Analysis of the Influence of POI-based Land Use Structure on Clustering Degree of Community Population

Authors: Huan Zhang*, Tianjin university, Tianjie Zhang, Tianjin university, Lei Wang, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences
Topics: Quantitative Methods, Land Use
Keywords: Neighbourhood vibrancy, Beijing, Clustering degree,Mixed land use
Session Type: Virtual Paper
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Land use structure are related to neighbourhood vibrancy. In the era of big data, urban researchers can obtain more detailed data to conduct quantitative research on the relationship between mixed land use and neighbourhood vibrancy. However, neighbourhood vibrancy is often defined as the number of people in big data, and few types of research paid attention to the clustering degree of the community population. Neighbourhood vibrancy is measured by the clustering degree of community population in this study to research the correlation between community population aggregation and land use structure. The results of the study may have implications for urban planners in terms of increasing urban vitality. Taking Beijing as the overall research scope, this study takes the relationship between land use structure and clustering degree of community population as the research objective. Based on a total of 465,381 POI data of Beijing, this study applies the concept of hill’s diversity number in ecology to measure land use structure. Based on the data obtained from the heat map application, the study applies hierarchical clustering to define the types of clustering degrees of community populations (all-day, daytime or nighttime clustering type). Finally, a multinomial logistic regression of land use structure and clustering degree of community population is constructed.
Preliminary conclusions include:
1.The functional classifications of land can partly explain the change of clustering degree of community population with time.
2.Increasing the mixing degree of land use can increase the probability of both the all-day and day-day clustering type.

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