This research presents a new "intelligent" dasymetric mapping technique (IDM), which combines an analyst's domain knowledge with a data-driven methodology to specify the functional relationship of the ancillary classes with the underlying statistical surface being mapped. The data-driven component of IDM employs a flexible empirical sampling approach to acquire information on the data densities of individual ancillary classes, and it uses the ratio of class densities to redistribute population to sub-source zone areas. A summary statistics table characterizing the resulting dasymetric map can be used to compare the quality of the output of different IDM parameterizations. A case study of four population variables is used to demonstrate IDM and provide a visual and quantitative error assessment comparing various IDM parameterizations with areal weighting and conventional "binary" dasymetric mapping. Intelligent dasymetric mapping outperforms areal weighting, and certain IDM parameterizations outperform binary dasymetric mapping.