Abstract
The presence of a varied range of definitions on the theme of poverty underlines the necessity of no longer relying on a single indicator yet rather on a group of indicators, useful in the definition of living conditions of various subjects. The starting point for this approach derives from the necessity of identifying, on the basis of statistical data, geographical zones of urban poverty, in the specific case of the area of the city of Bari. Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analysis techniques in order to understand the various phenomena. In particular, spatial clustering algorithms, which groups similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this paper is to present a density based algorithm for the discovery of clusters of units in large spatial data sets. These approaches have been improved using the SaTScan and DBSCAN or Seg-DBSCAN methodology. Further developments concern the application of a DENCLUE "weighed", obtained using the intensity of a phenomenon instead of the density kernel.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. Montrone (chapters 4 and 5) and to P. Perchinunno (chapters 1, 2 and 3).
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References
Whelan, C.T.: The role of income, life-style deprivation and financial strain in mediating the impact of unemployment on psychological distress. Working Paper 20. Economic and Social Research Institute, Dublin (1991)
Montrone, S., Campobasso, F., Perchinunno, P., Fanizzi, A.: An Analysis of Poverty in Italy through a Fuzzy Regression Model. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part I. LNCS, vol. 6782, pp. 342–355. Springer, Heidelberg (2011)
Cheli, B., Lemmi, A.: A “Totally” Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty. Economic Notes 24(1), 115–134 (1995)
Kulldorff, M.: SaTScanTM User Guide (August 26, 2006), http://www.satscan.org/
Ester, M., Kriegel, H.P., Sander, J., Xiaowei, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceeding of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 94–99 (1996)
Takahashi, K., Tango, T.: A flexibly shaped spatial scan statistic for detecting clusters. International Journal of Health Geographics 4, 11–13 (2005)
Patil, G.P., Taillie, C.: Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environmental and Ecological Statistics 11, 183–197 (2004)
Aldstadt, J., Getis, A.: Using AMOEBA to create spatial weights matrix and identify spatial clusters. Geographical Analysis 38, 327–343 (2006)
Bilancia, M., Montrone, S., Perchinunno, P.: A Model-Based Scan Statistics for Detecting Geographical Clustering of Disease. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2009, Part I. LNCS, vol. 5592, pp. 353–368. Springer, Heidelberg (2009)
Waller, L.A., Gotway, C.A.: Applied Spatial Statistics for Public Health Data. John Wiley & Sons, New York (2004)
Montrone, S., Perchinunno, P., Torre, C.M.: Analysis of Positional Aspects in the Variation of Real Estate Values in an Italian Southern Metropolitan Area. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010. LNCS, vol. 6016, pp. 17–31. Springer, Heidelberg (2010)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: Ordering points to identify the clustering structure. In: Proceedings SIGMOD 1999, pp. 49–60. ACM Press (1999)
Hinneburg, A., Keim, D.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings KDD 1998, pp. 58–65. AAAI Press (1998)
Hinneburg, A., Keim, D.: A general approach to clustering in large databases with noise. Knowledge and Information Systems, KAIS 5(4), 387–415 (2003)
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)
Bilancia, M., Campobasso, F., Fanizzi, A.: The pricing of risky security in a Fuzzy Least Square Regression Model. In: Locarek-junge, Weishs (eds.) Studies in Classification, Data Analysis and Knowledge Organization – Classification as a Tool for Research, pp. 639–646. Springer, Heidelberg (2010)
Montrone, S., Bilancia, M., Perchinunno, P., Torre, C.M.: Economic Evaluation and Statistical Methods for Detecting Hot Spots of Social and Housing Difficulties in Urban Policies. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2009, Part I. LNCS, vol. 5592, pp. 253–268. Springer, Heidelberg (2009)
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Montrone, S., Perchinunno, P. (2012). Clustering for the Localization of Degraded Urban Areas. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_8
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