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Principal component analysis, in its standard version, might not be appropriate for the analysis of spatial data. Particularly, the presence of spatial heterogeneity has been recognized as a possible source of misspecification for the derivation of composite indicators using principal component analysis. In recent times, geographically weighted approach to principal component analysis has been used for the treatment of continuous heterogeneity. However, this technique poses problems for the treatment of discrete heterogeneity and the interpretation of the results. The aim of this paper is to present a new approach to consider spatial heterogeneity in principal component analysis by using simulated annealing algorithm. The proposed method is applied for the definition of a composite indicator of local services for 121 municipalities in the province of Rome. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique based on an iterated spatially weighted regression procedure to verify the presence of economic growth heterogeneities in EU regions in the period 1981-2009. The approach extends a procedure originally proposed in the field of image analysis based on the assumption of local homogeneity of the signal. The presence of the heterogeneity is a criterion to divide the sample of observations (i.e. regions) into smaller homogeneous groups. Our results highlight the presence in the EU regions of five different clubs with different growth paths within each subgroup. Spatial dependence is also considered in the definition of the economic convergence model. [ABSTRACT FROM AUTHOR]

Building on the idea that different hospitals may operate with different technologies, the objective of this paper is a spatial characterization of the production function of hospital services through the identification of different spatial regimes. We introduce an original methodology to identify spatially constrained regimes, namely spatially constrained portions of territory in which the production units are maximally homogeneous in functional terms. The empirical algorithm can be described as a k -means cluster-wise regression procedure in which the units are belonging to a proximity graph and where distance is assessed in regressive terms. The analysis is implemented, first, on simulated data and, then, on output and input data of Italian hospitals for the year 2010. Our results, besides their methodological value, allow to shed light on the working of the hospital sector in Italy. The heterogeneity of the identified technological regimes can be associated to spatial heterogeneity of relevant aspects, like demand, internal organization, clinical and managerial governance, etc., and consequential policy implications can be, therefore, gathered. • A methodology to identify spatially constrained regimes, with production units maximally homogeneous in functional terms. • A production function is estimated for a large sample (681) Italian hospitals. • Spatial heterogeneity of relevant aspects associated to the heterogeneity of the spatial regimes, for policy implications. [ABSTRACT FROM AUTHOR]