Learning to cluster urban areas: two competitive approaches and an empirical validation

Camila Vera, Francesca Lucchini, Naim Bro, Marcelo Mendoza, Hans Löbel, Felipe Gutiérrez, Jan Dimter, Gabriel Cuchacovic, Axel Reyes, Hernán Valdivieso, Nicolás Alvarado, Sergio Toro

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Urban clustering detects geographical units that are internally homogeneous and distinct from their surroundings. It has applications in urban planning, but few studies compare the effectiveness of different methods. We study two techniques that represent two families of urban clustering algorithms: Gaussian Mixture Models (GMMs), which operate on spatially distributed data, and Deep Modularity Networks (DMONs), which work on attributed graphs of proximal nodes. To explore the strengths and limitations of these techniques, we studied their parametric sensitivity under different conditions, considering the spatial resolution, granularity of representation, and the number of descriptive attributes, among other relevant factors. To validate the methods, we asked residents of Santiago, Chile, to respond to a survey comparing city clustering solutions produced using the different methods. Our study shows that DMON is slightly preferred over GMM and that social features seem to be the most important ones to cluster urban areas.

Original languageEnglish
Article number62
JournalEPJ Data Science
Issue number1
StatePublished - Dec 2022
Externally publishedYes


  • Gaussian Mixture Models
  • Graph Neural Networks
  • Urban clustering


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