A spatial autocorrelation analysis of road traffic crash by severity using Moran's I spatial statistics: A comparative study of Addis Ababa and Berlin cities

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China

Number of cases of tuberculosis (TB) was higher than that of the national level in Kashgar, China. This study aimed to analyze the spatial and temporal distribution of TB and the relationship between TB and social factors, which can provide a reference for the prevention and control of TB. We applied spatial autocorrelation analysis to study the distribution of tuberculosis in Kashgar. We used a geographically weighted regression (GWR) model to analyze the relationship between TB and social factors. A total of 100,330 cases of TB in Kashgar from 2016 to 2021 were analyzed. The number of TB cases in Kashgar was higher in the east, lower in the west, and most elevated in the center. The highest cumulative number of cases was found in Shache county. Global Moran's I ranged from -0.212 to -0.549, and local spatial autocorrelation analysis identified four clusters. According to our analysis, the incidence of tuberculosis was negatively correlated among the regions of Kashgar, and the related causes need to be analyzed in depth in future studies. Per capita gross domestic product (GDP), number of medical institutions per capita, and total population influenced the incidence of tuberculosis in Kashgar. Based on our findings, we suggest some effective measures to reduce the risk of TB infection, such as improving the living standard, developing the regional economy, and distributing health resources rationally.

Geospatial Distribution of Ambulatory Surgery Center Utilization for Otorhinolaryngologic Surgeries Among Medicare Patients From 2015 to 2019

Objective

To investigate the geographic clustering of ambulatory surgical center (ASC) utilization in otolaryngology to determine hot spot areas of high utilization and cold spot areas of low utilization and socioeconomic factors that correlate with these hot spots and cold spots.

Study Design

To develop a national epidemiologic study of ASC utilization in otolaryngology in the United States.

Setting

United States of America.

Methods

Multiple county-level national databases were reviewed including Center for Medicare Services (CMS) physician billing data, CMS Medicare demographic data, and US Census socioeconomic data. The analysis was conducted using the average of all Medicare billing information from 2015 to 2019. Whether a procedure was performed in an ASC was extracted from CMS data using the CMS definition of an ASC. The percentage ASC billing was calculated as the fraction of CMS payments that were performed in ASCs for the top ENT procedures. A Python-based script for database building and GeoDa, Moran's I clustering coefficient, and a 1-way analysis of variance was utilized to chart and analyze demographic, geographic, and socioeconomic trends.

Results

Hot spots of utilization, with an average ASC billing of 80.13%, were seen in Southern California, Florida, Mid-Atlantic, and clusters throughout the Deep South. Cold spot clusters, with an average ASC billing of 2.21%, were located in large swaths of New England, Ohio, and the Deep South with clusters bisecting the Midwest. Cold spots had a higher percentage of poverty and percent eligible for Medicaid.

Conclusion

ASC utilization is best used to improve cost-effectiveness and accessibility of care but what is seen is that ASC use is currently highest in cities in coastal areas which already have high levels of care access and are making the most proportional money compared to their rural counterparts.

Intestinal schistosomiasis-related mortality in Minas Gerais, Brazil, 2000-2019: Temporal trends and spatial patterns for determining priority areas

OBJECTIVE

To identify priority areas for schistosomiasis control, we analysed the epidemiological characteristics, temporal trends and spatial patterns of schistosomiasis-related mortality in the state of Minas Gerais from 2000 to 2019.

METHODS

Ecological and time-series study with spatial analysis techniques on deaths from Schistosomiasis mansoni. A log-linear regression model was used to identify changes in mortality rates. Moran's global index, local indicators of spatial association and a retrospective spatio-temporal permutation model were applied to identify the spatial and temporal distribution of mortality rates and assist in identifying priority areas for interventions.

RESULTS

A total of 1290 deaths from schistosomiasis were recorded between 2000 and 2019, with an average mortality rate of 0.33 deaths/100,000. Although the overall mortality rate in the state of Minas Gerais decreased significantly over time (average annual percentage change = -9.6; 95% confidence interval = -14.4 to -4.6; p < 0.001), it increased in the mesoregions of Jequitinhonha, Mucuri Valley, and Rio Doce Valley. Spatial analysis identified the displacement and emergence of high-risk clusters from the central region of the state to the mesoregion of Rio Doce Valley.

CONCLUSION

Temporal changes and shifting of high-risk areas from the central region to the mesoregion of Rio Doce Valley may indicate possible failures in early diagnosis and treatment of the schistosomiasis control program in these areas. Our research contributes to a better understanding of the spatio-temporal dynamics of death rates due to schistosomiasis infections and might help health authorities to direct resources most efficiently to avoid serious clinical outcomes in Minas Gerais.

Poultry manure gleaned antibiotic residues in soil environment: A perspective of spatial variability and influencing factors

The antibiotics released by human and animals end up in the environmental sinks like soil and water to cause contamination and induce resistance in the microflora. The knowledge of fate and behavior of antibiotics in diverse geographical, climatological, and physicochemical is limited. Therefore, present study investigated the spatial distribution of antibiotics and their relationship with various factors and the source-sink relationship between soil and poultry manure. This was achieved by employing spatially constrained hierarchical clustering, global and local spatial autocorrelation, and spatial regression techniques. Most of the antibiotics co-occurred in both soil and poultry manure matrices, however antibiotic concentration in soil (1.20 μg kg^-1 < antibiotics ≤21.38 μg kg^-1) was lower than that in the poultry manure (7.05 μg kg^-1< antibiotics ≤60.2 μg kg^-1). Majority of the antibiotics showed spatial independence in both poultry manure and soil, except for sulfadiazine, sulfanilamide and sulfapyridine with Moran's I > - 0.111. Local indicator of spatial association indicated localized spatial clustering and outlier behavior of antibiotics. The underlying reasons for spatial heterogeneity of antibiotics resolved by spatial regression models indicated elevation, S%, C%, pH and mean annual temperature as the major factors. The influence of antibiotic concentration in poultry manure as a source was significant but marginal compared to the other predictors of spatial heterogeneity.

Place cells are nonrandomly clustered by field location in CA1 hippocampus

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

Redeploy manure resources to enhance the agro-pastoral cycle

Returning manure to the land is a critical link in the internal cycle of agricultural systems, but excess manure leads to water eutrophication. The traditional manure re-use method brings pathogenic microorganisms, heavy metals, antibiotic resistance genes (ARGs), insect eggs, and other contaminants into the soil, posing a great threat to the ecological environment and human health. Clarifying the spatial distribution patterns of manure nutrient supply and farmland nutrient demand can help guide a more efficient and harmless way to return manure to farmland. This work counted data on cultivation and breeding in 356 cities on the Chinese mainland from 2015 to 2019 and calculated the livestock breeding volume (LB), total environmental capacity (C), and remaining environmental capacity (RC) accordingly. The Spatial Autocorrelation Model (SAC) was used to analyze the distribution patterns of the three. Data results show that China currently has the potential to double LB, but most cities in the west have excess manure due to the mismatched distribution of LB and C. The hot spot analysis results demonstrate the priority/general areas of manure management and the export/import areas of manure resources. The results of the outlier analysis show that some cities located at the boundary of RC Cold/Hot spot areas (e.g., Chengdu City) can perform resource replacement nearby to relieve local environmental pressure. This study analyzes the potential and realistic resistance to utilizing manure as an organic nutrient resource and provides a reference for developing manure management links.

[Identification of Soil Heavy Metal Sources Around a Copper-silver Mining Area in Ningxia Based on GIS]

