Analysis of Regional Imbalances in Italy Based on Cluster Analysis

In 2021 ISTAT presented the Report on Equitable and Sustainable Well-being (BES 2020), consisting of a system of indicators that follow the significant changes that have characterized the Italian society in the last 10 years. With the integration of new indicators, realized in coherence with the fundamental lines of the Next Generation EU, there has been an enrichment of information on the country system concerning health aspects, education and training, and economic well-being. The 20 Italian regions, the 2 autonomous provinces of Bolzano and Trento, the 3 territorial divisions North, Center, South and the total of Italy constituting a set of 26 territorial units, have been described each with a set of 36 numerical indicators, concerning the areas of Health, Education and Training, Economic Wellbeing. These areas are the most suitable for highlighting regional imbalances in Italy. In this paper has been analyzed the input data matrix, made up of 26 rows, one for each of the territorial units, and of 36 columns, the number of descriptors used for each territorial unit, by means of a factor analysis, using the principal components method, in order to construct a regional taxonomy characterized by those of the 36 indicators that are most correlated with each of the factors that have emerged. Moreover, starting from the coordinates calculated for each of the 26 territorial units in the factor space, a cluster analysis of the 26 territorial units was carried out, using the connected graph method, in order to highlight the territorial similarities and differences in Italy. © 2021, Springer Nature Switzerland AG.

De Maria M. , Mazzei M.2 , Bik O.V. 1 , Palma A.L.2
Springer Science and Business Media Deutschland GmbH
12954 LNCS
  • 1 Peoples Friendship University of Russia, (RUDN University), 6 Miklukho-Maklaya Street, Moscow, 117198, Russian Federation
  • 2 National Research Council, Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti” - LabGeoInf, Via dei Taurini, 19, Rome, 00185, Italy
Cluster analysis; Factor analysis; Spatial data analysis; Spatial statistical model; Urban models
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