Comprehensive decision-strategy space exploration for efficient territorial planning strategies. (September 2020)

• Record Type: Journal Article
• Authors: Billaud, Olivier; Soubeyrand, Maxence; Luque, Sandra; Lenormand, Maxime
• Is Part Of: Computers, environment and urban systems. Volume 83(2020)
• Publication Date: 2020-09
• Subjects: Suitability analysis -- Multi-criteria decision analysis -- Ordered weighted averaging -- Land use planning -- Ecosystem services; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101516
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: GIS-based Multi-Criteria Decision Analysis is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance of criteria has been widely studied in the recent literature, the impact of the order weights used to combine the criteria have rarely been investigated. This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in GIS-based Multi-Criteria Analysis by Ordered Weighted Averaging. We propose a methodology based on an efficient exploration of the decision-strategy space defined by the level of risk and trade-off in the decision process. We illustrate our approach with a land use planning process in the South of France. The objective is to find suitable areas for urban development while preserving green areas and their associated ecosystem services. The ecosystem service approach has indeed the potential to widen the scope of traditional landscape-ecological planning by including ecosystem-based benefits, including social and economic benefits, green infrastructures and biophysical parameters in urban and territorial planning. We show that in this particular case the decision-strategy space can be divided into four clusters. Each of them is associated with a map summarizing the average spatial suitability distribution used to identify potential areas for urban development. We also demonstrate the pertinence of a spatial variance within-cluster analysis to disentangle the relationship between risk and trade-off values. At the end, we perform a site suitability ranking analysis to assess the relationship between the four detected clusters. Highlights: We propose a methodology to efficiently explore the decision-strategy space in MCDA. We assess the impact of order weights determination in MCDA by OWA. We evaluate this impact through a spatial sensitivity analysis. The approach is applied to a urban planning problem in the south of France.