Identifying sinks and sources of human flows: A new approach to characterizing urban structures

Human flow data are rich behavioral relevant to people's decision-making regarding where live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy understand massive relational data, datasets have often been reduced merely the statistics of trip counts at destinations, discarding from origin destination. In this study, we propose an alternative center identification method based on human mobility data. This extracts scalar potential field trips combinatorial Hodge theory. It detects only statistically significant attractive locations sinks but also origins sources trips. As a case identify commuting shopping in Tokyo metropolitan area. aim-specific analysis leads classification centers distinct aspects mobility. The proposed can be applied other with properties helps us examine complex spatial structures contemporary areas multiple perspectives