Research on the suitability of urban cultural facilities supply and demand based on multi-source data

The development of big data has greatly improved the convenience of residents' lives. Among them, spatiotemporal big data has effectively improved the timeliness, practicality, and accuracy of geographic spatial information, and has been widely used in the field of public service facilities. Research has employed web crawling technology to obtain various city cultural facilities and residential community POI data, and has used self-compiled PYTHON code to obtain the travel time required for each community to travel to various cultural facility points by car. By combining data from the statistical yearbook, this study takes Xuhui District in Shanghai as an example and explores the adaptability of the supply and demand of urban cultural facilities from two dimensions: spatial adaptability and supply-demand relevance. Using methods such as nearest neighbor analysis, multi-distance spatial clustering, coordination location quotient, and correlation analysis, it was found that the spatial form of the distribution of cultural facilities and residents is similar, but there is a structural mismatch between supply and demand. The research results are helpful for optimizing facility configuration and promoting fine-grained urban management