Using Clustering Methods to Enhance Data Representativeness: Toward a Well-Being Indicator for Corsican Municipalities

Ghinevra Comiti, Paul-Antoine Bisgambiglia (), Nathalie Lameta (), Morgane Millet (), Graziella Luisi and Paul-Antoine Bisgambiglia
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Paul-Antoine Bisgambiglia: SPE - Laboratoire « Sciences pour l’Environnement » (UMR CNRS 6134 SPE) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli], Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]
Nathalie Lameta: Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]
Paul-Antoine Bisgambiglia: SPE - Laboratoire « Sciences pour l’Environnement » (UMR CNRS 6134 SPE) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli], Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]

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Abstract: Are indicators based primarily on economic data sufficient to represent a region's quality of life? This study aims to develop a well-being indicator for Corsican municipalities to assist decision-makers. Traditional indices like Gross Domestic Product (GDP) and the Human Development Index (HDI) have limitations, often overlooking regional factors, as quality of life is deeply influenced by local territory, social context, and cultural background. Thus, developing indicators at the municipal level is crucial to better reflect local conditions and support decision-making in smaller communities. In this study, we use machine learning to enhance data collection and apply clustering methods to group municipalities with similar characteristics, thereby optimizing sampling efforts. We compare three popular clustering algorithms: Affinity Propagation, K-means, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Our approach reduces the 360 Corsican municipalities to four distinct groups, each sharing key quality-of-life attributes. We discuss our data collection process, the performance of the clustering algorithms, and potential future research directions.

Keywords: Multivariate Analysis; Quality of Life; Indicators Survey; Functional clustering; Community Well-Being (search for similar items in EconPapers)
Date: 2025-08-09
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Published in Computational Science and Computational Intelligence, 2509, Springer Nature Switzerland; Springer Nature Switzerland, pp.111-121, 2025, Communications in Computer and Information Science, ⟨10.1007/978-3-031-94953-1_10⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05390439

DOI: 10.1007/978-3-031-94953-1_10

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