ABSTRACT
As in many studies involving disparate data and methodologies, environmental health risk assessment research is prone to errors and uncertainties. Unfortunately, uncertainties associated with the research findings can seldom be eliminated, and policy decisions often need to be made based on the uncertain findings. Hence methods that can help reduce uncertainties associated with the research findings would be most useful and need to be developed. This article highlights, through a case study, four major sources of uncertainties, which include uncertainties from data, methods of analysis, interpretations of the findings, and reactions to the findings. Five groups of geospatial methods that can help reduce the uncertainties as a result of combinations of the four sources are identified, including methods for visualization and measurement, cluster detection, exposure modeling, scale analysis, and decision support. The article concludes that future effort should focus more on the development of decision tools to enable decision making based on uncertain findings.
