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Towards Spatial Composite Indicators: A Case Study on Sardinian Landscape

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  • Daniele Trogu

    (Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, 09123 Cagliari, Italy)

  • Michele Campagna

    (Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, 09123 Cagliari, Italy)

Abstract

Composite Indicators (CIs) recently earned popularity as decision-support tool in policy-making for their ability to give concise measures of complex phenomena. Despite growing diffusion of the use of CI in policy-making, current research has barely addressed the issue of the spatial dimension of input data and of final indicator scores. Nowadays the spatial dimension of data plays a crucial role in analysis, thanks to recent developments in spatial data infrastructures which has enabled seamless access to a large amount of geographic information. In addition, recent developments in spatial statistical techniques are facilitating the understanding of the presence of spatial effects among data, spatial dependence and spatial heterogeneity. These advances are improving our ability to understand the spatial dimension of information, which is crucial to obtain a more robust representation of the territorial reality and insights of territorial dynamics in order to inform decisions in spatial planning and policy-making. This paper proposes an original method for the integration of spatial multivariate analysis and the use of spatial data to extend existing state of the art methods for CIs, as a step towards the construction of Spatial Composite Indicators. The method was successfully tested on a landscape planning case study.

Suggested Citation

  • Daniele Trogu & Michele Campagna, 2018. "Towards Spatial Composite Indicators: A Case Study on Sardinian Landscape," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1369-:d:143643
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    2. Paolo Postiglione & Alfredo Cartone & M. Simona Andreano & Roberto Benedetti, 2023. "Constrained optimization for addressing spatial heterogeneity in principal component analysis: an application to composite indicators," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1539-1561, December.
    3. Matheus Pereira Libório & Alexandre Magno Alvez Diniz & Douglas Alexandre Gomes Vieira & Petr Iakovlevitch Ekel, 2024. "Subjective–Objective Method of Maximizing the Average Variance Extracted From Sub-indicators in Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(2), pages 613-637, November.
    4. Monica Palma & Sandra De Iaco & Claudia Cappello & Veronica Distefano, 2024. "Tourism composite spatial indicators through variography and geographically weighted principal components analysis," Annals of Operations Research, Springer, vol. 342(3), pages 1687-1705, November.
    5. Annalina Sarra & Eugenia Nissi, 2020. "A Spatial Composite Indicator for Human and Ecosystem Well-Being in the Italian Urban Areas," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 353-377, April.

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