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
According to the World Health Organization, the fraction of population living in urban area has notably increased from the 35% registered back in the 60’s to the 55% estimated in 2018; by 2050, 66% of the worlds population is projected to be urban [1]
Two types of weights can be considered while using Ordered Weighted Averaging (OWA) operators: the criterion weights representing the relative importance of the criteria in the decision process, and the order weights characterizing the level of risk and trade-off taken in the decision
In order to identify potential cluster of risk and trade-off values leading to similar suitability maps, we apply an ascending hierarchical clustering (AHC) algorithm on this dissimilarity matrix using the Ward’s agglomeration method
Summary
According to the World Health Organization, the fraction of population living in urban area has notably increased from the 35% registered back in the 60’s to the 55% estimated in 2018; by 2050, 66% of the worlds population is projected to be urban [1]. We rely on an Ordered Weighted Averaging (OWA) multi-criteria operator [17] to combine all the criteria together in order to obtain a spatial explicit representation, that was a final map, to allow decision makers and local expertes to base planning decisions. Two types of weights can be considered while using OWA operators: the criterion weights representing the relative importance of the criteria in the decision process, and the order weights characterizing the level of risk and trade-off taken in the decision. Except for some trivial cases, like the ones described above, the limits of the decision-strategy space remain unclear, making an efficient exploration very complicated This is due to the fact that, in practice, the generation of order weights according to a certain level of risk and trade-off is not formally established. It is important to note that for certain risk and trade-off
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