Analysis of mobility behaviors in geographic and semantic spaces
Repeatedly visited personal and public places were extracted from trajectories by finding spatial clusters of stop points. Temporal patterns of people's presence in the places resulted from spatio-temporal aggregation of the data by the places and hourly intervals within the weekly cycle. Based on these patterns, we identified the meanings or purposes of the places: home, work, breakfast or coffee, lunch and dinner, and dinner or shopping. Meanings of some places could be refined using the credit card transaction data. By representing the place meanings as points on a 2D plane, we built an abstract semantic space and transformed the original trajectories to trajectories in the semantic space. Spatio-temporal aggregation of the transformed trajectories into flows between the semantic places and subsequent clustering of time intervals by the similarity of the flow situations allowed us to reveal the routine movement behaviors. To detect anomalies, we (a) investigated the visits to the places with unknown meanings, and (b) looked for unusual presence times or visit durations at different semantic places. The analysis is scalable since all tools and methods can be applied to much larger data.