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Using Entropy of Social Media Location Data for the Detection of Crowd Dynamics Anomalies

1
Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, Leganés, E-28911 Madrid, Spain
2
Information & Computing Lab., AtlantTIC Research Center, School of Telecommunications Engineering, University of Vigo, E-36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Electronics 2018, 7(12), 380; https://doi.org/10.3390/electronics7120380
Received: 31 October 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 3 December 2018
(This article belongs to the Special Issue Innovative Technologies and Services for Smart Cities)
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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. View Full-Text
Keywords: city behavior; anomaly detection; location-based social networks; data mining algorithms city behavior; anomaly detection; location-based social networks; data mining algorithms
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Garcia-Rubio, C.; Díaz Redondo, R.P.; Campo, C.; Fernández Vilas, A. Using Entropy of Social Media Location Data for the Detection of Crowd Dynamics Anomalies. Electronics 2018, 7, 380.

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