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

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. The uninterrupted growth in both number of users and activity of Online Social Networks (OSNs) can be attributed to the parallel increase of the smartphone penetration rate. These mobile devices allow a quick interaction with OSNs and make it easy for subscribers to share their ideas, thoughts, photos, messages, etc. All these posts automatically include the subscriber's location when using GPS-enabled devices, especially when interacting with Location-based Social Networks (LBSNs), i.e. location-centred OSNs. These networks focus their activity on sharing experiences at the right place and time they are happening (Foursquare, Twitter, Instagram, etc.). Consequently, LBSNs provide a very attractive source of geo-located data that, in the smart city field, may be an interesting alternative to the traditional video sources to monitor human activity in urban areas. On the one hand, the infrastructure costs are really low. Instead of having complex video-surveillance networks, which need to be deployed and maintained, citizens are the ones in charge of buying, maintaining and connecting their mobile devices. Besides, due to the ubiquity of the LBSNs, the area under analysis may be easily changed without any extra investment. On the other hand, traditional video-surveillance systems need to be monitored by human staff, who may be supported by complex algorithms for video frames analysis.