A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/3894
EDITED VERSION: https://linkinghub.elsevier.com/retrieve/pii/S0167739X19309859
UNESCO SUBJECT: 1203.99 Otras ; 1209.03 Análisis de Datos
DOCUMENT TYPE: article
Undoubtedly, 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.
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