Clustering constrained on linear networks

• DOI: 10.1007/s00477-022-02376-y
• Publication Date: 2023-01-09T16:11:23Z
• Authors: Asael Fabian Mart...; Somnath Chaudhuri; Carlos Díaz-Avalos; Pablo Juan; Jorge Mateu; Ramsés H. Mena
• Keywords: Methodology (stat.ME); FOS: Computer and information sciences; 0502 economics and business; 05 social sciences; FOS: Mathematics; Mathematics - Statistics Theory; Statistics Theory (math.ST); Statistics - Methodology

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.