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.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. Montrone (chapter 4), to F. Campobasso (chapter 1 and 2.2), to P. Perchinunno (chapter 3.1 and 5), and to A. Fanizzi (chapter 2.1 and 3.2).
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
Preview
Unable to display preview. Download preview PDF.
References
Fusco Girard, L., Forte, B.: Città sostenibile e Sviluppo Umano. F. Angeli, Milano (1997)
Bayliss, D.: Some recend trends in forecastings. CES, London (1968)
Towsend, P.: Poverty in The United Kingdom. Penguin, Harmondsworth (1979)
Gailly, B., Hausman, P.: Desavantages relatifs à une mesure objective de la pauvreté. In: Sarpellon, G. (ed.) Understanding poverty. Franco Angeli, Milano (1984)
Mack, J.E., Lansley, S.: Poor Britain. Allen and Unwin, London (1985)
Desai, M.E., Shah, A.: An econometric approach to the measurement of poverty. Oxford Economic Paper 40 3, 505–522 (1988)
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)
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)
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)
Patil, G.P., Taillie, C.: Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ-mental and Ecological Statistics 11, 183–197 (2004)
Kulldorff, M., Nagarwalla, N.: Spatial disease clusters: detection and inference. Statistics in Medicine 14, 799–810 (1995)
Kuldorff, M.: A spatial scan statistics. Communication in Statistics: Theory and Methods 26, 1481–1496 (1997)
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)
Purcell, J.N., Kish, L.: Estimation for Small Domain. Biometrics 35, 367–384 (1979)
Lohr, S.L., Prasad, N.G.N.: Small Area Estimaion with Auxiliary Survey Data, Statistics Centre Technical Report, AlbertaUniversity, California (2001)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Dubois, D., Prade, H.: Fuzzy sets and systems. Academic Press, Boston (1980)
Cheli, B., Lemmi, A.A.: Totally Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty. Economic Notes 24(1), 115–134 (1995)
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)
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)
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)
Kulldorff, M.: SaTScanTM User Guide (26 August, 2006), http://www.satscan.org/
Takahashi, K., Tango, T.: A flexibly shaped spatial scan statistic for detecting clusters. International Journal of Health Geographics 4, 11–13 (2005)
Aldstadt, J., Getis, A.: Using AMOEBA to create spatial weights matrix and identify spatial clusters. Geographical Analysis 38, 327–343 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Berlin Heidelberg
About this paper
Cite this paper
Montrone, S., Campobasso, F., Perchinunno, P., Fanizzi, A. (2010). A Fuzzy Approach to the Small Area Estimation of Poverty in Italy. In: Phillips-Wren, G., Jain, L.C., Nakamatsu, K., Howlett, R.J. (eds) Advances in Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14616-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-14616-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14615-2
Online ISBN: 978-3-642-14616-9
eBook Packages: EngineeringEngineering (R0)