This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of glo...This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.展开更多
After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important ro...After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important role in China’s national economy. The development of high-level</span><span style="font-family:"font-size:10pt;"> </span><span style="font-family:Verdana;">technological industry plays a leading role in guiding the transformation of </span><span style="font-family:Verdana;">China’s economy from “investment-driven” to “technology-driven”. The</span><span style="font-family:Verdana;"> high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.展开更多
In order to clarify the statistical pattern by which landfalling strong tropical cyclones(LSTCs)influenced the catastrophic migrations of rice brown planthopper(BPH),Nilaparvata lugens(stl)in China,the data of the L...In order to clarify the statistical pattern by which landfalling strong tropical cyclones(LSTCs)influenced the catastrophic migrations of rice brown planthopper(BPH),Nilaparvata lugens(stl)in China,the data of the LSTCs in China and the lighting catches of BPH that covered the main Chinese rice-growing regions from 1979 to 2008 were collected and analyzed in this work with the assistance of ArcGIS9.3,a software of geographic information system.The results were as follows:(1)In China,there were 220 strong tropical cyclones that passed the main rice-growing regions and 466 great events of BPH’s immigration in the 30 years from 1979 to 2008.73 of them resulted in the occurrence of BPH’s catastrphic migration(CM)events directly and 147 of them produced indirect effect on the migrations.(2)The number of the LSTCs was variable in different years during 1979 to 2008 and their influence was not the same in the BPH’s northward and southward migrations in the years.In the 30 years,the LSTCs brought more obvious influence on the migrations in 1980,1981,2005,2006 and 2007.The influence was the most obvious in2007 and all of the 7 LSTCs produced remarkable impact on the CMs of BPH’s populations.The effect of the LSTCs on the northward immigration of BPH’s populations was the most serious in 2006 and the influence on the southward immigration was the most remarkable in 2005.(3)In these years,the most of LSTCs occurred in July,August and September and great events of BPH's immigration occurred most frequently in the same months.The LSTCs played a more important role on the CM of BPH’s populations in the three months than in other months.(4)The analysis on the spatial distribution of the LSTCs and BPH’s immigration events for the different provinces showed that the BPH’s migrations in the main rice-growing regions of the Southeastern China were influenced by the LSTCs and the impact was different with the change of their spatial probability distribution during their passages.The most serious influence of the LSTCs on the BPH’s migrations occurred in Guangdong and Fujian provinces.(5)The statistical results indicated that a suitable insect source is an indispensable condition of the CMs of BPH when a LSTC influenced a rice-growing region.展开更多
The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the c...The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).展开更多
Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased ...Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.展开更多
Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiw...Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.展开更多
This research demonstrated quantitative methods of geospatial analysis applicable to carbon sequestration and storage in the conterminous United Sates. We identified national-scale NEP （net ecosystem production） cha...This research demonstrated quantitative methods of geospatial analysis applicable to carbon sequestration and storage in the conterminous United Sates. We identified national-scale NEP （net ecosystem production） changes for conversions to and from crop, and land in frequent conversion among forest, wetland, pasture and rangeland. The trend showed an increase in the margins of the Corn Belt states and coincided with land conversion from previous non-cropland to cropland in the United States. This research will not only improve the engineering understanding of carbon dioxide removal options involving the terrestrial biosphere, but will also inform decision-making in the carbon emission impacts. Therefore, it will provide a spatio-temporal reference for analyzing the national-level carbon exchange systems in the United States.展开更多
Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and...Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran’s I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff’s scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.展开更多
Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptom...Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].展开更多
