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This study compares adjacent and bivariate maps in communicating variance-based global sensitivity analysis (GSA) results for a geodiversity assessment spatial multi-criteria model and examines the influence of prior exposure to geodiversity and map reading skills on interpretation. It analyzes the quality of map interpretation, confidence levels, and map communication effectiveness. The findings indicate that there is no significant difference in the quality of map interpretation or confidence levels between the two map types. However, there are nuanced differences in interpretive patterns, suggesting the need for further investigation into factors affecting map interpretation. Adjacent maps are more effective in identifying factors linked to uncertainty in high geodiversity values, while bivariate maps excel in understanding spatial variability. Prior exposure to geodiversity and map reading skills do not significantly impact interpretation quality or confidence levels. Future research could explore other factors influencing map effectiveness and explore the cognitive processes underlying map interpretation. Understanding these processes could lead to more effective strategies for communicating the results of a GSA for spatial models through maps. [ABSTRACT FROM AUTHOR]

The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models. [ABSTRACT FROM AUTHOR]

The conventional methods of Multi-Criteria Evaluation (MCE) fail to account for local variability in criteria values and criteria preferences, disregarding local influence in spatial decision making. Moreover, understanding the interaction between MCE model input and output has paramount importance in terms of the level of confidence in the model results and their subsequent use in decision making. This research contributes to spatial MCE methodology by articulating the presence of uncertainty included in criteria weights and extending a local MCE technique with a spatially explicit uncertainty and sensitivity analysis. The article presents a methodology for multi-criteria evaluation extended by an integrated uncertainty-sensitivity analysis and illustrates it on the example of a land prioritization model for conservation practices. [ABSTRACT FROM AUTHOR]