes. To some extent, the results provide some suggestions for the macro-decision making for the prevention and control of HPAI in the areas of potential risk and reoccurrence.

The application of GIS and RS for epidemics: a case study of the spread of highly pathogenic avian influenza in China in 2004-2005

NASA Astrophysics Data System (ADS)

Zhong, Shaobo; Lan, Guiwen; Zhu, Haiguo; Wen, Renqiang; Zhao, Qiansheng; Huang, Quanyi

2008-12-01

Because of their inherent advantages, Geographic Information System (GIS) and Remote Sensing (RS) are extremely useful for dealing with geographically referenced information. In the study of epidemics, most data are geographically referenced, which makes GIS and RS the perfect even necessary tools for processing, analysis, representation of epidemic data. Comprehensively considering the data requirements in the study of highly pathogenic avian influenza (HPAI) coupled with the quality of the existing remotely sensed data in terms of the resolution of space, time and spectra, the data sensed by MODIS are chosen and the relevant methods and procedures of data processing from RS and GIS for some environmental factors are proposed. Through using spatial analysis functions and Exploratory Spatial Data Analysis (ESDA) of GIS, some results of relationship between HPAI occurrences and these potential factors are presented. The role played by bird migration is also preliminarily illustrated with some operations such as visualization, overlapping etc. provided by GIS. Through the work of this paper, we conclude: Firstly, the migration of birds causes the spread of HPAI all over the country in 2004-2005. Secondly, the migration of birds is the reason why the spread of HPAI is perturbed. That is, for some classic communicable diseases, their spread exhibits obvious spatial diffusion process. However, the spread of HPAI breaks this general rule. We think leap diffusion and time lag are the probable reasons for this kind of phenomena. Potential distribution of HPAI viruses (corresponding to the distribution of flyways and putative risk sources) is not completely consistent with the occurrences of HPAI. For this phenomenon, we think, in addition to the flyways of birds, all kinds of geographical, climatic factors also have important effect on the occurrences of HPAI. Through the case study of HPAI, we can see that GIS and RS can play very important roles in the study of epidemics.

Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

PubMed

Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

2017-08-07

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.

Accuracy of Petermann's K-factor in the theory of semiconductor lasers

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

El Mashade, M.B.; Arnaud, J.

1986-04-01

Petermann has proposed that the classical formula for the linewidth of a laser be multiplied by a factor K >> 1 in the case of gain-guided semiconductor lasers. The concept of power in the mode used by that author, however, is not well defined in a waveguide with gain, and his theory is therefore opened to question. The analysis given here avoids this difficulty and nevertheless agrees with Petermann's result. This is because spatial mode filtering is strong in oscillating lasers.