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dc.contributor.authorDíaz Redondo, Rebeca Pilar 
dc.contributor.authorGarcía Rubio, Carlos
dc.contributor.authorFernández Vilas, Ana 
dc.contributor.authorCampo Vázquez, María Celeste
dc.contributor.authorRodríguez Carrión, Alicia
dc.date.accessioned2022-09-28T10:00:01Z
dc.date.available2022-09-28T10:00:01Z
dc.date.issued2020-08
dc.identifier.citationFuture Generation Computer Systems, 109, 83-94 (2020)spa
dc.identifier.issn0167739X
dc.identifier.urihttp://hdl.handle.net/11093/3894
dc.description.abstractUndoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.spa
dc.description.sponsorshipMinisterio de Economía y Competitividad | Ref. TEC2014-54335-C4-2-Rspa
dc.description.sponsorshipMinisterio de Economía y Competitividad | Ref. TEC2014-54335-C4-3-Rspa
dc.description.sponsorshipAgencia Estatal de Investigación | Ref. TEC2017-84197-C4-2-Rspa
dc.description.sponsorshipAgencia Estatal de Investigación | Ref. TEC2017-84197-C4-3-Rspa
dc.language.isoengspa
dc.publisherFuture Generation Computer Systemsspa
dc.relationinfo:eu-repo/grantAgreement/MINECO//TEC2014-54335-C4-2-R/ES/MONITORIZACION DE INCIDENTES EN COMUNIDADES INTELIGENTES (INRISCO): SEGURIDAD Y MOVILIDAD
dc.relationinfo:eu-repo/grantAgreement/MINECO//TEC2014-54335-C4-3-R/ES/INRISCO: ANALISIS DE COMUNIDADES BASADO EN MINERIA SOCIAL
dc.relationinfo:eu-repo/grantAgreement/AEI//TEC2017-84197-C4-2-R/ES/MAGOS: DETECCION DE IRREGULARIDADES EN FUENTES DE DATOS Y PROCESOS DISTRIBUIDOS
dc.relationinfo:eu-repo/grantAgreement/AEI//TEC2017-84197-C4-3-R/ES/INTELIGENCIA DE FUENTES ABIERTAS PARA REDES ELECTRICAS INTELIGENTES SEGURAS. PRIVACIDAD DE DATOS Y COMUNICACIONES FIABLES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA hybrid analysis of LBSN data to early detect anomalies in crowd dynamicsen
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.1016/j.future.2020.03.038
dc.identifier.editorhttps://linkinghub.elsevier.com/retrieve/pii/S0167739X19309859spa
dc.publisher.departamentoEnxeñaría telemáticaspa
dc.publisher.grupoinvestigacionInformation and Computing Laboratoryspa
dc.subject.unesco1203.99 Otrasspa
dc.subject.unesco1209.03 Análisis de Datosspa
dc.date.updated2022-09-28T08:39:13Z
dc.computerCitationpub_title=Future Generation Computer Systems|volume=109|journal_number=|start_pag=83|end_pag=94spa


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