This study area was based on the catchment area of the Yaoxianzi ditch located in the arid region of western China. A total of 194 topsoil samples of 0-20 cm depth were collected using the mesh distribution method. The contents of nine heavy metals (Ni, Cu, Zn, As, Ag, Cr, Cd, Hg, and Pb) were determined using ICP-MS. The source and spatial distribution of heavy metals were analyzed using PMF and IDW. Spatial autocorrelation and clustering and outlier analysis were performed using the Spatial Statistical Analysis tool of ArcGIS. The main sources and distribution areas of heavy metals in the soil were obtained through comprehensive analysis. In the study area, the average values of Hg, Ag, Cd, and Pb were 20.48, 3.13, 2.23, and 1.12 times the background values, and the maximum values of Cd, Cu, Pb, and As were 10.92, 5.52, 2.03, and 1.39 times the filter values, respectively. The coefficients of variation of Cu, Cd, Pb, and Hg were ordered as Cu(283.23%)>Cd(224.77%)>Pb(144.40%)>Hg(67.12%) and were closely affected by human activities. The heavy metals in the soil around mining areas came from four main sources:natural parent material (32%), the mixed source of mining activities and transportation (17.1%), the mixed source of industrial activities and atmospheric sedimentation (40.3%), and the mixed source of agricultural activity and putting sandy gravel in farmland (10.6%). Cr and Ni, As and Cu, Hg, and Cd could represent these four sources of heavy metal pollution, respectively. The main sources of soil heavy metal pollution were mining activities and agricultural activities. The heavily contaminated areas were distributed in the mining areas in the south of the study area and in the planting areas in the eastern, central, and northwestern parts of the study area.

Geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe, Zambia

BACKGROUND

Communities around Kabwe, Zambia are exposed to lead due to deposits from an old lead (Pb) and zinc (Zn) mining site. Children are particularly more vulnerable than adults, presenting with greatest risk of health complications. They have increased oral uptake due to their hand to mouth activities. Spatial analysis of childhood lead exposure is useful in identifying specific areas with highest risk of pollution. The objective of the current study was to use a geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe.

METHODS

We analysed existing data on blood lead levels (BLL) for 362 children below the age of 15 from Kabwe town. We used spatial autocorrelation methods involving the global Moran's I and local Getis-Ord Gi*statistic in ArcMap 10.5.1, to test for spatial dependency among the blood lead levels in children using the household geolocations.

RESULTS

BLL in children from Kabwe are spatially autocorrelated with a Moran's Index of 0.62 (p < 0.001). We found distinct hotspots (mean 51.9 μg/dL) in communities close to the old lead and zinc-mining site, lying on its western side. Whereas coldspots (mean 7 μg/dL) where observed in areas distant to the mine and traced on the eastern side. This pattern suggests a possible association between observed BLL and distance from the abandoned lead and zinc mine, and prevailing winds.

CONCLUSION

Using geocoded data for households, we found clustering of childhood blood lead and identified distinct hotspot areas with high lead levels for Kabwe town. The geospatial approach used is especially valuable in resource-constrained settings like Zambia, where the precise identification of high risk locations allows for the initiation of targeted remedial and treatment programs.

Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA

This study constructs two biophysical metrics; one based on Land Surface Temperatures (LST) and an integrated spectral index. The latter is an aggregate of Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The goal is to determine how disparate weighting techniques, data transformation approaches, and spatial visualization pathways influence the computation of composite heat metrics. Using composite images made of aggregated images from late May to Early September within Google Earth Engine, we generated four composites by combining biophysical metrics with SoVI using equal and Eigen-based weightings informed by Principal Component Analysis (PCA). We compared equal interval classification, global and local Moran's as pathways for spatial visualization of hotspots. We utilized several data transformation techniques in a Geographic Information System (GIS), including rescaling, reclassification, zonal statistics, and spatial weighting. Mann Kendall and Sen's Slope detected and quantified monotonic trends in each spectral index. The results show that the LST biophysical metric and its composites indicate increased heat susceptibility over time, with disproportionately exposed core metro counties. The integrated spectral index and its proxies showed reduced vulnerability hence not a good proxy for LST. At the same time, the Mann Kendall and Sen's Slope found persistent increases in NDVI and NDWI and decreases in NDBI and NDBaI. However, opposite trends were evident in core city counties. The LST-based composites and spectral indices-based composites varied in the spatial-temporal distribution of hotspots. Disparate weighting mechanics, data transformation techniques, and visualization alternatives influence the magnitude and spatial-temporal distribution of heat hotspots.

