Event detection: Exploiting socio-physical interactions in physical spaces
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...
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2015
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sg-smu-ink.sis_research-41032018-07-13T04:41:26Z Event detection: Exploiting socio-physical interactions in physical spaces JAYARAJAH, Kasthuri MISRA, Archan RUAN, Xiao-Wen Ee-peng LIM, 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. 2015-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3103 info:doi/10.1145/2808797.2809387 https://ink.library.smu.edu.sg/context/sis_research/article/4103/viewcontent/137._Event_Detection_Exploiting_Socio_Physical_Interactions_in_Physical_Spaces__ASONAM2015_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University College campus Movement Singapore Management University Events Computer Sciences Databases and Information Systems |
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College campus Movement Singapore Management University Events Computer Sciences Databases and Information Systems JAYARAJAH, Kasthuri MISRA, Archan RUAN, Xiao-Wen Ee-peng LIM, Event detection: Exploiting socio-physical interactions in physical spaces |
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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. |
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text |
author |
JAYARAJAH, Kasthuri MISRA, Archan RUAN, Xiao-Wen Ee-peng LIM, |
author_facet |
JAYARAJAH, Kasthuri MISRA, Archan RUAN, Xiao-Wen Ee-peng LIM, |
author_sort |
JAYARAJAH, Kasthuri |
title |
Event detection: Exploiting socio-physical interactions in physical spaces |
title_short |
Event detection: Exploiting socio-physical interactions in physical spaces |
title_full |
Event detection: Exploiting socio-physical interactions in physical spaces |
title_fullStr |
Event detection: Exploiting socio-physical interactions in physical spaces |
title_full_unstemmed |
Event detection: Exploiting socio-physical interactions in physical spaces |
title_sort |
event detection: exploiting socio-physical interactions in physical spaces |
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Institutional Knowledge at Singapore Management University |
publishDate |
2015 |
url |
https://ink.library.smu.edu.sg/sis_research/3103 https://ink.library.smu.edu.sg/context/sis_research/article/4103/viewcontent/137._Event_Detection_Exploiting_Socio_Physical_Interactions_in_Physical_Spaces__ASONAM2015_.pdf |
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