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Clustering of Small Territories Based on Axes of Inequality

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  • Xavier Perafita

    (Observatori—Organisme Autònom de Salut Pública de la Diputació de Girona (Dipsalut), 17003 Girona, Spain
    Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17003 Girona, Spain)

  • Marc Saez

    (Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17003 Girona, Spain
    CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain)

Abstract

Background: In the present paper, we conduct a study before creating an e-cohort for the design of the sample. This e-cohort had to enable the effective representation of the province of Girona to facilitate its study according to the axes of inequality. Methods: The territory under study is divided by municipalities, considering these different axes. The study consists of a comparison of 14 clustering algorithms, together with 3 data sets of municipal information to detect the grouping that was the most consistent. Prior to carrying out the clustering, a variable selection process was performed to discard those that were not useful. The comparison was carried out following two axes: results and graphical representation. Results: The intra-cluster results were also analyzed to observe the coherence of the grouping. Finally, we study the probability of belonging to a cluster, such as the one containing the county capital. Conclusions: This clustering can be the basis for working with a sample that is significant and representative of the territory.

Suggested Citation

  • Xavier Perafita & Marc Saez, 2022. "Clustering of Small Territories Based on Axes of Inequality," IJERPH, MDPI, vol. 19(6), pages 1-25, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3359-:d:769947
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