Areal interpolation involves the transfer of data (often socioeconomic statistics and especially population data) from one zonation of a region to another, where the two zonations are geographically incompatible. This process is inevitably imprecise and is subject to a number of possible errors depending on the assumptions inherent in the methods used. Previous analysts have had only limited information with which to compare the results of interpolation and so assess the errors. In this paper a Monte Carlo simulation method based on modifiable areal units is employed. This allows multiple interpolations of population to be conducted from a single set of source zones to numerous sets of target zones. The properties of the full error distribution associated with a particular interpolation model can then be examined. The method based on dasymetric mapping consistently gave the highest accuracy of those tested, whereas the areal weighting method gave the lowest. More important than the results presented is the potential for future testing of other methods in increasingly complex situations.
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