Dasymetric map

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A dasymetric map is a method of thematic mapping in which a choropleth map is refined by incorporating additional geographic information. In a dasymetric map, boundaries are modified to conform to known areas of homogeneity and is not restricted to administrative or statistical boundaries. [1] Choropleth maps have a number of inherent issues due to the district boundaries being arbitrary with respect to the data variable, so that nothing is known about the variation within each district (leading to the Ecological fallacy), and the boundaries affect the apparent spatial pattern (the Modifiable areal unit problem). The dasymetric technique uses geographic information about the distribution of the phenomenon of interest to refine the district boundaries so they better reflect the real-world patterns. The resultant map is somewhere between a choropleth map and an Isarithmic map. The method was developed in 1911 and named by Tyan-Shansky and popularized by J.K. Wright.


A dasymetric map depicts quantitative aerial data using boundaries that divide the mapped area into zones of relative sameness with the purpose of best portraying the underlying statistical surface. The dasymetric map was conceived as a type of thematic map during the early to mid-nineteenth century. During their early development, the demand for both dasymetric and choropleth maps was driven by interest in population mapping. By 1900, dasymetric and choropleth mapping methods became more clearly differentiated. Choropleth maps became overwhelmingly popular in modern cartography and for general use outside the discipline. Dasymetric mapping has remained relatively unknown, even to most geographers. Consistent with their original purpose, dasymetric maps of population are still the most common type found today.[2]

Transformation of socioeconomic data from census tracts to residential areas with dasymetric mapping. The numbers in the figure (i.e. 10, 20, 30 and 40) refer to the numbers of dwellings in each of the four zones.


Dasymetric mapping involves modifying existing choropleth map data using ancillary GIS data. The following procedure is typically followed:[3]

  1. Collect data suitable for a choropleth map, including district polygons and statistical data aggregated into those districts. For example, the variable of interest could be the "percent of farmland planted in oats."
A land cover map of Vienna, Austria that could be used as the ancillary data for a dasymetric map. The red represents urban areas, green represents parks and natural areas, and the blue represents water covered areas.
  1. Obtain ancillary GIS data that maps the distribution of the phenomenon being visualized. In this example, the ancillary data might be land use/land cover data (derived from remote sensing imagery or cadastral data).
  2. Query the ancillary GIS data to select only those regions in which the variable of interest is relevant. In this example, one would select out the farmland and eliminate other land use regions.
  3. Transform the selected regions into a single boolean relevancy region, using tools appropriate for the nature of the ancillary data (e.g., if it is a vector polygon layer, use Dissolve.)
A comparison of a choropleth map and a dasymetric map. Notice the difference which accounts for the population that doesn't live in the Danube or surrounding green areas. This changes the layout of the map representing population distribution.[4]
  1. Use the Intersect or Clip overlay function to trim the choropleth districts to the boundaries of the relevancy region, recalculating spatial properties such as total area.
  2. If the variable is area-dependent (i.e., a density), recalculate based on the new area.

The basic procedure is sometimes extended, depending on the level of information in the ancillary data. For example, if the variable of interest is population density (probably the most common variable used in dasymetric mapping), and the ancillary information includes not only a distinction of inhabited/not inhabited but information suggesting different densities (i.e., urban, suburban, rural), then the cartographer can redistribute the reported total population of each district into the different zones to create several regions of estimated density within each district, rather than just one.

Dasymetric Versus Choropleth

Although dasymetric maps are closely related to choropleth maps, they differ in several ways. First, zonal boundaries on dasymetric maps are based on sharp changes in the statistical surface, obtained via ancillary data (see above), while zonal boundaries on choropleth maps have units established for more general purposes (e.g., states within the U.S.). Ancillary information can be both objective and subjective, depending on other available data and the cartographer's knowledge of the area. Second, individual dasymetric zones are developed to be internally homogeneous. In contrast, choropleth zones are not defined based on the data and, thus, have varying levels of internal homogeneity. Third, choropleth mapping methods have become standardized (including the development of common classification schemes, but the wide range of dasymetric procedures have been under researched.[citation needed]

Dasymetric Versus Isarithmic

Dasymetric and isarithmic maps can display the same kinds of variables, in fact, it could be said that a dasymetric map is an attempt to modify a choropleth map to be more like an isarithmic map. However, there are some key differences. Most importantly, Each region in a dasymetric map, like a choropleth, is a summary of the variable over the entire region, and no information is given on the internal variation of the variable (thus leading to the ecological fallacy and modifiable areal unit problem). While the modification of the boundaries should lead to a reduction of internal variation (i.e., the regions are more homogeneous), one cannot guarantee that values within the region fall within a prescribed range, as one can in an isarithmic map.


Like other forms of thematic mapping, the dasymetric method was created and historically used because of the need for accurate visualization methods of population data. An example would be a map depicting population density from census information, while taking into account geographical features such as lakes or parks--places in which people do not live.[5] Dasymetric maps are not widely used because of the limited options for producing them with automated tools such as geographic information systems. Although fields such as public health still rely on choropleth maps, dasymetric maps are becoming more prevalent in developing fields such as conservation and sustainable development. Researchers in various fields of science are pushing for the use of so-called critical GIS and are making dasymetric mapping techniques more easily applicable with modern technology. Dasymetric mapping has long been favored by earth scientists such as geologists and ecologists.[citation needed]

See Also

Further Reading


  1. Tyner, Judith. Principles of Map Design. Guilford, 2014.
  2. Eicher, Cory L. and Cynthia Brewer, "Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation." Cartography and Geographic Information Science. Cartography and Geographic Information Society, Inc. 2001. HighBeam Research. 31 Oct. 2011 [1]
  3. Slocum et al. (2009), 272
  4. Graser, Anita. "Improving Population Density Maps Using Dasymetric Mapping." [2]
  5. "Dasymetric Mapping." GIS Dictionary.[3]