Urban resilience assesses a city’s ability to withstand unknown risks.Scholars are not comprehensive in assessing urban resilience,and they lack consideration of population resilience.This study investigated 110 pref...Urban resilience assesses a city’s ability to withstand unknown risks.Scholars are not comprehensive in assessing urban resilience,and they lack consideration of population resilience.This study investigated 110 prefecturelevel cities in the Yangtze River Economic Belt(YREB)as study areas.We calculated the YREB’s level of urban resilience based on the aspects of“economy-society-population-ecology-infrastructure”,which ensured that the comprehensive evaluation of urban resilience is complete and sufficient.The spatio-temporal evolution of urban resilience was analyzed using exploratory spatial data.Geodetectors were used to investigate the impact of several indicators,focusing on economic,social,population,ecological,and infrastructure factors,on urban resilience.The results showed that the urban resilience of the YREB has maintained a slow upward trend from 2005 to 2018,and the average urban resilience of the YREB has risen from 0.2442 to 0.2560.The resilience gap between cities in the study region increased initially and then decreased.The dominant factor in the spatial differentiation of urban resilience was the economic factors,followed by the population factors.Urban resilience has been clarified and an evaluation index system is constructed,which can provide an effective reference for the evaluation of urban resilience among countries around the world.Based on this,factors that optimize urban resilience are configured,and the regional and national sustainable development can be promoted.展开更多
Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to o...Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34- C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM2.5. The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM2.5 pollution on lung cancer. The Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM2.5 and is a useful data source for environmental health research.展开更多
A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At pre...A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).展开更多
This paper principally focuses on the morphological differences,spatial pattern and regional types of rural settlements in Xuzhou City of Jiangsu Province in China.Using satellite images of Xuzhou City taken in 2007 a...This paper principally focuses on the morphological differences,spatial pattern and regional types of rural settlements in Xuzhou City of Jiangsu Province in China.Using satellite images of Xuzhou City taken in 2007 and 2008 and models of exploratory spatial data analysis(ESDA) and spatial metrics,the paper conducts a quantitative analysis of the morphological pattern of rural settlements,and finds significant characteristics.First,rural settlements in Xuzhou City are significantly agglomerated in terms of their spatial distribution;meanwhile,there is significant variation in the geographical density distribution.Second,the scale of rural settlements in Xuzhou City is larger than the average in Jiangsu Province,and the histogram of the scale data is more even and more like a gamma distribution.There are a significant high-value cluster in the scale distribution,and local negative correlation between the scale and density distribution of rural settlements in Xuzhou City.Third,the morphology of rural settlements in Xuzhou City shows relative regularity with good connection and integrity,but the spatial variation of the morphology is anisotropic.Finally,according to the characteristics of density,scale,and form of rural settlements,the rural settlements of Xuzhou City are divided into three types:A high-density and point-scattered type,a low-density and cluster-like type and a mass-like and sparse type.The research findings could be used as the scientific foundation for rural planning and community rebuilding,particularly in less-developed areas.展开更多
基金I would like to thank the National Natural Science Foundation of China(Grant No.42061041)for the funding.
文摘Urban resilience assesses a city’s ability to withstand unknown risks.Scholars are not comprehensive in assessing urban resilience,and they lack consideration of population resilience.This study investigated 110 prefecturelevel cities in the Yangtze River Economic Belt(YREB)as study areas.We calculated the YREB’s level of urban resilience based on the aspects of“economy-society-population-ecology-infrastructure”,which ensured that the comprehensive evaluation of urban resilience is complete and sufficient.The spatio-temporal evolution of urban resilience was analyzed using exploratory spatial data.Geodetectors were used to investigate the impact of several indicators,focusing on economic,social,population,ecological,and infrastructure factors,on urban resilience.The results showed that the urban resilience of the YREB has maintained a slow upward trend from 2005 to 2018,and the average urban resilience of the YREB has risen from 0.2442 to 0.2560.The resilience gap between cities in the study region increased initially and then decreased.The dominant factor in the spatial differentiation of urban resilience was the economic factors,followed by the population factors.Urban resilience has been clarified and an evaluation index system is constructed,which can provide an effective reference for the evaluation of urban resilience among countries around the world.Based on this,factors that optimize urban resilience are configured,and the regional and national sustainable development can be promoted.
文摘Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34- C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM2.5. The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM2.5 pollution on lung cancer. The Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM2.5 and is a useful data source for environmental health research.
文摘A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).
基金Under the auspices of National Natural Science Foundation of China(No.41071116)Humanity and Social ScienceFoundation of Ministry of Education(No.09YJC790225,11YJA630008)
文摘This paper principally focuses on the morphological differences,spatial pattern and regional types of rural settlements in Xuzhou City of Jiangsu Province in China.Using satellite images of Xuzhou City taken in 2007 and 2008 and models of exploratory spatial data analysis(ESDA) and spatial metrics,the paper conducts a quantitative analysis of the morphological pattern of rural settlements,and finds significant characteristics.First,rural settlements in Xuzhou City are significantly agglomerated in terms of their spatial distribution;meanwhile,there is significant variation in the geographical density distribution.Second,the scale of rural settlements in Xuzhou City is larger than the average in Jiangsu Province,and the histogram of the scale data is more even and more like a gamma distribution.There are a significant high-value cluster in the scale distribution,and local negative correlation between the scale and density distribution of rural settlements in Xuzhou City.Third,the morphology of rural settlements in Xuzhou City shows relative regularity with good connection and integrity,but the spatial variation of the morphology is anisotropic.Finally,according to the characteristics of density,scale,and form of rural settlements,the rural settlements of Xuzhou City are divided into three types:A high-density and point-scattered type,a low-density and cluster-like type and a mass-like and sparse type.The research findings could be used as the scientific foundation for rural planning and community rebuilding,particularly in less-developed areas.
