Intelligent dasymetric mapping and its application to areal interpolation

Abstract

ABSTRACT: 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