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arXiv · 2207.10566

Clustering constrained on linear networks

Asael Fabian Martínez·Somnath Chaudhuri·Carlos Díaz-Avalos·Pablo Juan·Jorge Mateu·Ramsés H. Mena
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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.

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Asael Fabian Martínez, Somnath Chaudhuri, Carlos Díaz-Avalos, Pablo Juan, Jorge Mateu, Ramsés H. Mena. 2022-07-19. Clustering constrained on linear networks. https://arxiv.org/abs/2207.10566

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