Tracking Group Movement in Location Based Social Networks
Sameera Kannangara, Hairuo Xie, Egemen Tanin, Aaron Harwood, Shanika A. Karunasekera · 2020
We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group movement model. Our experiments with real LBSN data and synthetic LBSN data show that GKF can detect groups and predict group movement with a high level of accuracy and efficiency.