The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
Within oasis-desert ecotone regions,the normalized difference vegetation index(NDVI)is an important parameter for evaluating the growth of vegetation.An accurate quantitative study between NDVI and environmental and a...Within oasis-desert ecotone regions,the normalized difference vegetation index(NDVI)is an important parameter for evaluating the growth of vegetation.An accurate quantitative study between NDVI and environmental and anthropogenic factors is critical for understand the driving factors of vegetation growth in oasis-desert ecotone.In 2016,four periods Landsat 8 OLI_TIRS images,relevant climatological parameters data(air temperature,air relative humidity,wind velocity and accumulated temperature),land cover type data and soil data were selected as proxies.In order to quantify the explanatory power for NDVI spatial and temporal distribution in the southern edge of Dunhuang City and northern side of the Mingsha Mountain,the geographical detector model was used to explain the potential influences of factors versus the spatial distribution of NDVI,and each explanatory variable's relative importance can be calculated.The factor detector results disclose that the spatial distribution of NDVI is primarily dominated by land cover type.The risk detector results show that,high NDVI region is located within woodland.The mean value of NDVI displays an increase and then decrease trend with air temperature increase.With the increase of wind velocity and decrease of air relative humidity,the NDVI value shows a decrease trend.The interactive q values between the two factors are higher than any q value of separated factors.Results also indicate that the strongest interactive effects of NDVI are different in distinct seasons.Consequently,anthropogenic activity is more important than environmental factors on NDVI in oasis-desert ecotone.We also demonstrate that air relative humidity rather than air temperature have played a greater role in NDVI spatial distribution.展开更多
Ecosystem services,which include water yield services,have been incorporated into decision processes of regional land use planning and sustainable development.Spatial pattern characteristics and identification of fact...Ecosystem services,which include water yield services,have been incorporated into decision processes of regional land use planning and sustainable development.Spatial pattern characteristics and identification of factors that influence water yield are the basis for decision making.However,there are limited studies on the driving mechanisms that affect the spatial heterogeneity of ecosystem services.In this study,we used the Hengduan Mountain region in southwest China,with obvious spatial heterogeneity,as the research site.The water yield module in the InVEST software was used to simulate the spatial distribution of water yield.Also,quantitative attribution analysis was conducted for various geomorphological and climatic zones in the Hengduan Mountain region by using the geographical detector method.Influencing factors,such as climate,topography,soil,vegetation type,and land use type and pattern,were taken into consideration for this analysis.Four key findings were obtained.First,water yield spatial heterogeneity is influenced most by climate-related factors,where precipitation and evapotranspiration are the dominant factors.Second,the relative importance of each impact factor to the water yield heterogeneity differs significantly by geomorphological and climatic zones.In flat areas,the influence of evapotranspiration is higher than that of precipitation.As relief increases,the importance of precipitation increases and eventually,it becomes the most influential factor.Evapotranspiration is the most influential factor in a plateau climate zone,while in the mid-subtropical zone,precipitation is the main controlling factor.Third,land use type is also an important driving force in flat areas.Thus,more attention should be paid to urbanization and land use planning,which involves land use changes,to mitigate the impact on water yield spatial pattern.The fourth finding was that a risk detector showed that Primarosol and Anthropogenic soil areas,shrub areas,and areas with slope<5°and 250-350 should be recognized as water yield important zones,while the corresponding elevation values are different among different geomorphological and climatic zones.Therefore,the spatial heterogeneity and influencing factors in different zones should be fully con-sidered while planning the maintenance and protection of water yield services in the Hengduan Mountain region.展开更多
Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study propos...Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金supported by the National Natural Sciences Foundation of China(41871016)the National Key Research and Development Program of China(2017YFC0504801)
文摘Within oasis-desert ecotone regions,the normalized difference vegetation index(NDVI)is an important parameter for evaluating the growth of vegetation.An accurate quantitative study between NDVI and environmental and anthropogenic factors is critical for understand the driving factors of vegetation growth in oasis-desert ecotone.In 2016,four periods Landsat 8 OLI_TIRS images,relevant climatological parameters data(air temperature,air relative humidity,wind velocity and accumulated temperature),land cover type data and soil data were selected as proxies.In order to quantify the explanatory power for NDVI spatial and temporal distribution in the southern edge of Dunhuang City and northern side of the Mingsha Mountain,the geographical detector model was used to explain the potential influences of factors versus the spatial distribution of NDVI,and each explanatory variable's relative importance can be calculated.The factor detector results disclose that the spatial distribution of NDVI is primarily dominated by land cover type.The risk detector results show that,high NDVI region is located within woodland.The mean value of NDVI displays an increase and then decrease trend with air temperature increase.With the increase of wind velocity and decrease of air relative humidity,the NDVI value shows a decrease trend.The interactive q values between the two factors are higher than any q value of separated factors.Results also indicate that the strongest interactive effects of NDVI are different in distinct seasons.Consequently,anthropogenic activity is more important than environmental factors on NDVI in oasis-desert ecotone.We also demonstrate that air relative humidity rather than air temperature have played a greater role in NDVI spatial distribution.
基金National Basic Research Program of China,No.2015CB452702National Natural Science Foundation of China,No.41571098.No.41530749+1 种基金National Key R&D Program of China,No.2017YFC1502903Major Consulting Project of Strategic Development Institute,Chinese Academy of Sciences,No.Y02015001。
文摘Ecosystem services,which include water yield services,have been incorporated into decision processes of regional land use planning and sustainable development.Spatial pattern characteristics and identification of factors that influence water yield are the basis for decision making.However,there are limited studies on the driving mechanisms that affect the spatial heterogeneity of ecosystem services.In this study,we used the Hengduan Mountain region in southwest China,with obvious spatial heterogeneity,as the research site.The water yield module in the InVEST software was used to simulate the spatial distribution of water yield.Also,quantitative attribution analysis was conducted for various geomorphological and climatic zones in the Hengduan Mountain region by using the geographical detector method.Influencing factors,such as climate,topography,soil,vegetation type,and land use type and pattern,were taken into consideration for this analysis.Four key findings were obtained.First,water yield spatial heterogeneity is influenced most by climate-related factors,where precipitation and evapotranspiration are the dominant factors.Second,the relative importance of each impact factor to the water yield heterogeneity differs significantly by geomorphological and climatic zones.In flat areas,the influence of evapotranspiration is higher than that of precipitation.As relief increases,the importance of precipitation increases and eventually,it becomes the most influential factor.Evapotranspiration is the most influential factor in a plateau climate zone,while in the mid-subtropical zone,precipitation is the main controlling factor.Third,land use type is also an important driving force in flat areas.Thus,more attention should be paid to urbanization and land use planning,which involves land use changes,to mitigate the impact on water yield spatial pattern.The fourth finding was that a risk detector showed that Primarosol and Anthropogenic soil areas,shrub areas,and areas with slope<5°and 250-350 should be recognized as water yield important zones,while the corresponding elevation values are different among different geomorphological and climatic zones.Therefore,the spatial heterogeneity and influencing factors in different zones should be fully con-sidered while planning the maintenance and protection of water yield services in the Hengduan Mountain region.
基金the National Key Research and Development Program of China(Grant No.2022YFF1302405)the Yunnan Province Key Research and Development Program(Grant No.202203AC100005)+1 种基金the National Natural Science Foundation of China(Grant No.42061005,42067033)Applied Basic Research Programs of Yunnan Province(Grant No.202101AT070110,202001BB050073).
文摘Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.
