The term 'dasymetric mapping' was first used by Russian geographers who described dasymetric maps as density measuring maps (Wright 1936). Dasymetric maps are similar to choropleth maps in that both types of maps represent data as stepped statistical surfaces. In other words, the data that are within a polygon are assumed to be distributed equally throughout that polygon’s area, and changes in the surface occur abruptly, and only at polygon boundaries.
The main difference between choropleth maps and dasymetric maps is the type of areal unit that is used for collecting data and representing the phenomenon of interest. In choropleth maps, data are typically represented using enumeration units (e.g., census tracts, health service areas, etc.) whose shapes may not be related to the distribution of the geographic phenomenon we are interested in mapping. For this reason, the visual impression that the map gives (i.e., that the phenomenon is evenly distributed throughout the enumeration unit) is usually incorrect. In dasymetric maps, however, the areal units that divide the space are based on the actual character of the data surface, often in combination with enumeration units (see Figure 4.cg.11,below).
By now, you might be wondering how we can create dasymetric maps if data are usually collected using unrelated enumeration units rather than areal units that reflect the nature of the data surface. To get around this problem, we can use ancillary data to create a new set of areal units that better represent the data surface. For example, land use is an ancillary data variable that is often used for creating dasymetric maps of population density. Generally, we can use two types of ancillary data variables: limiting variables and related variables. Limiting variables are attributes that can help us eliminate areas where data values could be. For example, a data layer that depicts where water bodies are located may be useful for mapping population density, as it is highly unlikely that there will be any people living in the middle of lakes or rivers. Related variables have some sort of association or predictable relationship with the data variable we are trying to map. In our population density application, an example of a related ancillary attribute might be land cover; we know that fewer people tend to live in areas that have a cropland land cover than a developed (i.e., built up) land cover, so we can require those areas to have a lower density.
: We will discuss ancillary data in more detail in the Lesson 5 Concept Gallery item called Dot Maps.
When we are creating this new set of areal units, we are basically performing what is called an areal interpolation. In other words, we are transferring quantities of our phenomenon from one set of areal units to another. One thing that we need to be careful about is that we should preserve what Tobler (1979) called the pycnophylactic property. An easy way of describing this is that if you have 100 people in a county, and you subdivide the county into a larger number of units (e.g., new units based on land cover) and redistribute the population among the new units, the sum of the population in the new units should still add up to 100 people. As Lanford and Unwin (1994, p.24) succinctly phrased it: "People are not destroyed or manufactured during the redistribution process."
Although off-the-shelf GIS software does not have built-in functionality for creating dasymetric maps, in recent years there has been renewed interest in creating automated methods for creating this type of map in both raster and vector format (e.g., Fisher and Langford (1996); Eicher (1999); Mennis (2003)).
If you are interested in investigating this subject further, I recommend the following:
- Mennis, J. 2003. "Generating surface models of population using dasymetric mapping." Professional Geographer. 55(1): 31-2.
- Tobler, W. 2001. "Pycnophylactic reallocation." CSISS..
Note that this resource discussed pycnophylactic reallocation within the context of making isoline maps rather than dasymetric maps. The principle is the same, but the nature of the way the surfaces changes (i.e., smoothly or abruptly) is what is different.