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- Title
基于大数据的城市功能区人口时空聚散模式研究.
- Authors
王润泽; 周 鹏; 潘 悦; 林奕晨; 项 晓
- Abstract
Urban functional area and population mobility are hot research topics in the fields of behavioural geography and urban planning. The phenomenon of population aggregation and dispersion in different urban functional areas is a key issue. Based on the POI and Tencent location based service (LBS) big data, this paper takes the main urban area of Wuhan as the study area, and adopts the functional density index and functional dominance index to identify the urban functional areas. The change characteristics ol population flow in urban functional areas are analyzed through spatial correlation, and the cluster analysis method is used to summarize the spatiotemporal aggregation and dispersion patterns of population flow. The research results show that: 1) the functional mixing degree ol central urban area is relatively high;2) influenced by the spatial and temporal needs of the crowd, the patterns of population flow in different urban functional areas show some differences;3) according to the trend ol population aggregation and dispersion in different urban functional areas and the characteristics ol their composition, six patterns of population aggregation and dispersion are obtained, namely, public dominated-aggregation and dispersion fluctuation, business dominated-continuous aggregation, residential dominated-continuous aggregation, green space dominated-aggregation and dispersion alternation, commercial dominated-dynam込 balance and industrial dominated-aggregation first and then dispersion. This study is of great significance lor optimizing urban spatial layout, allocating urban resources and improving urban operation elliciency.
- Subjects
WUHAN (China); TENCENT Holdings Ltd.; URBAN planning; CITIES &; towns; URBAN geography; CITY dwellers; CLUSTER analysis (Statistics); PUBLIC spaces; URBAN density
- Publication
Geography & Geographic Information Science, 2022, Vol 38, Issue 1, p45
- ISSN
1672-0504
- Publication type
Article
- DOI
10.3969/j.issn.1672--0504.2022.01.007