Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation

Abstract

Dasymetric maps display statistical data in meaningful spatial zones. Such maps can be preferable to choropleth maps that show data by enumeration zones, because dasymetric zones more accurately represent underlying data distributions. Though dasymetric mapping has existed for well over a century, the methods for producing these maps have not been thoroughly examined. In contrast, research on areal interpolation has been more thorough and has examined methods of transferring data from one set of map zones to another, an issue that is applicable to dasymetric mapping. Inspired by this work, we tested five dasymetric mapping methods, including methods derived from work on areal interpolation. Dasymetric maps of six socio-economic variables were produced fm a study area of 159 counties in the eastern U.S. using county choropleth data and ancillary land-use data. Both polygonal (vector) and grid (raster) dasymetric methods were tested. We evaluated map accuracy using both statistical analyses and visual presentations of error. A repeated-measures analysis of variance showed that the traditional limiting variable method had significantly lower error than the other four methods. In addition, polygon methods had lower error than their grid-based counterparts, though the difference was not statistically significant. Error maps largely supported the conclusions from the statistical analysis, while also presenting patterns of error that were not obvious from the statistics.