Spatio-Temporal Trajectory Region-of-Interest Mining Using Delaunay Triangulation
Luke Bermingham, Kyungmi Lee, Ickjai Lee · 2014
Due to the ubiquity of GPS enabled devices and the advances in sensing technologies, trajectory data has become abundant. Regions of interest are important since they describe specific hot-spots within the data that often correlate with domain specific phenomena. Traditional region of interest mining utilises grid based rasters to model space. This suffers from two main problems: hard to determine the best grid size and unable to model consistent spatial adjacency. This paper utilises a 3D argument free space tessellation, Delaunay triangulation, to partition spatio-temporal trajectory data and extract arbitrary shaped regions of interest. Experimental results demonstrate the robustness and improved effectiveness of our approach at identifying granular spatio-temporal patterns.