G-RoI: Automatic Region-of-Interest detection driven by geotagged social media data
To refer or cite this work, please use this reference: L. Belcastro, F. Marozzo, D. Talia, P. Trunfio, "G-RoI: Automatic Region-of-Interest detection driven by geotagged social media data". ACM Transactions on Knowledge Discovery from Data, October 2017.
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-of-Interest (RoI), to represent the boundaries of the PoI's area. \emph{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 \emph{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.