Developing spatially explicit methods to enhance geovisual analytics and decision support

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When faced with important decisions, we try to base our decision-making on a rational framework which often includes multiple decision criteria. Multi-criteria decision analysis (MCDA) was first introduced to the field of Geographic Information Systems (GIS) nearly 20 years ago. GIS-based MCDA is now conducted in various fields of application, including environmental, transportation, and urban/regional planning, in waste management, as well as in hydrology, agriculture, and forestry. Paradoxically, there has been little use of the locational characteristics in GIS-based MCDA besides the visualization of evaluation results on maps. For example, there is neither a generally accepted framework for spatially explicit decision criteria nor a systematic approach to incorporating spatial variations in criterion weights or decision rules. MCDA calculations are typically based on non-spatial criteria and do not include spatially explicit parameters or decision-maker preferences. The proposed research aims at closing this conceptual gap and demonstrating the utility of spatially explicit methods for multi-criteria decision-making in selected applications. My team will integrate geovisualization and MCDA tools with spatial statistics and develop a comprehensive map-centred methodology that guides users through valid sequences of decision-making tasks in fields such as community service planning and environmental assessments. We will also study the effects of spatial variations in decision criteria, criterion standardization, and criterion weighting on GIS-based MCDA results, and develop a framework for spatio-temporal MCDA. This research contributes to the body of knowledge in Geomatics. It has the potential to make GIS-based MCDA more effective and addresses conceptual issues in spatial decision support that may have hampered its broader acceptance. Ultimately, this research program will improve strategic decision-making by government, non-profit organizations, and businesses, where decisions have a geospatial component.