Abstract
The “Sustainable Development Goals” indicate which changes nations and people of the world are committed to achieve, by virtue of a global consensus, obtained through a long, complex, and difficult path of dialogue and international and interdisciplinary collaboration. Ending poverty, in all its manifestations including its most extreme forms, through interconnected strategies, is the theme of Goal 1. Providing people all over the world with the support they need, as through promotion of social protection systems, is, in fact, the very essence of sustainable development. The objective of this work is the statistical analysis of the indicators useful for achieving the “No Poverty” Goal 1 through multidimensional statistical analysis methodologies (Totally fuzzy and relative) to understand which Italian regions need more government intervention.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to L. Mongelli (Sect. 1, 4), to A. Massari (Sect. 5), to P. Perchinunno (Sect. 3.1, 3.2) and to S. L'Abbate (Sect. 2and 3.3).
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Perchinunno, P., Massari, A., L’Abbate, S., Mongelli, L. (2022). Spatial Statistical Model for the Analysis of Poverty in Italy According to Sustainable Development Goals. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_45
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