Learning to cluster urban areas: two competitive approaches and an empirical validation
Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
2 Millennium Institute for Foundational Research on Data (IMFD), Santiago, Chile
3 National Center of Artificial Intelligence, Santiago, Chile
4 Faculty of Government, Universidad Adolfo Ibáñez, Santiago, Chile
5 Department of Transport Engineering and Logistics, Pontificia Universidad Católica de Chile, Santiago, Chile
6 Department of Informatics, Universidad Técnica Federico Santa María, Santiago, Chile
7 Public Administration and Political Science, Universidad de Concepción, Concepción, Chile
8 School of Government, Universidad Mayor, Santiago, Chile
Accepted: 5 December 2022
Published online: 20 December 2022
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.
Key words: Urban clustering / Graph Neural Networks / Gaussian Mixture Models
© The Author(s) 2022
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