The ability of GIS to produce accurate analysis results is dependent on the accuracy and the
resolution of the data. In many instances the resolution of census enumerator tract data is too
coarse and therefore inefficient in conducting fine grained spatial analysis. Dasymetric techniques
can increase the spatial resolution of data by incorporating related high resolution ancillary data
layers allowing the primary data to be represented at finer resolutions. Areal interpolation relates
to a geostatistical process of transferring data from one set of polygons to another. This paper
proposes the application of a hybrid technique using dasymetric mapping and areal interpolation
principals to overcome the issues of transferring data from arbitrary spatial units to fit for purpose
analysis zones on demand. As a consequence the technique also overcomes the problems of coarse
scale population data as well as issues relating to the modifiable areal unit problem (MAUP). The
data used to illustrate the value and accuracy of the developed methodology is that of the 2011
census population data and ESKOM’s SPOT building count. The final outcome is an algorithm
allowing the disaggregation and aggregation of population data to any spatial unit with a high
level of accuracy.