Proposta metodologica per lo studio delle relazioni spaziali tra unita' microterritoriali con dati aggregati

Cartographic representation of spatial data of is a useful means to highlight rankings of desease/excellence among small areas as regard to an indicator of interest, otherwise possible spatial correlations are not immediatly pointed out in the analysis. In this paper we propose a methodology to perform a ranking of small areas (SubT) for level (1Y) and temporary variations (2Y) of a discretized numeric indicator y, starting from aggregate data (big areas, UT). Many methodologies which simultaneously want to estimate scores of 1Y, 2Y and the factors which produce the ranking performed are based on principal components, but, in our opinion, other approachs seem more appropriate, for example, predictive methods based on (generalised) regression. Unfortunately this methodology is not applicable in the contest of cartographic representation of a discretized dependent variable and we propose an Ordinal Logit model for 1Y and 2Y: the model specificated allows to investigate the role (weight) of predictors in the determination of the ranking performed (on UT) and suggests a way of ranking SubT