Spatio-Temporal Variation of Health Production Efficiency Considering Environmental Pollution in China Based on Modified EBM and Spatial Econometric Model

In order to strengthen the construction of China's health industry and improve the health of the people, based on the data of 31 provinces and cities in China from 2009 to 2019, the improved EBM model is used to measure the health production efficiency of each region, and Moran index is used to study the Spatio-temporal variation of health production efficiency of each province. Finally, the spatial econometric model is applied to study the influencing factors of the Spatio-temporal variation of health production efficiency. The results show that generally speaking, the average efficiency of 31 provinces and cities is above 0.7, and the average efficiency of some regions is above 1. From the perspective of time variation, the average efficiency value in the eastern region and the middle region increases from 0.816 to 0.882 and from 0.851 to 0.861, respectively. However, the average efficiency value in the western region and northeast region decreases from 0.861 to 0.83 and from 0.864 to 0.805, respectively. From the perspective of spatial distribution, HH agglomeration and LL agglomeration exist in most regions. By comparing Moran scatter plots in 2009 and 2019, it is found that the quadrants of most regions remain unchanged, and LL agglomeration is the main agglomeration type in local space. There is a significant spatial dependence among different regions. From the perspective of spatial empirical results, Pgdp, Med, and Pd have a positive effect on health production efficiency. The direct effect and indirect effect of Pgdp, Med, and Gov all pass the significance test of 1%, indicating that there are spatial spillover effects of the three indicators. Each region should reasonably deal with the spillover effect of surrounding regions, vigorously develop economic activities, carry out cooperation with surrounding regions and apply demonstration effect to accelerate the development of overall health production.

Growth and challenges of China's nursing workforce from 1998 to 2018: A retrospective data analysis

BACKGROUND

Nurses play a vitally important role in promoting equitable and essential care. China undertook bold reforms in its education and healthcare systems since 1990s. The effect of these reforms on the nursing workforce has not been assessed systematically.

OBJECTIVE

This study aims to assess the changing trends and the underlying challenges of the nursing workforce in Mainland China in the period of 1998-2018.

DESIGN

Retrospective data analysis.

METHODS

Data were acquired from the National Health Statistics Yearbook from 1999 to 2019. Descriptive statistics were used to analyze the nature of the nursing workforce in terms of quantity, quality, and structure. Non-parametric tests were used to compare doctors and nurses in terms of number and work experiences. Global Moran's I index and hotspot analysis were applied to compare the equity in distribution of nurses at national and provincial levels.

RESULTS

From 1998 to 2018, the number of nurses increased from 1.22 to 4.10 million with an average rate of increase of 6.3% per annum. The ratio of doctors to nurses changed from 1: 0.61 to 1: 1.14, reaching 1: 1 in 2013. The main educational level of registered nurses elevated to associated degree (48.9%), and nurses with advanced titles increased at the most rapid rate. In 2018, 60.3% of nurses were younger than 35 years old. The Global Moran's I index ranged from 0.211 to 0.198 (Z > 1.96, P < 0.05). Hotspot analysis showed the distribution of nurses was unequally concentrated in the northern region and with the highest distribution in Beijing.

CONCLUSIONS

Great improvement on the scale and the quality of nursing workforce over the past 20 years has been witnessed in China. However, the shortage of nurses, outflow of younger nurses and the imbalance distribution of nursing workforce among the country are emerging challenges. Plans should not be ignored on continuously cultivating more qualified nurses, retaining younger nurses, attracting nurses to work in rural areas and the northeast region. Tweetable abstract: Numbers of Chinese nurses finally outstrip the number of doctors but do limited educational opportunities limits their contribution to the nation's health? New article in @ijnsjournal.

Spatial Distribution of Road Traffic Accident at Hawassa City Administration, Ethiopia

BACKGROUND

Globally, road traffic accidents (RTAs) are the leading killer of young people and are projected to be the 7th leading cause of death by 2030. This study is aimed at analyzing the spatial distribution of road traffic accident and identifying hotspot areas across Kebeles (smallest administrative division in Ethiopia) of Hawassa city administration in Ethiopia.

