Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels.However,built environment-related factors have primarily...Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels.However,built environment-related factors have primarily been examined in developed countries,resulting in a limited understanding of the phenomenon in the context of developing countries.Methodologically,these studies mostly used global regression models,which failed to incorporate spatial autocorrelation and spatial heterogeneity.Additionally,some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections.Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity,Dhaka,the capital of a developing country:Bangladesh.Using secondary pedestrian crash data,the study applied one global non-spatial model,two global spatial regression models,and two local spatial regression models following a comprehensive spatial regression modeling framework.The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density,mixed and recreational land use density,primary road density,major intersection density,and share of non-motorized modes.Except for the last factor,all the other ones were positively related to pedestrian crash density.Among the five models used in this study,the multiscale geographically weighted regression(MGWR)performed the best as it calibrated each local relationship with a distant spatial scale parameter.The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.展开更多
China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exi...China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.展开更多
文章以河谷城市甘肃省兰州市为例,在测度“三生”空间冲突强度并揭示多尺度分异机制的基础上提出冲突协调策略。结果显示:①“三生”空间格局演化与地域分异显著。生活空间的增加主要源于对生态空间和生产空间的侵占。②兰州市“三生”...文章以河谷城市甘肃省兰州市为例,在测度“三生”空间冲突强度并揭示多尺度分异机制的基础上提出冲突协调策略。结果显示:①“三生”空间格局演化与地域分异显著。生活空间的增加主要源于对生态空间和生产空间的侵占。②兰州市“三生”空间冲突时空演化与地类分异显著。时序上,“三生”空间冲突强度呈持续上升态势,整体处于基本可控级别;空间上,形成了“一心、两翼、多轴带”的空间冲突格局;就地类而言,城镇的生活空间与生产空间冲突水平较高。③兰州市“三生”空间冲突影响因素尺度效应和异质性明显。高程、坡度、地形起伏度等对“三生”空间冲突具有抑制作用;人口密度、人均G D P、夜间灯光指数等因素加剧了区域“三生”空间冲突程度。展开更多
鲣(Katsuwonus pelamis)是中西太平洋金枪鱼围网捕捞的重要资源,其资源分布受环境影响明显。为探索环境对鲣渔获率影响的空间异质性特征,利用中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)所公布的200...鲣(Katsuwonus pelamis)是中西太平洋金枪鱼围网捕捞的重要资源,其资源分布受环境影响明显。为探索环境对鲣渔获率影响的空间异质性特征,利用中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)所公布的2005—2019年中西太平洋金枪鱼围网综合的1°×1°渔业及海洋环境数据,对标准化后的环境因子及渔获率选用多尺度地理加权回归(Multi-scale Geographically Weighted Regression,MGWR)方法进行研究。结果表明:1)与传统广义加性模型(Generalized Additive Model,GAM)相比,考虑环境影响空间异质性问题的地理加权回归模型(Geographically Weighted Regression,GWR)和MGWR拟合优度(R^(2))有明显提升,校正后拟合优度(Adjusted R^(2))分别为0.273、0.846和0.871,且拟合结果的空间分布形态更符合真实情况。2)各环境因子对鲣资源分布存在显著的空间非平稳性影响。各海洋环境因子对鲣渔获率分布影响的空间异质性程度(各环境变量变异系数大小)依次为水下55 m东西向海流速度(Sea water X velocity at 55 m depth,U55)>海表面温度(Sea surface temperature,SST)>净初级生产力(Net primary productivity,NPP)>100 m盐度(Sea water salinity at 100 m depth,S100)>55 m南北向海流速度(Sea water Y velocity at 55 m depth,V55)。3)各环境因子的影响存在明显尺度效应差异,NPP的作用尺度为44,其次为S100和U55(均为48),SST的为54,V55为全局尺度。4)总体上,S100、NPP、SST、V55和U55对鲣渔获率正向影响比例依次为73.5%、64.8%、66.8%、80.8%和32.3%;其中S100、NPP和SST对鲣渔获率空间分布的影响相似,具体表现为东西向差异,170°E以西主要为正向影响,170°E以东为负向影响;U55为负向影响为主的因子。展开更多
作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用...作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用量、公交供给特性、换乘可达性以及地铁网络特性对换乘需求的影响及其空间异质性。研究结果表明:多尺度地理加权回归模型相比于线性回归模型以及传统的地理加权回归模型具有更强的解释力,地铁公交换乘量的影响因素具有显著的空间异质性;公交运营班次供给以及可达站点数量的提升能够促进地铁公交间的换乘;公交站点周边住宅型POI(Point of Interest)数量在城市外围区域对换乘量起到促进作用,企业型POI数量则对换乘量起到抑制作用;共享单车借用量会抑制地铁与公交之间的换乘需求,特别是在与中心城区联系紧密的城市外围区域。展开更多
基金This research is funded by Bangladesh University of Engineering and Technology(BUET).
