Dynamic community detection considering daily rhythms of human mobility

Detecting human mobility community structures is an important means to reveal urban spatial structures. Due to the circadian clock, human mobility has a daily rhythm, representing dynamic interactions among urban functional areas at different times of the day. However, few community detection methods have been developed by explicitly considering such daily rhythms of human mobility. To fill the gap, this study proposes a novel method for detecting dynamic community structures derived from the daily rhythms of human mobility. The proposed method employs a consensus clustering technique to detect consensus structures of dynamic communities throughout the day. The time series characteristics of the detected dynamic communities are further introduced. The hierarchical clustering technique is used to uncover evolving patterns of detected dynamic communities. A case study was carried out using taxi trajectories in New York City to verify the proposed method. Results indicated that the proposed method could well detect a robust structure of dynamic communities even when human mobility patterns changed dramatically at certain time periods. Results also suggested that the proposed method could identify underlying evolving patterns of detected dynamic communities.