Abstract
Interpolating population data between incompatible spatial zones is an important task in many GIS applications. This paper investigates whether regional regression models between population and land cover outperform a global approach, and whether the 3-class dasymetric method improves upon the binary dasymetric approach. In the experiments conducted, regional regressions resulted in better areal interpolation, but also highlighted spatial non-stationarity in the relationship between population and land cover. The benefits of a 3-class dasymetric model over a binary model were inconclusive. However, it is suggested that greater flexibility in model calibration to more fully incorporate spatial non-stationarity could improve 3-class dasymetric performance. Accurate urban residential density mapping is also important since the 3-class dasymetric method seems less robust than the binary approach to land cover classification error.
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