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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]

Land use evaluation involves careful consideration of several environmental factors and their relative importance quantified by factor weights. Local multi-criteria evaluation provides a mechanism for computing factor (criteria) weights within local neighborhoods that capture spatial heterogeneity and contribute to more accurate evaluation results. The accuracy of results, however, is tempered by the potential uncertainty of criteria weights. The paper presents a spatially explicit approach to uncertainty and sensitivity analysis of local criteria weights and modeling scale on the variability of model output. The efficacy of the approach is presented on the example of Environmental Benefit Index (EBI) model used by the U.S. Department of Agriculture Conservation Reserve Program (CRP) to select environmentally sensitive agricultural areas for conservation. The uncertainty analysis resulted in identifying robust areas for CRP selection characterized by high suitability and low uncertainty. The sensitivity analysis focused on the next-best group of candidates characterized by high suitability and high uncertainty. The results show that there is a relationship between spatial heterogeneity, data representation scale, and the level of uncertainty in the results of EBI model. The sensitivity of model output can be attributed to both the uncertainty of criteria weights and the modeling scale. A potential practical value of this approach is the improved analytical support for land suitability evaluation requiring a consideration of sub-optimal land units (high suitability/high uncertainty). Also, this approach can guide modelling effort by allowing the analyst to visualize spatial distribution and patterns of model output uncertainty and focus data collection on influential model input factors. [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]

Visualization of the outputs from sensitivity analyses of spatially explicit models can potentially enhance the perception of the uncertainty in model outputs. However, spatial global sensitivity analysis (GSA) produces multiple output maps, twice as many as the number of input parameters under investigation, which can result in an overwhelming visual load to interpret. In this research, we tested the visual stacking of coincident maps to handle the visual complexity for an application of an urban land-use change model. The coincident maps were compared with the adjacent representation. Both types of maps were tested using a web-based survey to understand the efficacy in supporting the comprehension of the GSA results. The majority of the questions showed no statistically significant difference between visualization techniques; however, the difficulty of interpreting the adjacent maps was observed among novice users which suggesting the need for an update for the future representations using coincident maps. • Spatial GSA produces multiple output maps, and the number increases proportionally with the increase of input parameters. • We illustrate a new visual stacking representation which summarizes several GSA attributes for the same location. • The new technique was tested against the most commonly used spatial visualization technique applied for GSA. [ABSTRACT FROM AUTHOR]