Inequalities in the Provinces of Abruzzo: A Comparative Study Through the Indices of Deprivation and Principal Component Analysis

  • Domenico Di Spalatro
  • Fabrizio Maturo
  • Lorella Sicuro
Chapter
First Online:
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 104)

Abstract

The indices of deprivation are a valuable tool to measure the socioeconomic disadvantage in certain geographical areas of interest. This study aims to compare inequalities between the provinces of Abruzzo over the last two decades suggesting some indices of deprivation to capture the key aspects of the great wealth of information relating to population census. Specifically, we propose three indices of deprivation to measure the material and social disadvantage. Moreover, a principal component analysis is performed using the most know indicators of deprivation. Using these methods, we observe an increase in the proportion of disadvantaged areas in the Abruzzo region from 1991 to 2011 in its four provinces.

Keywords

Deprivation indicator Disadvantaged areas IDM IDS IAS 
This is a preview of subscription content, log in to check access.

References

  1. Bartley, M., & Blane, D. (1994). Commentary: Appropriateness of deprivation indices must be ensured. BMJ, 309(6967), 1479–1479. doi: 10.1136/bmj.309.6967.1479.
  2. Betti, G., & Verma, V. (2007). Fuzzy measures of the incidence of relative poverty and deprivation: a multi-dimensional perspective. Statistical Methods and Applications, 17(2), 225–250. doi: 10.1007/s10260-007-0062-8.
  3. Blane, D., Townsend, P., Phillimore, P., & Beattie, A. (1989). Health and Deprivation: Inequality and the North. The British Journal of Sociology, 40(2), 344. doi: 10.2307/590279.
  4. Cadum, E., Costa, F., Biggeri, A., & Martuzzi, M. (1999). Deprivazione e mortalità: un indice di deprivazione per l’analisi delle disuguaglianze su base geografica, Epidemiologia e Prevenzione, 23, 175–187.Google Scholar
  5. Carstairs, V., & Morris, R. (1989). Deprivation and health. BMJ, 299(6713), 1462–1462. doi: 10.1136/bmj.299.6713.1462-a.
  6. Di Battista, T., Fortuna, F., Maturo, F. (2014). Parametric functional analysis of variance for fish biodiversity, in: International Conference on Marine and Freshwater Environments, iMFE 2014. URL:www.scopus.com.
  7. Di Battista, T., Fortuna, F., Maturo, F. (2016a). Environmental monitoring through functional biodiversity tools. Ecological Indicators, 60, 237–247. doi: 10.1016/j.ecolind.2015.05.056.
  8. Di Battista T., Fortuna F., Maturo F. (2016b). BioFTF: An R Package for Biodiversity Assessment with the Functional Data Analysis Approach. Ecological Indicators. doi: 10.1016/j.ecolind.2016.10.032.
  9. Di Battista T., Fortuna F., Maturo F. (2016c). Parametric Functional Analysis of Variance for Fish Biodiversity Assessment. Journal of Environmental Informatics. doi: 10.3808/jei.201600348.
  10. Fattore, M., Brüggemann, R., & Owsiński, J. (2011). Using Poset Theory to Compare Fuzzy Multidimensional Material Deprivation Across Regions. New Perspectives in Statistical Modeling and Data Analysis, 49–56. doi: 10.1007/978-3-642-11363-5_6.
  11. Forrest, R., & Gordon, D., (1993). People and Places: a 1991 Census atlas of England, SAUS. University of Bristol.Google Scholar
  12. Gordon, D. (1995). Census based deprivation indices: their weighting and validation. Journal of Epidemiology & Community Health, 49(Suppl 2), S39–S44. doi: 10.1136/jech.49.suppl_2.s39.
  13. Jarman, B. (1983). Identification of underprivileged areas. BMJ, 286(6379), 1705–1709. doi: 10.1136/bmj.286.6379.1705.
  14. Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: how can we conceptualise, operationalise and measure them? Social Science & Medicine, 55(1), 125–139. doi: 10.1016/s0277-9536(01)00214-3.
  15. Maturo, F. (2016). Dealing with randomness and vagueness in business and management sciences: the fuzzy-probabilistic approach as a tool for the study of statistical relationships between imprecise variables. Ratio Mathematica 30, 45–58.Google Scholar
  16. Maturo, F., & Fortuna, F. (2016). Bell-Shaped Fuzzy Numbers Associated with the Normal Curve. Topics on Methodological and Applied Statistical Inference, 131–144. doi: 10.1007/978-3-319-44093-4_13.
  17. Maturo, A., & Maturo, F. (2013). Research in Social Sciences: Fuzzy Regression and Causal Complexity. Studies in Fuzziness and Soft Computing, 237–249. doi: 10.1007/978-3-642-35635-3_18.
  18. Maturo, A., & Maturo, F. (2016). Fuzzy Events, Fuzzy Probability and Applications in Economic and Social Sciences. Studies in Systems, Decision and Control, 223–233. doi: 10.1007/978-3-319-40585-8_20.
  19. Maturo A. & Maturo F. (2014). Finite Geometric Spaces, Steiner Systems and Cooperative Games. Analele Universitatii “Ovidius” Constanta. Seria Matematica. Vol. 22(1), pp. 189–205. ISSN: Online 1844-0835. doi:  10.2478/auom-2014-0015.
  20. Maturo, F., & Hošková-Mayerová, Š. (2016). Fuzzy Regression Models and Alternative Operations for Economic and Social Sciences. Studies in Systems, Decision and Control, 235–247. doi: 10.1007/978-3-319-40585-8_21.
  21. Michelozzi, P., Perucci, C., Forastiere, F., Fusco, D., Ancona, A., & Dell’Orco V. (1999). Differenze sociali nella mortalità a Roma negli anni 1990–1995, Epidemiologia e Prevenzione, 23, 230-238.Google Scholar
  22. Minerba, D., & Vacca, D. (2006). Gli indici di deprivazione per l’analisi delle disuguaglianze tra i comuni della Sardegna. Istituto Nazionale di statistica. http://www.istat.it/it/archivio/6727.
  23. Najjary, Z., Saremi, H., Biglarbegian, M., & Najari, A. (2016). Identification of deprivation degrees using two models of fuzzy-clustering and fuzzy logic based on regional indices: A case study of Fars province. Cities, 58, 115–123. doi: 10.1016/j.cities.2016.05.013.
  24. Potsi, A., D’Agostino, A., Giusti, C., & Porciani, L. (2015). Childhood and capability deprivation in Italy: a multidimensional and fuzzy set approach. Qual Quant. doi: 10.1007/s11135-015-0277-y.
  25. Ramsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi: 10.1007/978-1-4757-7107-7.
  26. Testi, A., Ivaldi, E., & Busi, A. (2005). Caratteristiche e potenzialità informative degli indici di deprivazione, Tendenze nuove, 111–124.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Domenico Di Spalatro
    • 1
  • Fabrizio Maturo
    • 2
  • Lorella Sicuro
    • 1
  1. 1.National Institute of Statistics - ISTATPescaraItaly
  2. 2.Department of Business Administration -“G. d’Annunzio” University of Chieti-PescaraPescaraItaly

Personalised recommendations