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A Fuzzy Approach to the Small Area Estimation of Poverty in Italy

  • Silvestro Montrone
  • Francesco Campobasso
  • Paola Perchinunno
  • Annarita Fanizzi
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 4)

Abstract

Urban poverty, especially in metropolitan areas, represents one of the most significant problems to both developed and developing countries. The aim of the present work is to identify territorial zones characterized by the presence of such a phenomenon. In particular, data gathered from the EU-SILC study for 2006 has been examined and elaborated in order to obtain estimates of poverty at a provincial level through the use of statistical methods such as Small Area Estimation and Total Fuzzy and Relative. The results obtained from this approach have been improved using SaTScan methodology for the graphical identification of homogeneous areas of poverty.

Keywords

Small Area Estimation Fuzzy logic SatScan poverty area 

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References

  1. 1.
    Fusco Girard, L., Forte, B.: Città sostenibile e Sviluppo Umano. F. Angeli, Milano (1997)Google Scholar
  2. 2.
    Bayliss, D.: Some recend trends in forecastings. CES, London (1968)Google Scholar
  3. 3.
    Towsend, P.: Poverty in The United Kingdom. Penguin, Harmondsworth (1979)Google Scholar
  4. 4.
    Gailly, B., Hausman, P.: Desavantages relatifs à une mesure objective de la pauvreté. In: Sarpellon, G. (ed.) Understanding poverty. Franco Angeli, Milano (1984)Google Scholar
  5. 5.
    Mack, J.E., Lansley, S.: Poor Britain. Allen and Unwin, London (1985)Google Scholar
  6. 6.
    Desai, M.E., Shah, A.: An econometric approach to the measurement of poverty. Oxford Economic Paper 40 3, 505–522 (1988)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Campobasso, F., Fanizzi, A., Perchinunno, P.: Homogenous urban poverty clusters within the city of Bari. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 232–244. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Lemmi, A., Pannuzi, N.: Fattori demografici della povertà, Continuità e discontinuità nei processi demografici. L’Italia nella transizione demografica. 4 Rubettino, Arcavacata di Rende, 211–228 (1995)Google Scholar
  10. 10.
    Patil, G.P., Taillie, C.: Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ-mental and Ecological Statistics 11, 183–197 (2004)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Kulldorff, M., Nagarwalla, N.: Spatial disease clusters: detection and inference. Statistics in Medicine 14, 799–810 (1995)CrossRefGoogle Scholar
  12. 12.
    Kuldorff, M.: A spatial scan statistics. Communication in Statistics: Theory and Methods 26, 1481–1496 (1997)CrossRefGoogle Scholar
  13. 13.
    Montrone, S., Campobasso, F.: La stima per piccole aree della disoccupazione in Puglia, Quaderno n. 9, Dip. Scienze Statistiche, Università degli Studi di Bari (2002)Google Scholar
  14. 14.
    Purcell, J.N., Kish, L.: Estimation for Small Domain. Biometrics 35, 367–384 (1979)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Lohr, S.L., Prasad, N.G.N.: Small Area Estimaion with Auxiliary Survey Data, Statistics Centre Technical Report, AlbertaUniversity, California (2001)Google Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Dubois, D., Prade, H.: Fuzzy sets and systems. Academic Press, Boston (1980)zbMATHGoogle Scholar
  18. 18.
    Cheli, B., Lemmi, A.A.: Totally Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty. Economic Notes 24(1), 115–134 (1995)Google Scholar
  19. 19.
    Cerioli, A., Zani, S.: A Fuzzy Approach to the Measurement of Poverty. In: Dugum, C., Zenga, M. (eds.) Income and Wealth Distribution, inequality and Poverty. Springer, Berlin (1990)Google Scholar
  20. 20.
    Perchinunno, P., Rotondo, F., Torre, C.M.: A Multivariate Fuzzy Analysis for the regeneration of urban poverty. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 137–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Montrone, S., Bilancia, M., Perchinunno, P., Torre, C.M.: Scan Statistics for the localization of hot spots of urban poverty. In: Conference Proceedings of the Regional Studies Association; Winter Conference; Londra, November 28, pp. 74–77 (2008)Google Scholar
  22. 22.
    Kulldorff, M.: SaTScanTM User Guide (26 August, 2006), http://www.satscan.org/
  23. 23.
    Takahashi, K., Tango, T.: A flexibly shaped spatial scan statistic for detecting clusters. International Journal of Health Geographics 4, 11–13 (2005)CrossRefGoogle Scholar
  24. 24.
    Aldstadt, J., Getis, A.: Using AMOEBA to create spatial weights matrix and identify spatial clusters. Geographical Analysis 38, 327–343 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Silvestro Montrone
    • 1
  • Francesco Campobasso
    • 1
  • Paola Perchinunno
    • 1
  • Annarita Fanizzi
    • 1
  1. 1.Department of Statistical ScienceUniversity of BariBariItaly

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