In the 21st century,climate change has exacerbated global instability,leading to a rise in landslide occurrences.In Bangladesh,mountainous areas such as Bandarban experience significant landslides during the monsoon s...In the 21st century,climate change has exacerbated global instability,leading to a rise in landslide occurrences.In Bangladesh,mountainous areas such as Bandarban experience significant landslides during the monsoon season.This study seeks to evaluate landslide susceptibility in Bandarban and identify hotspots for optimal landslide hazard mitigation.This study examined landslide susceptibility using the analytical hierarchy process(AHP)and spatial weighted overlay(SWO).Ten conditioning factors were considered,with AHP based on responses from 100 key respondents.Using field surveys and high-resolution satellite images,280 landslide occurrence samples were collected to rank the subfactors.Using AHP-derived weights of factors and subfactors,the SWO approach was used to create the landslide susceptibility map(LSM).The Getis-Ord(Gi*)spatial statistics was then used to generate landslide susceptibility hotspots.The result showed that human influence weight 17.02%,making it the most crucial factor in landslide susceptibility.AHP-derived weights were reliable because their consistency ratio was<0.1.According to the study,59.86% of the area is moderately susceptible,20.06%is high,and 4.31%is very high.The validation of LSM by ROC curve found excellent performance(AUC=0.93)of the approaches.Specifically,63.8%of very high susceptibility areas and 33.26%of high susceptibility areas were found within the hotspot zones with 99%confidence.The research showed the combined use of field samples and remote sensing-based spatial variables improved the accuracy of LSM.These findings can be useful for ensuring proper land use planning and implementation of landslide hazard mitigation measures.展开更多
Soil nitrogen(N) is critical to ecosystem services and environmental quality. Hotspots of soil N in areas with high soil moisture have been widely studied, however, their spatial distribution and their linkage with so...Soil nitrogen(N) is critical to ecosystem services and environmental quality. Hotspots of soil N in areas with high soil moisture have been widely studied, however, their spatial distribution and their linkage with soil N variation have seldom been examined at a catchment scale in areas with low soil water content. We investigated the spatial variation of soil N and its hotspots in a mixed land cover catchment on the Chinese Loess Plateau and used multiple statistical methods to evaluate the effects of the critical environmental factors on soil N variation and potential hotspots. The results demonstrated that land cover, soil moisture, elevation, plan curvature and flow accumulation were the dominant factors affecting the spatial variation of soil nitrate(NN), while land cover and slope aspect were the most important factors impacting the spatial distribution of soil ammonium(AN) and total nitrogen(TN). In the studied catchment, the forestland, gully land and grassland were found to be the potential hotspots of soil NN, AN and TN accumulation, respectively. We concluded that land cover and slope aspect could be proxies to determine the potential hotspots of soil N at the catchment scale. Overall, land cover was the most important factor that resulted in the spatial variations of soil N. The findings may help us to better understand the environmental factors affecting soil N hotspots and their spatial variation at the catchment scale in terrestrial ecosystems.展开更多
Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS a...Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.展开更多
This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical-statistical investigations, simulat...This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical-statistical investigations, simulations of such structures play an important role. In these simulations various methods and models are applied, namely the RSA model, sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis-Hastings algorithm. The statistical description of real and simulated particle systems uses ideas of the mathematical theories of random sets and point processes. This leads to characteristics such as volume fraction or porosity, covariance, contact distribution functions, specific connectivity number from the random set approach and intensity, pair correlation function and mark correlation functions from the point process approach. Some of them can be determined stereologically using planar sections, while others can only be obtained using three-dimensional data and 3D image analysis. They are valuable tools for fitting models to empirical data and, consequently, for understanding various materials, biological structures, porous media and other practically important spatial structures.展开更多
Hospital is an important factor of people’s livelihood security,and the spatial layout of hospitals effectively ensures the medical convenience for residents.Location entropy and mathematical statistical analysis are...Hospital is an important factor of people’s livelihood security,and the spatial layout of hospitals effectively ensures the medical convenience for residents.Location entropy and mathematical statistical analysis are used to study spatial distribution of hospitals.The results display that the distribution of medical facilities in Handan City is at a disadvantage level in Hebei Province,and medical facilities arr concentrated in the plain area.The layout of grade 3A hospitals in Hebei Province is characterized by urban centralization,and it is stronger in the east and weaker in the west.There is no medical facilities in Feixiang District of Handan City,and layout of medical facilities in Hanshan District and Congtai District is at advantage level of Handan City.The built-up area is the influencing factor for the distribution of medical resources.展开更多
文摘This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.
文摘After 30 years of economic development, the high-tech industry has played </span><span style="font-family:Verdana;">an </span><span style="font-family:Verdana;">important role in China’s national economy. The development of high-level</span><span style="font-family:"font-size:10pt;"> </span><span style="font-family:Verdana;">technological industry plays a leading role in guiding the transformation of </span><span style="font-family:Verdana;">China’s economy from “investment-driven” to “technology-driven”. The</span><span style="font-family:Verdana;"> high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.
