RT Journal Article
T1 Using entropy of social media location data for the detection of crowd dynamics anomalies
A1 García Rubio, Carlos
A1 Diaz Redondo, Rebeca P.
A1 Campo Vázquez, María Celeste
A1 Fernandez Vilas, Ana
AB 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.
PB MDPI
SN 2079-9292
YR 2018
FD 2018-12
LK https://hdl.handle.net/10016/33760
UL https://hdl.handle.net/10016/33760
LA eng
NO This article belongs to the Special Issue Innovative Technologies and Services for Smart Cities.
NO 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).
DS e-Archivo
RD 1 sept. 2024