METHOD

Secondary data on daily traffic accident record from October 2013 to June 2018 was obtained from Hawassa city administration police department. The spatial clustering and hotspots identification were carried through Moran's I and Getis-Ord Gi* statistics. Data analysis was conducted using GeoDa 1.16.0.0 and ArcGIS 10.2 softwares.

RESULTS

Drivers within age group of 18-30 years, who were hired by private business owners and who had no driving license committed the highest number of traffic accidents. The majority of traffic accidents were caused due to careless driving, failure to give priority for pedestrian, high speed and driver failure to give priority for each other. In addition, about 82.01% of traffic accidents were recorded on asphalts road and 11.51% by gravel road. Spatial clustering of road traffic accidents for accidents occurred on gravel road and in sunny weather conditions found to be significant. Different hotspot areas were identified for gravel type of road and sunny weather condition.

CONCLUSION

The concerned government bodies involved in policymaking are recommended to give special attention to young driver who were hired by private business owners. Interventions to mitigate the occurrence of traffic accident would take in to account the identified hotspot areas.

Normal alkanes in sediments from the Persian Gulf: spatial pattern and implications for autochthonous, allochthonous, and petroleum-originated contaminants

Surface sediments (n = 124) were gathered to evaluate spatial pattern and source apportionment of ∑₂₄n-alkanes in the Persian Gulf. Moran's I indices revealed that ∑₂₄n-alkanes were randomly distributed in sampling space in general. One noticeable exception was a significant spatial high-high cluster (Z-score = 8.6). Profile of n-alkanes observed for this cluster maximized at n-C₂₀, n-C₁₈, n-C₁₆, and n-C₂₂. Detection of very low carbon preference indices and strong even carbon-numbered predominance suggested that bacterial inputs were the process making this cluster. Based on diagnostic ratios and GIS techniques, in situ production of organic materials was widespread in the studied space. Allochthonous and petroleum-based inputs were not traceable for 73.8% and 24.7% of the area, respectively. Petrogenic source apportionment map proposed that petroleum-originated contaminants were probably limited to 6.32 km² of the sampling matrix. The biogenic inputs (autochthonous and allochthonous inputs) and not the petroleum contaminants were the main source of n-alkanes deposited.

Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014-2016

Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014-2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) models were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

Using GIS, geostatistics and Fuzzy logic to study spatial structure of sedimentary total PAHs and potential eco-risks; An Eastern Persian Gulf case study

GIS, geo-statistics and autocorrelation analysis were employed to reveal spatial structure of sedimentary ∑16PAHs. Global Moran's I index outlined significant ∑₁₆PAHs clusters for the entire region (Moran's I index =0.62, Z-score = 25.6). Anselin Moran's I index specified locations of the significant low/high spatial clusters. The levels of random and structural variance of ∑₁₆PAHs were about 0.083 and 0.154, respectively. Nugget to sill ratio confirmed that ∑₁₆PAHs has a moderate level of spatial structure and the major part of PAHs variability is not random. Prediction and standard error maps of ∑₁₆PAHs, produced by ordinary kriging, highlighted that more samples should be taken from high cluster region for next studies. Fuzzy logic functions (OR and AND) were used to develop eco-risk maps. It revealed that the potential hazards of PAHs are considerable at the vicinity of petrochemical facilities.

Geographic distribution of staphylococcus spp. infections and antimicrobial resistance among dogs from Gauteng Province presented at a veterinary teaching hospital in South Africa

The objective of this study was to investigate spatial patterns of staphylococcal infections and resistance patterns of clinical isolates among dogs from Gauteng province in South Africa. Data from records of 1497 dog clinical samples submitted to a veterinary teaching hospital between 2007 and 2012 were used in the study. Spatial empirical Bayesian smoothed risk maps were used to investigate spatial patterns of staphylococcal infections, antimicrobial resistance (AMR), and multidrug resistance (MDR). Moran's I and spatial scan statistics were used to investigate spatial clusters at municipal and town spatial scales. Significant clusters of staphylococcal infections were identified at both the municipal (Relative Risk [RR] = 1.71, p = 0.003) and town (RR = 1.65, p = 0.039) scales. However, significant clusters of AMR (p = 0.003) and MDR (p = 0.007) were observed only at the town scale. Future larger studies will need to investigate local determinants of geographical distribution of the clusters so as to guide targeted control efforts.

