[5][6] The term "dasymetric" was coined in 1911 by Benjamin Semyonov-Tian-Shansky, who first fully developed and documented the technique, defining them as maps "on which population density, irrespective of any administrative boundaries, is shown as it is distributed in reality, i.e. by natural spots of concentration and rarefaction.
[11] The dasymetric technique starts with a chosen variable aggregated over predetermined geographical districts as in a choropleth map.
The most common type of ancillary data for this is land cover, reclassified into ordinal degrees of human inhabitation from uninhabited wilderness to urban development.
[3][12] Another option is cadastral data, including small-scale administrative areas (e.g., national parks, wilderness reserves) or large-scale parcels.
If the variable being mapped is area-dependent (such as population density), the values need to be recalculated according to the areas of the refined districts.
[14] Several techniques have been developed that attempt a more sophisticated interpolation, using the ancillary data to reallocate individuals (and thus aggregate totals) between areas believed to be more and less dense, similar to Tian-Shansky's original method.
was done in a common sense way, but modern automated methods use statistical analysis to estimate a "best fit" of the choropleth data to the ancillary zones.
Because the dots are usually randomly placed, they can give an impression of internal homogeneity almost as strong as the constant color of the choropleth map.
[10] Because it does not directly incorporate ancillary information, some consider it to not technically be a form of dasymetric mapping, but a related "areal interpolation" technique.