Enhancing Detailed Planning from Functional Mix Perspective with Spatial Analysis and Multiscale Geographically Weighted Regression: A Case Study in Shanghai Central Region

Detailed spatial planning serves as statutory guidance for regulating specific spatial functions, including public services, living conditions, and production spaces. It emphasizes meeting the comprehensive needs of the local population, making it crucial to understand the relationship between population distribution and the mix of various city functions, particularly in the era of urban regeneration. Therefore, this study utilized point-of-interest (POI) data representing land functions and population data to investigate these relationships via spatial analysis and Multiscale Geographically Weighted Regression (MGWR). Applied to the central urban area of Shanghai, the study reveals that the level of mixed land use and various functionalities affect population distribution at different adaptive scales. We also found a higher degree of functional mix does not always meet population needs. Although generally there is a positive correlation between functional mix and population distribution, they are not always closely bonded. The proposed method provides an efficient workflow for identifying the applicable scale of various functions to increase functional mix and attract the population, which can provide real-time evidence supporting detailed planning. Test results also reveal the less-considered space along the boundaries of administrative districts. We also found developing tools for detailed planning is an urgent need to facilitate cross-boundary cooperation and development, especially in the context of urban regeneration where they always are overlooked at the detailed planning level. By using open-sourced POI and population data, our proposed workflow can be easily applied to other cities or regions, enhancing their practical value for similar research contexts.