G-Rol: Automatic Region-of-Interest Detection Driven by Geotagged Social Media Data

摘要

Geotagged data gathered from social media can be used to discover interesting locations visited by users called (R) Places-of-Interest (PoIs). Since a Pol 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-of-Interest (RoI) to represent the boundaries of the PoI's area. RoI mining techniques are aimed at discovering ROIs from Pols and other data. Existing RoI mining techniques are based on three main approaches: predefined shapes, density-based clustering, and grid-based aggregation. This article 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 Pols 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 F-1 score is 0.34 higher than that obtained with the well-known DBSCAN algorithm in Rome RoIs and 0.23 higher in Paris RoIs.