The spatial heterogeneity of land use patterns and residents’ corresponding economic activities give rise to urban mobility’s latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels.
Urban Mobility Mining and Its Facility POI Proportion Analysis based on Mobile Phone Data