[Spatial Variation of Heavy Metals in Soils and Its Ecological Risk Evaluation in a Typical Carya cathayensis Production Area]

In recent years, the problem of soil pollution has become more and more serious. The problem of soil heavy metal pollution and its related human health risks has become a hot spot at home and abroad. Carya cathayensis is a unique high-grade woody nut and oil tree from China, and there are few reports on heavy metal pollution in Carya cathayensis plantation soils. Therefore, in order to study the spatial variability of heavy metals and the risk of pollution in Carya cathayensis soil and to promote the sustainable development of the Carya cathayensis industry, Lin'an, a typical Carya cathayensis plantation area, was selected for this study. A total of 188 soil samples were collected from the study area. We systematically studied the spatial heterogeneity of soil heavy metal content in the study area based on GIS technology, geostatistics, Moran's I, and other spatial analysis methods. The single factor pollution index method, the Nemerow comprehensive pollution index method, and the potential ecological risk assessment method were used to evaluate the heavy metal pollution in the study area. The results indicated that the mean content of soil cadmium (Cd), copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), and chromium (Cr) were 0.37, 40.76, 87.61, 30.10, 28.33, 56.57 mg·kg^-1, respectively. The average values of Cd and Cu were 1.33 and 2.87 times of the background values, respectively, and the average content of other heavy metals did not exceed the background values. The results of the single factor Nemerow pollution index and potential ecological risk assessment methods showed that heavy metals in the study area exceeded the soil background values in some samples, and the second grade standard of soil environmental quality was exceed for Cd, Cu, Zn, Pb, and Ni in 31.38%, 31.38%, 2.65%, 0.53%, and 17.02% of the samples, respectively. This indicated that the soils in the study area had different accumulation characteristics for Cd, Cu, Zn, Pb, Ni, and Cr, and the local soil had reached pollution levels for Cd, Cu, Zn, Pb, and Ni. Among them, Cd was the most serious, reaching the degree of strong ecological damage, followed by Cu. In general, the heavy metal contents indicated a moderate degree of ecological damage. Based on the analysis of the semi-variance function, the Cd, Cu, and Ni in the soil were best fit with exponential models, the Zn and Pb were better fit with the Gaussian model, and Cr was consistent with the spherical model. Cd, Cu, Pb, Ni, and Cr had the strong spatial autocorrelation, with Nugget/Sill ratios of 12.1%, 4.6%, 14.9%, 2.6%, and 11.2%, respectively, while the Nugget/Sill ratio of Zn was 48.8%, indicating a medium spatial autocorrelation. Moran's I and Kriging interpolation results found that the heavy metals Cd, Cu, Zn, Pb, Ni, and Cr all had obvious spatial distribution patterns and local spatial aggregation phenomena. The high values of heavy metals in soils were mainly found in Taiyang, Daoshi, Qingliangfeng, Heqiao, and Tuankou, and the probability of the risk for contamination by Cd and Cu was higher in the study area. The high values of Cd, Cu, Zn, Ni, and Cr were mainly related to mining, while Pb was closely related to the application of potassium.

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model]

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Scale effect on spatial patterns of ecosystem services and associations among them in semi-arid area: A case study in Ningxia Hui Autonomous Region, China

