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Clustering and classification of spatio-temporal data using spatial dynamic panel data models

Author

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  • Giuseppe Feo

    (University of Salerno)

  • Francesco Giordano

    (University of Salerno)

  • Sara Milito

    (University of Salerno)

  • Marcella Niglio

    (University of Salerno)

  • Maria Lucia Parrella

    (University of Salerno)

Abstract

The class of Spatial Dynamic Panel Data models has been proposed in the socio-econometric literature to analyze spatio-temporal data. In this paper we consider a particular variant of such models, where the set of spatial units is assumed to be partitioned into clusters and the parameters of the model are assumed to be homogeneous within clusters and heterogeneous across clusters. For this model, assuming that the true partition is unknown, we propose a new clustering procedure and a validation test, based on a multiple testing approach, that help to choose the best configuration of model, for a given observed dataset, by estimating the optimal number of clusters and the best partition of units. The validity of the proposed procedures has been shown both theoretically and empirically, on simulated and real data, also compared to alternative methods.

Suggested Citation

  • Giuseppe Feo & Francesco Giordano & Sara Milito & Marcella Niglio & Maria Lucia Parrella, 2025. "Clustering and classification of spatio-temporal data using spatial dynamic panel data models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(2), pages 387-435, June.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:2:d:10.1007_s11634-024-00620-7
    DOI: 10.1007/s11634-024-00620-7
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