The aim of this paper is to identify territorial areas and/or population subgroups characterized by situations of deprivation or strong social exclusion through a fuzzy approach that allows the definition of a measure of the degree of belonging to the disadvantaged group. Grouping methods for territorial units are employed for areas with high (or low) intensity of the phenomenon by using clustering methods that permit the aggregation of spatial units that are both contiguous and homogeneous with respect to the phenomenon under study. This work aims to compare two different clustering methods: the first based on the technique of SaTScan [Kuldorff: A spatial scan statistics. Commun. Stat.: Theory Methods 26, 1481–1496 (1997)] and the other based on the use of Seg-DBSCAN, a modified version of DBSCAN [Ester et al.: 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)].

Socioeconomic Zoning: Comparing Two Statistical Methods

PERCHINUNNO, Paola;MONTRONE, Silvestro
2012

Abstract

The aim of this paper is to identify territorial areas and/or population subgroups characterized by situations of deprivation or strong social exclusion through a fuzzy approach that allows the definition of a measure of the degree of belonging to the disadvantaged group. Grouping methods for territorial units are employed for areas with high (or low) intensity of the phenomenon by using clustering methods that permit the aggregation of spatial units that are both contiguous and homogeneous with respect to the phenomenon under study. This work aims to compare two different clustering methods: the first based on the technique of SaTScan [Kuldorff: A spatial scan statistics. Commun. Stat.: Theory Methods 26, 1481–1496 (1997)] and the other based on the use of Seg-DBSCAN, a modified version of DBSCAN [Ester et al.: 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)].
978-88-470-2750-3
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/67936
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