Land (Sep 2024)

Research on the Correlation between the Dynamic Distribution Patterns of Urban Population Density and Land Use Morphology Based on Human–Land Big Data: A Case Study of the Shanghai Central Urban Area

  • Yi Shi,
  • Yi Zheng,
  • Daijun Chen,
  • Junyan Yang,
  • Yue Cao,
  • Ao Cui

DOI
https://doi.org/10.3390/land13101547
Journal volume & issue
Vol. 13, no. 10
p. 1547

Abstract

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The dynamic distribution of urban population density and the interaction with land use elements involve mutual constraints and guidance. However, in the existing research on the relationship between urban population density and land use, the discussion on the distribution patterns of urban population density typically spans long time periods and uses large spatial units, lacking analysis of the dynamic changes in population density within high granularity land parcels over a day. In studies related to the urban built environment, the complex relationships between different-dimensional land use elements and the dynamic distribution of population density also need further exploration. To address these bottlenecks, this study takes Shanghai’s central urban area as an example. Based on 24 h mobile signaling data on weekdays, weekends, and typical holidays, as well as urban land use data, clustering algorithms are used to summarize patterns of dynamic population density distribution. Pearson correlation analysis is then employed to study the correlation between dynamic population density distribution patterns and different land use elements. The results indicate that various urban land use factors such as locational centrality, functional diversity, transportation accessibility, compactness, and landscape quality have different impacts on the dynamic distribution of population density in spatial units, and the dynamic distribution patterns of population density in different land use types also vary. This research contributes to guiding the optimization of spatial quality and formulating planning and management measures that more effectively match construction intensity with population activity density.

Keywords