Visualising Uncertainty in Spatial Decision Support

Rachel O’Brien +
Institute for Land Water and Society, Charles Sturt University

Abstract. Uncertainty is an  issue in environmental spatial decision  support, as it is in most spatial modelling problems. When uncertainty is ignored in spatial modelling, issues can arise around the validity of decisions based on these models. This paper discusses sources of uncertainty in Spatial Decision Support Systems (SDSS) and introduces the SDSS CaNaSTA (Crop Niche Selection in Tropical Agriculture) based on Bayesian probability modelling. CaNaSTA focuses in  particular on visualising uncertainty introduced through lack of data or knowledge. The SDSS incorporates some sources of uncertainty into the structure of the model itself, and provides tools to visualise other sources of uncertainty. Although CaNaSTA has been developed for use in agricultural decision-making,  the model and tools used to handle and visualise uncertainty are applicable to all spatial decision tasks. This paper provides a case-study approach to acknowledging this uncertainty and ways of managing it in a spatial decision making context.

Keywords: Spatial Decision Support Systems, CaNaSTA, visualising uncertainty

In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).

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