基金National Natural Science Foundation of China(41075086,30671340)National Meteorological Public Professional Science and Technology Program of China(GYHY201006026)+1 种基金Agricultural Science and Technology Independent Innovation Foundation in Jiangsu Province(CX(12)3056)Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In order to clarify the statistical pattern by which landfalling strong tropical cyclones(LSTCs)influenced the catastrophic migrations of rice brown planthopper(BPH),Nilaparvata lugens(stl)in China,the data of the LSTCs in China and the lighting catches of BPH that covered the main Chinese rice-growing regions from 1979 to 2008 were collected and analyzed in this work with the assistance of ArcGIS9.3,a software of geographic information system.The results were as follows:(1)In China,there were 220 strong tropical cyclones that passed the main rice-growing regions and 466 great events of BPH’s immigration in the 30 years from 1979 to 2008.73 of them resulted in the occurrence of BPH’s catastrphic migration(CM)events directly and 147 of them produced indirect effect on the migrations.(2)The number of the LSTCs was variable in different years during 1979 to 2008 and their influence was not the same in the BPH’s northward and southward migrations in the years.In the 30 years,the LSTCs brought more obvious influence on the migrations in 1980,1981,2005,2006 and 2007.The influence was the most obvious in2007 and all of the 7 LSTCs produced remarkable impact on the CMs of BPH’s populations.The effect of the LSTCs on the northward immigration of BPH’s populations was the most serious in 2006 and the influence on the southward immigration was the most remarkable in 2005.(3)In these years,the most of LSTCs occurred in July,August and September and great events of BPH's immigration occurred most frequently in the same months.The LSTCs played a more important role on the CM of BPH’s populations in the three months than in other months.(4)The analysis on the spatial distribution of the LSTCs and BPH’s immigration events for the different provinces showed that the BPH’s migrations in the main rice-growing regions of the Southeastern China were influenced by the LSTCs and the impact was different with the change of their spatial probability distribution during their passages.The most serious influence of the LSTCs on the BPH’s migrations occurred in Guangdong and Fujian provinces.(5)The statistical results indicated that a suitable insect source is an indispensable condition of the CMs of BPH when a LSTC influenced a rice-growing region.
基金financially supported by the Natural Science Foundation of Guizhou Province[Grant No.J(2009)2029]Leading Academic Discipline Program+2 种基金211 Project for Guizhou University(the 3rd phase)Young Scientists Project of Natural Science Foundation of Guizhou University(Grant No.2009072)Young Scientists Foundation Project of the College of Resources and Environmental Engineering of Guizhou University(Grant No.ZHY0902)
文摘The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).
文摘Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.
文摘Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.
文摘This research demonstrated quantitative methods of geospatial analysis applicable to carbon sequestration and storage in the conterminous United Sates. We identified national-scale NEP （net ecosystem production） changes for conversions to and from crop, and land in frequent conversion among forest, wetland, pasture and rangeland. The trend showed an increase in the margins of the Corn Belt states and coincided with land conversion from previous non-cropland to cropland in the United States. This research will not only improve the engineering understanding of carbon dioxide removal options involving the terrestrial biosphere, but will also inform decision-making in the carbon emission impacts. Therefore, it will provide a spatio-temporal reference for analyzing the national-level carbon exchange systems in the United States.
文摘Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran’s I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff’s scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.
基金Daniel Shafiee Kermany,Ju Young Ahn,Matthew Vasquez,Lin Wang,Kai Liu,Raksha Raghunathan,Jianting Sheng,Hong Zhao,and Stephen Tin Chi Wong are supported by NCI U01CA252553,NCI R01CA238727,NCI R01CA177909,NCI R01CA244413John S.Dunn Research Foundation,and Ting Tsung and Wei Fong Chao Foundation+3 种基金Xiang Hong-Fei Zhang,Zhan Xu,Xiaoxin Hao,Weijie Zhang are supported by US Department of Defense DAMD W81XWH-16-1-0073(Era of Hope Scholarship)NCI R01CA183878,NCI R01CA251950,NCI U01CA252553,DAMD W81XWH-20-1-0375Breast Cancer Research Foundation,and McNair Medical Institute.Vahid Afshar-Kharghan,Min Soon Cho,Wendolyn Carlos-AlcaldeHani Lee are supported by NCI R01CA177909,NCI R01CA016672,NCI R01CA275762,and NCI P50CA217685.
