Abstract
Introducing fuzzy sets as an extension in modelling categories has drawn the attention on the discussion on different types of certainty as well as uncertainty. Fuzzy sets broadened the concepts on dealing with error and imprecision, which are issues well known to spatial analysis. Eliminating or tracking error and imprecision has been contributing to increase knowledge with respect to spatial phenomena. Notions in space and spatial terms became more precise in a complex decision making environment, too. The use of fuzzy sets in GIS applications triggered the question on what kind of uncertainty fuzzy logic is aiming at in the process of gaining precise knowledge. Following an approach discussed in [10] ambiguity and vagueness are addressed by fuzzy methodology. Uncertainty in knowledge (i.e. ambiguity) occurs when values are associated with multiple attributes and no reliable decision criterion is available, e.g. “Is a particular elevation a ‘hill’ or a ‘mountain’ ?”. Uncertain knowledge (i.e. fuzziness or vagueness) is due to our inability to make sharp and precise distinctions in our world, e.g. “What is a ‘mountain’ ?” as opposed to “What is not a ‘mountain’ ?”. Whereas uncertainty in knowledge is being dealt with by probability and possibility measures, uncertain knowledge is addressed by measures of fuzziness [8].
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