Intelligent Dasymetric Mapping and Its Application to Areal Interpolation

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