Well-being analysis of Italian provinces with spatial principal components. (December 2022) - Giacalone, Massimiliano; Mattera, Raffaele; Nissi, Eugenia
Abstract: Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into account the spatial dimension of the phenomenon makes it unsuitable for studying the well-being of urban areas. In this paper, we propose to use Spatial Principal Component Analysis (SPCA) for measuring well-being. The SPCA technique provides principal component scores that summarize both the spatial variability and the spatial autocorrelation structure among the statistical units. In this paper, the SPCA is used to construct well-being CIs for the Italian provinces. Highlights: We propose a Composite Indicator based on Spatial Principal Component Analysis considering the presence of spatial autocorrelation. The application concerns 103 Italian Province capital cities and employs Urban BES considering 64 indicators belonging to 8 dimensions. The addressed approach is useful for policymakers in the promotion of well-being for public policy. Results contribute to a better understanding of spatial variations and disparities across Italian provinces.