This paper,set against the backdrop of expanding urban rail networks and dynamic urban development,focuses on the distribution and evolution of commercial Points of Interests(POIs)within the central urban rail transit...This paper,set against the backdrop of expanding urban rail networks and dynamic urban development,focuses on the distribution and evolution of commercial Points of Interests(POIs)within the central urban rail transit areas of Beijing.The study examines data from four different years-2008,2013,2017,and 2020-to observe the temporal evolution of commercial entities.It identifies stable explanatory variables affecting the distribution and evolution of commercial POIs,which include rail transit accessibility,characteristics of the working and residential population distribution around stations,and the construction intensity in the vicinity of station areas.Through statistical analysis and model building,relatively stable linear regression equations were established,with R2 values generally maintained above 0.5(except for 2017).The study advances our understanding of the influence of rail transit on urban commercial spaces and how this influence shifts with temporal and urban developmental changes.It elucidates the correlation between changes in the number of businesses and spatial configuration,offering insights and information for urban planners and policy makers.This research also serves as a model for exploring the interplay between urban rail transit and commercial spaces in other major cities.展开更多
基金by the National Natural Science Foundation of China"Analyzing morphology and distribution of local shops based on hybrid accessibility"(No.52278002).
文摘This paper,set against the backdrop of expanding urban rail networks and dynamic urban development,focuses on the distribution and evolution of commercial Points of Interests(POIs)within the central urban rail transit areas of Beijing.The study examines data from four different years-2008,2013,2017,and 2020-to observe the temporal evolution of commercial entities.It identifies stable explanatory variables affecting the distribution and evolution of commercial POIs,which include rail transit accessibility,characteristics of the working and residential population distribution around stations,and the construction intensity in the vicinity of station areas.Through statistical analysis and model building,relatively stable linear regression equations were established,with R2 values generally maintained above 0.5(except for 2017).The study advances our understanding of the influence of rail transit on urban commercial spaces and how this influence shifts with temporal and urban developmental changes.It elucidates the correlation between changes in the number of businesses and spatial configuration,offering insights and information for urban planners and policy makers.This research also serves as a model for exploring the interplay between urban rail transit and commercial spaces in other major cities.
