Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2015
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.
Keywords
College campus, Movement, Singapore Management University, Events
Discipline
Computer Sciences | Databases and Information Systems
Publication
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015: August 25-28, Paris
First Page
508
Last Page
513
ISBN
9781450338547
Identifier
10.1145/2808797.2809387
Publisher
ACM
City or Country
Paris
Citation
JAYARAJAH, Kasthuri; MISRA, Archan; RUAN, Xiao-Wen; and Ee-peng LIM.
Event detection: Exploiting socio-physical interactions in physical spaces. (2015). ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015: August 25-28, Paris. 508-513.
Available at: https://ink.library.smu.edu.sg/sis_research/3103
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/2808797.2809387