Towards spatial composite indicators: A case study on Sardinian landscape

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.