Abstract
We suggest a new criterion based on fuzzy sets theory in order to evaluate well-being in European regions at NUTS 2 level. With reference to the various domains of this vague and multidimensional concept, a subset of 16 variables available in Eurostat database is selected. After a fuzzy transformation, the variables are aggregated into a fuzzy synthetic indicator, considering different weighting criteria. For each region the fuzzy indicator value, in the range [0, 1], may be interpreted as a membership degree to the subset of the areas with the highest well-being. The results are compared with the ones obtained by principal component analysis (PCA) and k-means cluster analysis applied to the same dataset. Furthermore, the relationships of the fuzzy indicator with GDP per capita and with human development index (HDI) are highlighted. The advantages and the drawbacks of the suggested approach are discussed.
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Milioli, M.A., Berzieri, L., Zani, S. (2015). Comparing Fuzzy and Multidimensional Methods to Evaluate Well-Being in European Regions. In: Morlini, I., Minerva, T., Vichi, M. (eds) Advances in Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-17377-1_18
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DOI: https://doi.org/10.1007/978-3-319-17377-1_18
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