文摘Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].
文摘In the 21st century,climate change has exacerbated global instability,leading to a rise in landslide occurrences.In Bangladesh,mountainous areas such as Bandarban experience significant landslides during the monsoon season.This study seeks to evaluate landslide susceptibility in Bandarban and identify hotspots for optimal landslide hazard mitigation.This study examined landslide susceptibility using the analytical hierarchy process(AHP)and spatial weighted overlay(SWO).Ten conditioning factors were considered,with AHP based on responses from 100 key respondents.Using field surveys and high-resolution satellite images,280 landslide occurrence samples were collected to rank the subfactors.Using AHP-derived weights of factors and subfactors,the SWO approach was used to create the landslide susceptibility map(LSM).The Getis-Ord(Gi*)spatial statistics was then used to generate landslide susceptibility hotspots.The result showed that human influence weight 17.02%,making it the most crucial factor in landslide susceptibility.AHP-derived weights were reliable because their consistency ratio was<0.1.According to the study,59.86% of the area is moderately susceptible,20.06%is high,and 4.31%is very high.The validation of LSM by ROC curve found excellent performance(AUC=0.93)of the approaches.Specifically,63.8%of very high susceptibility areas and 33.26%of high susceptibility areas were found within the hotspot zones with 99%confidence.The research showed the combined use of field samples and remote sensing-based spatial variables improved the accuracy of LSM.These findings can be useful for ensuring proper land use planning and implementation of landslide hazard mitigation measures.
基金financially supported by the National key research and development program (2017YFD0800502)the National Natural Science Foundation of China (Grant Nos. 41573067, 41790444, 41471189, 31700414)
文摘Soil nitrogen(N) is critical to ecosystem services and environmental quality. Hotspots of soil N in areas with high soil moisture have been widely studied, however, their spatial distribution and their linkage with soil N variation have seldom been examined at a catchment scale in areas with low soil water content. We investigated the spatial variation of soil N and its hotspots in a mixed land cover catchment on the Chinese Loess Plateau and used multiple statistical methods to evaluate the effects of the critical environmental factors on soil N variation and potential hotspots. The results demonstrated that land cover, soil moisture, elevation, plan curvature and flow accumulation were the dominant factors affecting the spatial variation of soil nitrate(NN), while land cover and slope aspect were the most important factors impacting the spatial distribution of soil ammonium(AN) and total nitrogen(TN). In the studied catchment, the forestland, gully land and grassland were found to be the potential hotspots of soil NN, AN and TN accumulation, respectively. We concluded that land cover and slope aspect could be proxies to determine the potential hotspots of soil N at the catchment scale. Overall, land cover was the most important factor that resulted in the spatial variations of soil N. The findings may help us to better understand the environmental factors affecting soil N hotspots and their spatial variation at the catchment scale in terrestrial ecosystems.
文摘Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.
文摘This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical-statistical investigations, simulations of such structures play an important role. In these simulations various methods and models are applied, namely the RSA model, sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis-Hastings algorithm. The statistical description of real and simulated particle systems uses ideas of the mathematical theories of random sets and point processes. This leads to characteristics such as volume fraction or porosity, covariance, contact distribution functions, specific connectivity number from the random set approach and intensity, pair correlation function and mark correlation functions from the point process approach. Some of them can be determined stereologically using planar sections, while others can only be obtained using three-dimensional data and 3D image analysis. They are valuable tools for fitting models to empirical data and, consequently, for understanding various materials, biological structures, porous media and other practically important spatial structures.
基金Sponsored by the Construction Project of Postgraduate Demonstration Course in Hebei Province (KCJSX2020081)。
文摘Hospital is an important factor of people’s livelihood security,and the spatial layout of hospitals effectively ensures the medical convenience for residents.Location entropy and mathematical statistical analysis are used to study spatial distribution of hospitals.The results display that the distribution of medical facilities in Handan City is at a disadvantage level in Hebei Province,and medical facilities arr concentrated in the plain area.The layout of grade 3A hospitals in Hebei Province is characterized by urban centralization,and it is stronger in the east and weaker in the west.There is no medical facilities in Feixiang District of Handan City,and layout of medical facilities in Hanshan District and Congtai District is at advantage level of Handan City.The built-up area is the influencing factor for the distribution of medical resources.
