Understanding Trajectory Data Based on Heterogeneous Information Network Using Visual Analytics

Abstract

With its continuous development, location information acquisition technology is able to collect more and more trajectory data, and the rich information contained therein is gradually attracting attention from researchers. Trajectory data involves complex relationships among moving objects, time, space, which are hard to understand and be used directly. Nowadays, visual analysis of trajectory data is mainly focus on its representation and interaction, but fails to address the complex correlation contained in trajectory data. Hence, we propose TrajHIN, a heterogeneous information network model built on trajectory data, measure the meta path-based similarity and centrality, and use a visual analytics method to deeply understand trajectory data. The example of visual analysis of real trajectory data has been interpreted and given feedback from domain experts, which proves effectiveness of TrajHIN and feasibility of mining implicit semantic information from trajectory data.