Clustering spatial networks through latent mixture models. (24th January 2023)

• Record Type: Journal Article
• Authors: Egidi, Leonardo; Pauli, Francesco; Torelli, Nicola; Zaccarin, Susanna
• Is Part Of: Journal of the Royal Statistical Society. Volume 186:Number 1(2023)
• Page Start: 137
• Page End: 156
• Publication Date: 2023-01-24
• Subjects: Bayesian model-based clustering -- commuting flows -- geographical partitioning -- Gaussian mixture; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1093/jrsssa/qnac002
• Languages: English
• ISSNs: 0964-1998

Abstract

Abstract: 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.