Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

Abstract

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

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