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.

Clustering spatial networks through latent mixture models

Egidi, Leonardo
;
Pauli, Francesco;Torelli, Nicola;Zaccarin, Susanna
2023-01-01

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.
2023
24-gen-2023
Pubblicato
https://doi.org/10.1093/jrsssa/qnac002
File in questo prodotto:
File Dimensione Formato  
Egidi Clustering spatial networks through latent.pdf

Accesso chiuso

Descrizione: articolo
Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 6.21 MB
Formato Adobe PDF
6.21 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Egidi+Clustering+spatial+networks+through+latent-Post_print.pdf

Open Access dal 25/01/2024

Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 6.64 MB
Formato Adobe PDF
6.64 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3039038
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact