Clustering spatial networks through latent mixture models

We consider a Bayesian model-based clustering technique that directly accounts for network relations between territorial units and their position in a geographical space. This proposal is motivated by a practical problem: to design administrative structures that are intermediate between the municipality and the province within an Italian region based on the existence of a relatively (to population) high commuting flow. In our social network model, the commuting flows are explained by the distances between the municipalities, i.e., the nodes, in a 3-dimensional space, where the 2 actual geographical coordinates and the third latent variable are modelled through a Gaussian mixture.

Automated summary

We propose a Bayesian model-based clustering technique that incorporates network relations and geographical positioning of territorial units. The aim is to design administrative structures in an Italian region based on commuting flows between municipalities. The social network model explains commuting flows using distances between municipalities in a 3-dimensional space modeled through a Gaussian mixture.