Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

• Auteur principal: Cusatelli, Carlo (auth)
• Autres auteurs: Giacalone, Massimiliano (auth), Nissi, Eugenia (auth)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Florence Firenze University Press 2021
• Collection: Proceedings e report 132
• Sujets: Social research & statistics; Well being; Spatial Principal Component Analysis (sPCA); Composite Indicators
• Accès en ligne: OAPEN Library: download the publication; OAPEN Library: description of the publication

Résumé

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

Description matérielle

1 electronic resource (6 p.)

ISBN

978-88-5518-461-8.27
9788855184618

Accès

Open Access