ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams

Abstract

Spatio-temporal clustering aims to discover interesting regions in spatio-temporal data. In this paper, we propose a novel, serial, density-contour based spatio-temporal clustering algorithm called ST-DCONTOUR which employs a model-based clustering methodology to obtain spatio-temporal clusters from location streams. Our approach subdivides the incoming data into batches and employs a serial approach that generates spatial clusters for each batch first; next, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Our approach employs contouring algorithms to identify spatial clusters as closed contours of a region where density is above a given threshold, and relies on contour analysis techniques to identify continuing, disappearing, and newly appearing spatial clusters in consecutive batches. We evaluate our approach by conducting a case study involving NYC taxi trips data. The experimental results show that ST-DCONTOUR can discover interesting spatio-temporal patterns in taxi pickup location streams.