This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogeneity by adopting geographically weighted principal component analysis at a fine spatial resolution. Moreover, it focuses on dependence by introducing a novel approach based on spatial filtering. These methods are applied in order to derive a composite indicator of socioeconomic deprivation in the Italian province of Rome while considering two spatial scales: municipalities and localities. The results show that considering spatial information uncovers a range of issues, including neighbourhood effects, which are useful in order to improve local policies.

Since some decades, inequality is attracting a growing interest within the political debate as well as in theoretical and empirical studies. Considering inequality at regional level offers useful insights for policy makers, facilitating the assessment of the effectiveness of strategies aimed at reducing regional disparities and helping in developing place-based actions. The study of regional inequality poses some relevant issues related to the spatial nature of data. In fact, dealing with georeferenced data implies the opportunity of considering the spatial interactions among regional units that are likely to play a role in shaping the inequality dynamics. Some studies highlighted the importance of incorporating spatial effects in a traditional measure of inequality such as the Gini index. These studies are based on the definition of a proximity structure, that allows one to discriminate between the spatial and the non-spatial component of inequality. Different definitions of the proximity structure are likely to influence the spatial component of inequality. Those aspects are analysed in the present paper to offer more detailed insight in the territorial dimension of inequality. The measures and their decompositions are discussed in the case of European NUTS 3 regions.

The topic of economic convergence is still crucial in the European Union (EU) as promoting regional growth and the reduction of disparities remains a key objective. In this paper we investigate development and economic growth across EU regions. Particular attention is given to σ-convergence and β-convergence. These analyses are carried out for regional units corresponding to the third level of the NUTS (Nomenclature of Territorial Units for Statistics) classification. Focusing on a refined geographical scale could offer a detailed picture of the regional growth dynamics within the EU. Additionally, we use a spatial augmented version of the conditional β-convergence model to take into account the spatial interdependencies among regions. Findings shed light on the impact of spatial interaction effects and on the need of territorial policies to achieve convergence in the EU. This aspect highlights how coordinating the regional development policies between regions is pivotal to achieve economic, as well as social and political stability within the EU.

Spatial interactions among regional units may influence the geographical distribution of economic activities. Many traditional measures of geographical concentration fail in capturing this aspect, being insensitive to permutations of the spatial position of regions. This paper proposes an approach to the measurement of geographical concentration of economic activities that accounts for spatial interactions among regions. The locational Gini is split into spatial and non-spatial components, so that a new interpretation of the index is presented. The measure is applied to evaluate the geographical concentration of different economic sectors for 1,323 NUTS 3 regions in the European Union over the period 2001–2018.

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.