In the last decades green infrastructure planning, design, and management have been widely recognized as a way to contribute to reach higher levels of sustainability of development. However, often green infrastructures are considered in a sectoral way, while their design should be more integrated within comprehensive planning and design. The paper proposes the use of geodesign methods and technology to support the early phases of integrated strategic territorial planning, in order to enrich the relationships between the design of green infrastructure and of the other relevant systems via more comprehensive planning and design, and by applying systems thinking. A case study developed with architecture and engineering students under the umbrella of the International Geodesign Collaboration is used, to demonstrate how with intensive geodesign workshops it is possible to create spatially explicit design scenarios which take into account the relationships between green infrastructure and other territorial systems and dynamics. A set of analyses on the case study results of the two scales is used to demonstrate the assumption. It is also argued that geodesign intensive workshops can, in a very short time, contribute to raising the awareness among the participants of collaborative design to the importance of green infrastructure in strategic territorial planning.

In this paper we present the findings of a study undertaken by the Joint Research\ud Centre of the European Commission in collaboration with the regional authority of\ud Lombardy, in northern Italy, on the socioeconomic impact of the spatial data infrastructure\ud (SDI) of that region. The study took place in 2009 and focused primarily on assessing the\ud benefits of the SDI to the organisations outside public administration, that is, developers\ud and consultants involved in planning, design, and impact assessment. We report the\ud results of two surveys undertaken in 2009. The first addressed consultants involved in\ud the preparation of environmental impact assessments and strategic environmental\ud assessments, and found significant economic and policy benefits resulting from the use\ud of the regional SDI. The second survey focused on professionals involved in planning\ud and design (architects, engineers, planners) and found a much more varied landscape\ud characterised by lack of awareness and skill in taking advantage of the new infrastructure\ud of analytical tools. The results of this study demonstrate that the maturity reached by\ud some of the existing SDIs now makes it possible to evaluate tangible benefits, and act as an\ud incentive to analyse further how SDIs are producing positive externalities. The knowledge\ud gained by monitoring the impacts of on-going SDI initiatives can be used to justify\ud investments ex post, and inform further SDI development and wider positive impacts

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.