Testing popular visualization techniques for representing model uncertainty.

Introduction

It has been demonstrated that simple and straightforward simulation models within a Spatial Decision Support System (SDSS) can be used to design land-use allocation alternatives (e.g., Grabaum and Meyer 1998). These models are usually implemented in the so-called "computational framework" of the SDSS (Aerts and Heuvelink 2002). Although model results do not always represent a complete overview of all possible allocation alternatives, they do supply a decision-maker with a quick, and to a certain extent, reliable overview of feasible solutions.

"Certain extent" refers to the uncertainty of the SDSS and, in this paper, to the uncertainty of an urban growth model called SLEUTH (slope, land use, exclusion, urban extent, transportation, and hill shade) developed for use within an SDSS environment (Figure 1; Aerts 2002; Clarke and Gaydos 1998). Uncertainty in this context may be referred to as uncertainty in the input data and model formulations (e.g., Cleaves 1995; Heuvelink 1998; Ehlschlaeger 2002). Managing uncertainty for decision-making issues involves quantifying uncertainty, and having a thorough understanding of how uncertainty propagates through different operations in the model. Moreover, it involves learning how to make a decision when uncertainty is present and communicating uncertainty to decision-makers. Researchers have responded to these issues by developing numerous concepts and techniques that can be used to quantify uncertainty and its propagation for decision-making problems (e.g., Hunter and Goodchild 1995; Heuvelink 1998).

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It is commonly agreed that visualizing uncertainty is a critical aspect of using spatial data and models within decision-making problems, such as land allocation problems (Armstrong et al. 1992; Goodchild et al. 1994; Hunter 1999). In addition there are a considerable number of visualization techniques available to communicate uncertainty of GIS (geographic information system) data and models to users (MacEachren 1994; MacEachren and Kraak 1997; Pang et al. 1997; Beard and Buttenfield 1991; 1999). There is, however, little literature on the effectiveness of visualizing uncertainty in this context (Ne...

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