Are we eliciting the right preferences? Insights from spatial MCDA sensitivity and uncertainty analysis

Planning support systems (PSS) based on geographic information systems (GIS) and multi-criteria decision analysis (MCDA) are sensitive to uncertainties in input parameters, yet these are often overlooked, with attention mainly on objective weighting. This study applies uncertainty analysis and Sobol global sensitivity analysis to assess key preference-related uncertainties in the Spatial Suitability Analysis Tool (SSANTO), a GIS-MCDA PSS for blue-green infrastructure (BGI) planning. We examined weights of objective, attribute value scales and the additivity parameter (γ) of the aggregation function, which controls how individual attributes are combined into overall suitability. Using spatially explicit Sobol analysis with 90,000 Monte Carlo simulations, we showed a limited subset of parameters drives outcomes: γ is dominant, followed by weights of objective, while value scales have minor effects. Spatial sensitivity is important, as low-average contributors may exert strong local effects. These findings guide parameter prioritization, data collection, and design of robust, transparent GIS-MCDA workflows, providing a framework for analyzing preference uncertainties.