Identifying scale effect on spatial patterns of ecosystem services and associations among them has been recognized as critical to the sustainable management of ecosystem services. We proposed a method to conveniently integrate ecosystem services of small scale into different larger scales. Taking Ningxia Hui Autonomous Region in west China as an example, we analyzed the change regularity of spatial patterns of 7 ecosystem services under 22 different scales. Further, the tradeoffs and synergies among ecosystem services across all scales were compared. The results showed that all of the 7 ecosystem services had been characterized by stable spatial cluster patterns across all of the 22 scales in our study. However, the extent of aggregation decreased with the increase of scale owing to the 'peak cutting and valley filling' process of map scale synthesis. Most of the associations among ecosystem services were robust across scales. However, there was a trend that smaller scales had more pairwise correlations than larger scales. The formation of tradeoffs and synergies among ecosystem services can be attributed to one or more of the following three factors: land use conflict or consistency, common drivers, and interactions among ecosystem services. We attribute the change of relationships among ecosystem services with scales to that the role of factors causing tradeoffs and synergies among ecosystem services may change with scales. Food supply service synergizes with all of the 3 regulating services at almost all of the scales in our study area, indicating that well managed farmlands with high net profit are beneficial to soil conservation and organic matter accumulation in semi-arid area.

Sampling design and required sample size for evaluating contamination levels of 137Cs in Japanese fir needles in a mixed deciduous forest stand in Fukushima, Japan

We estimated the sample size (the number of samples) required to evaluate the concentration of radiocesium (¹³⁷Cs) in Japanese fir (Abies firma Sieb. & Zucc.), 5 years after the outbreak of the Fukushima Daiichi Nuclear Power Plant accident. We investigated the spatial structure of the contamination levels in this species growing in a mixed deciduous broadleaf and evergreen coniferous forest stand. We sampled 40 saplings with a tree height of 150 cm-250 cm in a Fukushima forest community. The results showed that: (1) there was no correlation between the ¹³⁷Cs concentration in needles and soil, and (2) the difference in the spatial distribution pattern of ¹³⁷Cs concentration between needles and soil suggest that the contribution of root uptake to ¹³⁷Cs in new needles of this species may be minor in the 5 years after the radionuclides were released into the atmosphere. The concentration of ¹³⁷Cs in needles showed a strong positive spatial autocorrelation in the distance class from 0 to 2.5 m, suggesting that the statistical analysis of data should consider spatial autocorrelation in the case of an assessment of the radioactive contamination of forest trees. According to our sample size analysis, a sample size of seven trees was required to determine the mean contamination level within an error in the means of no more than 10%. This required sample size may be feasible for most sites.

Water quality variation in the highly disturbed Huai River Basin, China from 1994 to 2005 by multi-statistical analyses

Water quality deterioration is a prominent issue threatening water security throughout the world. Huai River Basin, as the sixth largest basin in China, is facing the most severe water pollution and high disturbance. Statistical detection of water quality trends and identification of human interferences are significant for sustainable water quality management. Three key water quality elements (ammonium nitrogen: NH3-N, permanganate index: CODMn and dissolved oxygen: DO) at 18 monitoring stations were selected to analyze their spatio-temporal variations in the highly disturbed Huai River Basin using seasonal Mann-Kendall test and Moran's I method. Relationship between surrounding water environment and anthropogenic activities (point source emission, land use) was investigated by regression analysis. The results indicated that water environment was significantly improved on the whole from 1994 to 2005. CODMn and NH3-N concentrations decreased at half of the stations, and DO concentration increased significantly at 39% (7/18) stations. The high pollution cluster centers for both NH3-N and CODMn were in the middle stream of Shaying River and Guo River in the 2000s. Water quality of Huai River Basin was mainly influenced by point source pollution emission, flows regulated by dams, water temperature and land use variations and so on. This study was expected to provide insights into water quality evolution and foundations for water quality management in Huai River Basin, and scientific references for the implementation of water pollution prevention in China.

Geographic analysis of individual and environmental risk factors for hypospadias births

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 to 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 (odds ratio = 1.22; 95% confidence interval = 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.

CONCLUSION

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. Birth Defects Research (Part A) 100:887-894, 2014. © 2014 Wiley Periodicals, Inc.

