文献类型：期刊文献

英文题名：Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

作者：Huang, Zhou[1,2];Yin, Ganmin[1,2];Peng, Xia[3,4];Zhou, Xiao[1,2];Dong, Quanhua[1,2]

第一作者：Huang, Zhou

通讯作者：Peng, X[1]

机构：[1]Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China;[2]Peking Univ, Beijing Key Lab Spatial Informat Integrat & Its Ap, Beijing, Peoples R China;[3]Beijing Union Univ, Tourism Coll, Beijing, Peoples R China;[4]Beijing Union Univ, Tourism Coll, Beijing 100101, Peoples R China

第一机构：Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Tourism Coll, Beijing 100101, Peoples R China.|[1141732]北京联合大学旅游学院;[11417]北京联合大学;

年份：0

外文期刊名：ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE

收录：;Scopus(收录号：2-s2.0-85149253204);WOS:【SSCI(收录号:WOS:000934955000001)】；

语种：英文

外文关键词：Urban planning; commercial agglomeration attractiveness; bipartite graph centrality; supply and demand; human-environment relationship; big geo-data

摘要：Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R-2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human-environment relationship and guide urban policymakers in business planning.