Statistical Methods for Detecting Geographical Clustering of Housing Poverty

Abstract

The fuzzy set approach to multidimensional poverty measurement is enjoying increasing popularity. A different, yet strongly related issue concerns geo-informatics surveillance for poverty hot-spot detection: hot-spot refers to a local outbreak of persistent poverty typologies. Circle-based spatial-scan statistics is a popular approach, now widely used by many governments and academic research teams. In this paper we define a [0;1] valued fuzzy poverty measure for the census sections in the urban area of Bari, Apulia, Italy. The scan statistics (SaTScan) and other methods (DBSCAN) were used to successfully identifying poverty clusters. The implications for digital governance are also discussed.


Tutti gli autori

  • MASSARI A.;PERCHINUNNO P.;MONTRONE S.

Titolo volume/Rivista

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Anno di pubblicazione

2011

ISSN

0035-6832

ISBN

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