Long-term (1930-2010) trends in groundwater levels in Texas: influences of soils, landcover and water use

Rapid groundwater depletion has raised grave concerns about sustainable development in many parts of Texas, as well as in other parts of the world. Previous hydrologic investigations on groundwater levels in Texas were conducted mostly on aquifer-specific basis, and hence lacked state-wide panoramic view. The aim of this study was to present a qualitative overview of long-term (1930-2010) trends in groundwater levels in Texas and identify spatial patterns by applying different statistical (boxplots, correlation-regression, hierarchical cluster analysis) and geospatial techniques (Moran's I, Local Indicators of Spatial Association) on 136,930 groundwater level observations from Texas Water Development Board's database. State-wide decadal median water-levels declined from about 14 m from land surface in the 1930s to about 36 m in the 2000s. Number of counties with deeper median water-levels (water-level depth>100 m) increased from 2 to 13 between 1930s and 2000s, accompanied by a decrease in number of counties having shallower median water-levels (water-level depth<25 m) from 134 to 113. Water-level declines across Texas, however, mostly followed logarithmic trends marked by leveling-off phenomena in recent times. Assessment of water-levels by Groundwater Management Areas (GMA), management units created to address groundwater depletion issues, indicated hotspots of deep water-levels in Texas Panhandle and GMA 8 since the 1960s. Contrasting patterns in water use, landcover, geology and soil properties distinguished Texas Panhandle from GMA 8. Irrigated agriculture is the major cause of depletion in the Texas Panhandle as compared to increasing urbanization in GMA 8. Overall our study indicated that use of robust spatial and statistical methods can reveal important details about the trends in water-level changes and shed lights on the associated factors. Due to very generic nature, techniques used in this study can also be applied to other areas with similar eco-hydrologic issues to identify regions that warrant future management actions.

The identification of 'hotspots' of heavy metal pollution in soil-rice systems at a regional scale in eastern China

Chinese agricultural soils and crops are suffering from increasing damage from heavy metals, which are introduced from various pollution sources including agriculture, traffic, mining and especially the flourishing private metal recycling industry. In this study, 219 pairs of rice grain and corresponding soil samples were collected from Wenling in Zhejiang Province to identify the spatial relationship and pollution hotspots of Cd, Cu, Ni and Zn in the soil-rice system. The mean soil concentrations of heavy metals were 0.316 mg kg(-1) for Cd, 47.3 mg kg(-1) for Cu, 31.7 mg kg(-1) for Ni and 131 mg kg(-1) for Zn, and the metal concentrations in rice grain were 0.132 mg kg(-1) for Cd, 2.46 mg kg(-1) for Cu, 0.223 mg kg(-1) for Ni and 17.4 mg kg(-1) for Zn. The coefficient of variability (CV) of soil Cd, Cu and rice Cd were 147%, 146% and 180%, respectively, indicating an extensive variability. While the CVs of other metals ranged from 23.4% to 84.3% with a moderate variability. Kriging interpolation procedure and the Local Moran's I index detected the locations of pollution hotspots of these four metals. Cd and Cu had a very similar spatial pattern, with contamination hotspots located simultaneously in the northwestern part of the study area, and there were obvious hotspots for soil Zn in the north area, while in the northeast for soil Ni. The existence of hotspots may be due to industrialization and other anthropogenic activities. An Enrichment Index (EI) was employed to measure the uptake of heavy metals by rice. The results indicated that the accumulation and availability of heavy metals in the soil-rice system may be influenced by both soil heavy metal concentrations and soil physico-chemical properties. Cross-correlograms quantitatively illustrated that EIs were significantly correlated with soil properties. Soil pH and organic matter were the most important factors controlling the uptake of heavy metals by rice. As results, positive measures should be taken into account to control soil pollution and to curtail metal contamination to the food chain in the areas of Wenling, which were the most polluted by toxic metals.

Spatial accessibility and availability measures and statistical properties in the food environment

Spatial accessibility is of increasing interest in the health sciences. This paper addresses the statistical use of spatial accessibility and availability indices. These measures are evaluated via an extensive simulation based on cluster models for local food outlet density. We derived Monte Carlo critical values for several statistical tests based on the indices. In particular we are interested in the ability to make inferential comparisons between different study areas where indices of accessibility and availability are to be calculated. We derive tests of mean difference as well as tests for differences in Moran's I for spatial correlation for each of the accessibility and availability indices. We also apply these new statistical tests to a data example based on two counties in South Carolina for various accessibility and availability measures calculated for food outlets, stores, and restaurants.