Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators

Abstract

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. KEYWORDS spatial filtering, spatial dependence, geographically weighted principal component analysis (GWPCA), modifiable areal unit problem (MAUP), deprivation index JEL C1, C21, C43 HISTORY

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

“…Concerning spatial heterogeneity and clustering procedures, the use of a high resolution has been also recommended for the case of Italy (Cartone & Postiglione, 2020). In fact, the information on this spatial effect could be lost or distorted at aggregated levels, so that a higher resolution helps accuracy of clustering routines.…”

A development index for the Greek regions

“…In other words, the estimated weights are region-specific implying that they differ among regions. An advantage of this method, as any other method based on a “pure” statistical technique, is the reduction of subjectivity because weights are data-driven rather than they are assigned by the researcher (Cartone and Postiglione 2020 ). This method is typically employed with linear aggregation (Hoskins and Mascherini 2009 ).…”

“…al. 2018 ; Sarra and Nissi 2019 ; Walheer 2019 ; Cartone and Panzera 2020 ; Cartone and Postiglione 2020 ; Casolani et al 2020 ). As a result, in this study, we use the spatial robust Benefit of the Doubt technique that enables addressing for spatial heterogeneity in the construction of CIs, introducing an additional constraint associated with spatial proximity (Fusco et al 2018 ).…”

Deprivation at local level: Practical problems and policy implications for the province of Milan

“…Deprivation indices are flexible instruments useful to assess the level of inequalities, material needs, social exclusion, and to support regional and local policies. However, in many cases composite indicators are generically derived without including spatial features that can be extremely helpful for policy‐makers (Cartone & Postiglione, 2020). In fact, in setting policies to reduce disparities it is not desirable ignoring spatial scale.…”