Abstract
The paper analyses the concept of poverty through a multidimensional approach that uses multiple indicators to define a condition of poverty and allows to denote territorial areas and/or population subgroups characterized by situations of hardship or severe social exclusion. This study responds to need of defining and constructing indicators that are capable of estimating poverty in small areas. The complexity of the poverty phenomenon thus poses the need to identify analytical techniques that allow poverty to be framed in a broader context, to improve knowledge of the problem and deal with it through specific economic and social interventions. The data analysed in this paper allowed the construction of three sets of indicators referring to three areas of poverty: economic, social, and housing. The data refer to one Italian region: Apulia. Two methodologies were adopted to study the data. The first based on the Fuzzy approach that uses the technique of Fuzzy Sets to synthesize and measure the incidence of relative poverty in the considered population starting from the statistical information provided by a plurality of indicators. The second, based on a cluster analysis algorithm: the DBSCAN method for identifying dense areas from the fuzzy values processed by the first methodology.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to A. Massari (paragraphs 1 and 4), to P. Perchinunno (paragraph 3.2) and to S. L’Abbate (paragraphs 3.1, 3.3), to M. Carbonara (paragraph 2).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Analisi della povertà relativa Siqual, Istat 1997. http://siqual.istat.it/SIQual/visualizza.do?id=8888916&refresh=true&language=IT
La misura della povertà assoluta. Istat 2009. https://ebiblio.istat.it/digibib/Metodi%20e%20norme/MOD1546628Ed2009N39.pdf
Zadeh, L.A: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Poli, G., Muccio, E., Cerreta, M.: Circular, cultural and creative city index: a comparison of indicators-based methods with a machine-learning approach. Aestimum 81, 53–70 (2022). https://doi.org/10.36253/aestim-13880
Cerreta, M., Panaro, S., Poli, G.: A knowledge-based approach for the implementation of a SDSS in the Partenio Regional Park (Italy). In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9789, pp. 111–124. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42089-9_8
Dubois, D., Prade, H.: Fuzzy Sets and Systems. Academic Press, Boston, New York London (1980)
Cerioli, A., Zani, S.: A fuzzy approach to the measurement of poverty. In: Dagum, C., Zenga, M. (eds.) Income and Wealth Distribution, inequality and Poverty. Springer, Berlin (1990). https://doi.org/10.1007/978-3-642-84250-4_18
Cheli, B., Lemmi, A.A.: Totally fuzzy and relative approach to the multidimensional analysis of poverty. Econ. Notes 24(1), 115–134 (1995)
Montrone, S., Perchinunno, P., Rotondo, F., Torre, C.M., Di Giuro, A.: Identification of hot spots of social and housing difficulty in urban areas: scan statistic for housing market and urban planning policies. In: Murgante, B., Borruso, G., Lapucci, A. (eds.) Geocomputation and Urban Planning, Studies in Computational Intelligence, Vol. 176, pp. 57–78. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-89930-3_4
Perchinunno, P., Rotondo, F., Torre, C.M.: A multivariate fuzzy analysis for the regeneration of urban poverty areas. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008. LNCS, vol. 5072, pp. 137–152. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69839-5_11
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland (1996)
Massari, A., L’Abbate, S., Mongelli, L., Perchinunno, P.: Spatial statistical model for the analysis of poverty in Italy according to sustainable development goals. In: Lecture Notes in Computer Science, vol. 13378 (2022). https://doi.org/10.1007/978-3-031-10562-3_45. ISBN: 978-3-031-10535-7
Perchinunno, P., Massari, A., L’Abbate, S., Mongelli, L.: Sustainable Development Goals per l’analisi statistica della povertà. Metodi e analisi statistiche, Dipartimento di Economia e Finanza, Università degli studi di Bari Aldo Moro., pp. 27–40 (2022). ISBN 978-88-6629-078-0
Perchinunno, P., Rotondo, F., Mongelli, L., L’Abbate, S.: Ecological transition and sustainable development integrated statistical indicators to support public policies. Sci. Rep. 12, 18513 (2022). https://doi.org/10.1038/s41598-022-23085-0
Anelli, D., Tajani, F.: Spatial decision support systems for effective ex-ante risk evaluation: an innovative model for improving the real estate redevelopment processes. Land Use Policy 128, 106595 (2023)
Anelli, D., Tajani, F., Ranieri, R.: Urban resilience against natural disasters: mapping the risk with an innovative indicators-based assessment approach. J. Clean. Prod. 371, 133496 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Perchinunno, P., Massari, A., L’Abbate, S., Carbonara, M. (2023). A Spatial Statistical Approach for the Analysis of Urban Poverty. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14106. Springer, Cham. https://doi.org/10.1007/978-3-031-37111-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-37111-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37110-3
Online ISBN: 978-3-031-37111-0
eBook Packages: Computer ScienceComputer Science (R0)