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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.

Using a hybrid methodology of dasyametric mapping and data interpolation techniques to undertake population data (dis)aggregation in South Africa