Event Detection

Abstract

This paper investigates how digital traces of people's movements and activities in the physical world (e.g., at college campuses and commutes) may be used to detect local, short-lived events in various urban spaces. Past work that use occupancy-related features can only identify high-intensity events (those that cause large-scale disruption in visit patterns). In this paper, we first show how longitudinal traces of the coordinated and group-based movement episodes obtained from individual-level movement data can be used to create a socio-physical network (with edges representing tie strengths among individuals based on their physical world movement & collocation behavior). We then investigate how two additional families of socio-physical features: (i) group-level interactions observed over shorter timescales and (ii) socio-physical network tie-strengths derived over longer timescales, can be used by state-of-the-art anomaly detection methods to detect a much wider set of both high & low intensity events. We utilize two distinct datasets--one capturing coarse-grained SMU campus-wide indoor location data from hundreds of students, and the other capturing commuting behavior by millions of users on Singapore's public transport network--to demonstrate the promise of our approaches: the addition of group and socio-physical tie-strength based features increases recall (the percentage of events detected) more than 2-folds (to 0.77 on the SMU campus and to 0.73 at sample MRT stations), compared to pure occupancy-based approaches.