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

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  • Alfredo Cartone
  • Paolo Postiglione

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.

Suggested Citation

  • Alfredo Cartone & Paolo Postiglione, 2021. "Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(2), pages 126-147, April.
    • Handle: RePEc:taf:specan:v:16:y:2021:i:2:p:126-147
    DOI: 10.1080/17421772.2020.1775876
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    Cited by:

    1. Giacalone, Massimiliano & Mattera, Raffaele & Nissi, Eugenia, 2022. "Well-being analysis of Italian provinces with spatial principal components," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Matheus Pereira Libório & João Francisco Abreu & Petr Iakovlevitch Ekel & Alexei Manso Correa Machado, 2023. "Effect of sub-indicator weighting schemes on the spatial dependence of multidimensional phenomena," Journal of Geographical Systems, Springer, vol. 25(2), pages 185-211, April.
    3. Matheus Pereira Libório & Elisa Fusco & Alexandre Magno Alves Diniz & Oséias da Silva Martinuci & Petr Iakovlevitch Ekel, 2024. "A Novel Approach for Multispatial and Multitemporal Analysis of Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 173(3), pages 783-800, July.
    4. Seong-Yun Hong & Seonggook Moon & Sang-Hyun Chi & Yoon-Jae Cho & Jeon-Young Kang, 2022. "Local Sparse Principal Component Analysis for Exploring the Spatial Distribution of Social Infrastructure," Land, MDPI, vol. 11(11), pages 1-16, November.
    5. Alfredo Cartone & Domenica Panzera & Paolo Postiglione, 2022. "Regional economic disparities, spatial dependence and proximity structures," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(5), pages 1034-1050, October.
    6. Matheus Pereira Libório & Alexandre Magno Alvez Diniz & Angélica Cidália Gouveia Santos & Cristiane Neri Nobre & Douglas Alexandre Gomes Vieira & Hasheem Mannan & Marcos Flávio Silveira Vasconcelos Da, 2024. "Benefit-of-the-Doubt in the Spatial Analysis of Child Well-Being in European Countries," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 17(4), pages 1851-1870, August.

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