Automatic Region-of-Interest detection driven by geotagged social media data

Geotagged data gathered from social media can be used to discover interesting locations visited by users called Places-of-Interest (PoIs). Since a PoI is generally identified by the geographical coordinates of a single point, it is hard to match it with user trajectories. Therefore, it is useful to define an area, called Region-ofInterest (RoI), to represent the boundaries of the PoI’s area. RoI mining techniques are aimed at discovering Regions-of-Interest from PoIs and other data. Existing RoI mining techniques are based on three main approaches: predefined shapes, density-based clustering and grid-based aggregation. This paper proposes G-RoI, a novel RoI mining technique that exploits the indications contained in geotagged social media items to discover RoIs with a high accuracy. Experiments performed over a set of PoIs in Rome and Paris using social media geotagged data, demonstrate that G-RoI in most cases achieves better results than existing techniques. In particular, the mean F1 score is 0.34 higher than that obtained with the well-known DBSCAN algorithm in Rome RoIs and 0.23 higher in Paris RoIs.