Examines urban spatial restructuring in Chongqing, highlighting interactions between population and urban functions amid topographic constraints.
Understanding how urban spatial structure evolves under environmental constraints is essential for sustainable urban development and planning practice. However, existing studies often analyze population distribution and urban functions separately, providing limited explanation of the mechanisms underlying urban spatial restructuring. This limitation is particularly evident in mountain cities, where topographic constraints, fragmented developable land, and corridor-based accessibility shape distinctive spatial patterns. Using Chongqing as a case study, this study examines the evolution of its central urban area between 2020 and 2024 from an integrated population–function perspective. Multi-temporal Baidu residential and employment population data and POI data are used to characterize population activities and urban functions. A functional reinterpretation framework is developed by aggregating conventional POI categories into three dimensions: prosperity, dynamism, and synergy. Kernel density estimation, density difference analysis, Getis–Ord Gi* hotspot analysis, and population–function relationship analysis are applied to examine spatial evolution and interaction patterns under topographic constraints. The results show that Chongqing experienced substantial spatial restructuring characterized by increasing polycentricity and internal reorganization rather than simple outward expansion. Residential and employment populations expanded along river valleys and development corridors, while employment activities remained more concentrated in established centers. Functional evolution shows clear differentiation: prosperity remains concentrated in traditional centers, dynamism expands toward emerging development platforms, and synergy diffuses through residential and transportation networks. Residential populations are more strongly associated with synergy functions, whereas employment populations are more closely linked to dynamism functions.
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Yang et al. (2026) studied this question.
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