Clustering constrained on linear networks
Methodology
2022-07-22 v1 Statistics Theory
Statistics Theory
Abstract
An unsupervised classification method for point events occurring on a network of lines 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.
Cite
@article{arxiv.2207.10566,
title = {Clustering constrained on linear networks},
author = {Asael Fabian Martínez and Somnath Chaudhuri and Carlos Díaz-Avalos and Pablo Juan and Jorge Mateu and Ramsés H. Mena},
journal= {arXiv preprint arXiv:2207.10566},
year = {2022}
}