Analyzing of Spatial Interactive Network Based on Urban Community Division

The urban community interaction patterns are an important portrait of the urban spatial structure, and they can serve with urban community construction, traffic management, and resource allocation. In the big data era, various movement trajectories are available for studying spatial structures. In this study, on the basis of the massive taxi-trip data of Wuhan, we built a spatially embedded network and identified the intra-city spatial interactions. The community detection method was applied to reveal urban structures of different times in Wuhan. At the same time, we studied the degree of association between different regions based on the frequency of interactions of trajectory data. Finally, we found that: (a) Compared to weekends and working days, people have a wider range of travel and more random travel locations on holidays. (b) From community detection, Hanyang District and Hankou Districts were classified as the same “community,” and the result of Wuchang District division was similar to the administrative boundaries. (c) In Wuhan, the most closely related areas were Hankou and Wuchang District, and the closeness between Wuchang District and Qingshan District was the second. From the research results, it can be concluded that the closeness of community interaction is positively related to the level of regional economic development, which illustrates the importance of community development and provides decision-making basis for urban traffic management.