Clustering constrained on linear networks
Asael Fabian Martínez, Somnath Chaudhuri, Carlos Díaz‐Ávalos, Pablo Juan, Jorge Mateu, Ramsés H. Mena · Stochastic Environmental Research and Risk Assessment · 2023
An unsupervised classification method for point events occurring on a geometric network is proposed. The idea relies on the distributional flexibility and practicality of random partition models to discover the clustering structure featuring observations from a particular phenomenon taking place on a given set of edges. By incorporating the spatial effect in the random partition distribution, induced by a Dirichlet process, one is able to control the distance between edges and events, thus leading to an appealing clustering method. A Gibbs sampler algorithm is proposed and evaluated with a sensitivity analysis. The proposal is motivated and illustrated by the analysis of crime and violence patterns in Mexico City.