Hot spot di povertà: alcune realtà a confronto

Poverty clusters have high concentration of poor people, but that does not mean that everyone living in them is poor. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy: for example, in poverty areas earnings are lower and unemployment is higher, as well as adverse housing and neighbourhood conditions are more frequent. The fuzzy set approach to multidimensional poverty measurement is enjoying increasing popularity A different but strongly related issue concerns the geoinformatic surveillance for poverty hot-spot detection: hot- spot means a local “outbreak” of persistent poverty typologies. Circle-based spatial-scan statistics (Kulldorff, 1997, Patil and Taille, 2004, Aldstat and Getis, 2006) is a popular approach, and is now widely used by many governments and academic researchers. In this paper we define a [0-1]-valued fuzzy poverty measure for the census sections in the urban area of Bologna, Cagliari and Naples, in Italy: data were drawn from the 2001 Italian General Census. The upper level set scan statistics applied to a continuous response variable (Patil et al., 2006, Patil et al., 2007) was used to successfully identifying poverty clusters. The implications and possibilities for applications to digital governance are also discussed.