Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Abstract

Urban agglomeration is a higher stage of urban development. Exploring the spatial correlation of functional areas is important for promoting high-quality urban development. However, recently the research on urban functional areas is mainly focused on how to identify urban functional areas, and they lack some methods to analyze the spatial correlation patterns of urban functional areas. Therefore, firstly, this study uses POI data and a deep learning model to identify the urban functional areas of four typical urban agglomerations in China. Then, we create a new method to mine the spatial correlation patterns of urban functional areas from two levels (city and cities in one urban agglomeration). Moreover, we find that various graphs well express the spatial correlation patterns. Based on the above, we establish a new technical process for mining the spatial correlation of urban functional areas. The main conclusions are as follows: (1) The multilayer detailed division of the functional area is helpful to mine the spatial correlation pattern of the functional area. (2) The rank of each city in the urban agglomeration can be divided according to the urban functional area; there are great differences in richness and scale of the mixed-functional areas in the urban agglomeration, but there is little difference among the urban agglomerations. (3) The spatial correlation patterns of the functional areas in the first-rank cities of each urban agglomeration area are highly similar. (4) There is a certain spatial correlation pattern of functional areas in Chinese urban agglomerations. (5) There are great differences in the similarity of spatial correlation patterns between cities in one urban agglomeration, and the spatial relationship of similarity may not surround the most developed cities. This research will help urban planners to develop functional areas in different cities.