文摘Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels.However,built environment-related factors have primarily been examined in developed countries,resulting in a limited understanding of the phenomenon in the context of developing countries.Methodologically,these studies mostly used global regression models,which failed to incorporate spatial autocorrelation and spatial heterogeneity.Additionally,some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections.Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity,Dhaka,the capital of a developing country:Bangladesh.Using secondary pedestrian crash data,the study applied one global non-spatial model,two global spatial regression models,and two local spatial regression models following a comprehensive spatial regression modeling framework.The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density,mixed and recreational land use density,primary road density,major intersection density,and share of non-motorized modes.Except for the last factor,all the other ones were positively related to pedestrian crash density.Among the five models used in this study,the multiscale geographically weighted regression(MGWR)performed the best as it calibrated each local relationship with a distant spatial scale parameter.The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.
基金Under the auspices of the Philosophy and Social Science Planning Project of Guizhou,China(No.21GZZD59)。
文摘China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.
文摘文章以河谷城市甘肃省兰州市为例,在测度“三生”空间冲突强度并揭示多尺度分异机制的基础上提出冲突协调策略。结果显示:①“三生”空间格局演化与地域分异显著。生活空间的增加主要源于对生态空间和生产空间的侵占。②兰州市“三生”空间冲突时空演化与地类分异显著。时序上,“三生”空间冲突强度呈持续上升态势,整体处于基本可控级别;空间上,形成了“一心、两翼、多轴带”的空间冲突格局;就地类而言,城镇的生活空间与生产空间冲突水平较高。③兰州市“三生”空间冲突影响因素尺度效应和异质性明显。高程、坡度、地形起伏度等对“三生”空间冲突具有抑制作用;人口密度、人均G D P、夜间灯光指数等因素加剧了区域“三生”空间冲突程度。
文摘鲣(Katsuwonus pelamis)是中西太平洋金枪鱼围网捕捞的重要资源,其资源分布受环境影响明显。为探索环境对鲣渔获率影响的空间异质性特征,利用中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)所公布的2005—2019年中西太平洋金枪鱼围网综合的1°×1°渔业及海洋环境数据,对标准化后的环境因子及渔获率选用多尺度地理加权回归(Multi-scale Geographically Weighted Regression,MGWR)方法进行研究。结果表明:1)与传统广义加性模型(Generalized Additive Model,GAM)相比,考虑环境影响空间异质性问题的地理加权回归模型(Geographically Weighted Regression,GWR)和MGWR拟合优度(R^(2))有明显提升,校正后拟合优度(Adjusted R^(2))分别为0.273、0.846和0.871,且拟合结果的空间分布形态更符合真实情况。2)各环境因子对鲣资源分布存在显著的空间非平稳性影响。各海洋环境因子对鲣渔获率分布影响的空间异质性程度(各环境变量变异系数大小)依次为水下55 m东西向海流速度(Sea water X velocity at 55 m depth,U55)>海表面温度(Sea surface temperature,SST)>净初级生产力(Net primary productivity,NPP)>100 m盐度(Sea water salinity at 100 m depth,S100)>55 m南北向海流速度(Sea water Y velocity at 55 m depth,V55)。3)各环境因子的影响存在明显尺度效应差异,NPP的作用尺度为44,其次为S100和U55(均为48),SST的为54,V55为全局尺度。4)总体上,S100、NPP、SST、V55和U55对鲣渔获率正向影响比例依次为73.5%、64.8%、66.8%、80.8%和32.3%;其中S100、NPP和SST对鲣渔获率空间分布的影响相似,具体表现为东西向差异,170°E以西主要为正向影响,170°E以东为负向影响;U55为负向影响为主的因子。
文摘作为多模式公交的重要组成部分,地铁与地面公交的衔接换乘是城市客运交通一体化的关键环节。本文基于南京市多源数据分析地铁与公交之间的换乘需求,以地铁公交换乘量为因变量构建多尺度地理加权回归模型,揭示地铁站点周边共享单车使用量、公交供给特性、换乘可达性以及地铁网络特性对换乘需求的影响及其空间异质性。研究结果表明:多尺度地理加权回归模型相比于线性回归模型以及传统的地理加权回归模型具有更强的解释力,地铁公交换乘量的影响因素具有显著的空间异质性;公交运营班次供给以及可达站点数量的提升能够促进地铁公交间的换乘;公交站点周边住宅型POI(Point of Interest)数量在城市外围区域对换乘量起到促进作用,企业型POI数量则对换乘量起到抑制作用;共享单车借用量会抑制地铁与公交之间的换乘需求,特别是在与中心城区联系紧密的城市外围区域。
