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Undoubtedly, Location-based Social Networks (LBSNs) provide an interestingsource of geo-located data that we have previously used to obtain patterns ofthe dynamics of crowds throughout urban areas. According to our previousresults, activity in LBSNs reflects the real activity in the city. Therefore,unexpected behaviors in the social media activity are a trustful evidence ofunexpected changes of the activity in the city. In this paper we introduce ahybrid solution to early detect these changes based on applying a combinationof two approaches, the use of entropy analysis and clustering techniques, onthe data gathered from LBSNs. In particular, we have performed our experimentsover a data set collected from Instagram for seven months in New York City,obtaining promising results.

A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics