  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.
