SCPP

Abstract

A collection of individuals is represented by point patterns. Each individual is a finite set of geographical locations representing their visiting pattern to places in a region. We present SCPP, an algorithm for clustering these individuals considering the spatial patterns of their visiting locations. We adopted a probabilistic framework based on the theory of point processes that allows us to derive a non-obvious distance metric between each individual point pattern and the underlying, unobserved continuous intensity function. This metric is the Kullback-Leibler divergence between the true data-generating point process distribution and the model-generating distribution. We also introduce a theoretically based framework for the cost function to be minimized, a functional T (P) taking as arguments the probability distributions underlying the unknown clusters. We present an extensive experimental analysis to show SCPP’s effectiveness using several synthetic datasets and spatial mobility patterns from geo-tagged social media.