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Uncertainty Methods

Multicriteria spatial error analysis aims evaluating the effects of errors (uncertainties) associated with the criterion maps and the decision maker’s preferences (weights) on the decision outcomes (ordering the alternatives).

Multicriteria spatial analysis is subject to uncertainty, due in part to the fact that the geographical data sets upon which it operates are the outcome of a process of discretization and generalization. The criterion map errors are referred to as the uncertainty associated with GIS data sets on the basis of which the maps have been created. The GIS database errors can be classified into the positional (or locational) and attribute (or criterion) errors. The errors can also be categorized as measurement or conceptual. The former are associated with imprecision in the measurement of criterion values, the latter attributed to the process of translating real-world entities into map objects.

In addition to the GIS data-set errors, there is uncertainty involved in the specification of decision-maker preferences. This is associated with the fact that judgment plays a key role in spatial multicirteria decision making. In some situations decision makers are not able to provide precise judgments with respect to the relative importance of evaluation criteria due to limited or imprecise information and knowledge. Also, it is common that inconsistency can be found while elucidating the decision maker’s preference.

There are basically two approaches to handle uncertainties in multicriteria decision analysis: direct and indirect methods. The former incorporates uncertainty into the multicriteria decision rules directly (e.g. see probabilistic additive weighting). Sensitivity analysis is an alternative method to the indirect incorporation of uncertainties into the decision-making process.

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