Clustering constrained on linear networks

Asael Fabian Martínez

doi:10.1007/s00477-022-02376-y

Clustering constrained on linear networks

2023-01-09

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Asael Fabian Martínez *

DOI：10.1007/s00477-022-02376-y

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Abstract

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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.

Keywords:

Bayesian nonparametrics

Penalty function

Random partition model

Spatial clustering

Clustering constrained on linear networks

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