Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., one minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This paper introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the DBSCAN clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A Sparse Mobility Markov Chain Model (SMMC) considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%.
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