NDLI: Event Detection: Exploiting Socio-Physical Interactions in Physical Spaces

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Event Detection: Exploiting Socio-Physical Interactions in Physical Spaces

Content Provider	ACM Digital Library
Author	Ruan, Xiao-Wen Misra, Archan Jayarajah, Kasthuri Lim, Ee-Peng
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.
Starting Page	508
Ending Page	513
Page Count	6
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2809387
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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