Using entropy of social media location data for the detection of crowd dynamics anomalies

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dc.contributor.author García Rubio, Carlos
dc.contributor.author Diaz Redondo, Rebeca P.
dc.contributor.author Campo Vázquez, María Celeste
dc.contributor.author Fernandez Vilas, Ana
dc.date.accessioned 2021-12-14T11:48:48Z
dc.date.available 2021-12-14T11:48:48Z
dc.date.issued 2018-12
dc.identifier.bibliographicCitation Garcia-Rubio, C., Díaz Redondo, R., Campo, C. & Fernández Vilas, A. (2018). Using Entropy of Social Media Location Data for the Detection of Crowd Dynamics Anomalies. Electronics, 7(12), 380.
dc.identifier.issn 2079-9292
dc.identifier.uri http://hdl.handle.net/10016/33760
dc.description This article belongs to the Special Issue Innovative Technologies and Services for Smart Cities.
dc.description.abstract Evidence of something unusual happening in urban areas can be collected from different data sources, such as police officers, cameras, or specialized physical infrastructures. In this paper, we propose using geotagged posts on location-based social networks (LBSNs) to detect crowd dynamics anomalies automatically as evidence of a potential unusual event. To this end, we use the Instagram API media/search endpoint to collect the location of the pictures posted by Instagram users in a given area periodically. The collected locations are summarized by their centroid. The novelty of our work relies on using the entropy of the sequence of centroid locations in order to detect abnormal patterns in the city. The proposal is tested on a data set collected from Instagram during seven months in New York City and validated with another data set from Manchester. The results have also been compared with an alternative approach, a training phase plus a ranking of outliers. The main conclusion is that the entropy algorithm succeeds inn finding abnormal events without the need for a training phase, being able to dynamically adapt to changes in crowd behavior.
dc.description.sponsorship This research was partially funded by the Ministry of Economy and Competitiveness through TEC2017-84197-C4-1-R, TEC2017-84197-C4-2-R, TEC2014-54335-C4-2-R, and TEC2014-54335-C4-3-R, and the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC).
dc.format.extent 15
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other City behavior
dc.subject.other Anomaly detection
dc.subject.other Location-based social networks
dc.subject.other Data mining algorithms
dc.title Using entropy of social media location data for the detection of crowd dynamics anomalies
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/electronics7120380
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2014-54335-C4-2-R
dc.relation.projectID Gobierno de España. TEC2017-84197-C4-1-R
dc.relation.projectID Gobierno de España. TEC2014-54335-C4-2-R
dc.relation.projectID Gobierno de España. TEC2014-54335-C4-3-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 380
dc.identifier.publicationissue 12
dc.identifier.publicationtitle Electronics
dc.identifier.publicationvolume 7
dc.identifier.uxxi AR/0000022968
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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