ST-COPOT: Spatio-temporal Clustering with Contour Polygon Trees

Abstract

Nowadays, growing effort has been put to develop spatio-temporal clustering approaches that are capable of discovering interesting patterns in large spatio-temporal data streams. In this paper, we propose a 3-phase serial, density-contour based clustering algorithm called ST-COPOT, which can identify spatio-temporal cluster at multiple levels of density granularity. ST-COPOT takes the point cloud data as input and divides it into batches, next, it employs a non-parametric kernel density estimation approach and contouring algorithms to obtain spatial clusters; at last, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Moreover, a novel data structure called contour polygon tree is introduced as a compact representation of the spatial clusters obtained for each batch for different density thresholds, and a family of novel distance functions that operate on contour polygon trees are proposed to identify continuing clusters. The experimental results on NYC taxi trips data show that ST-COPOT can effectively discover interesting spatio-temporal patterns in taxi pickup location streams.