Dasymetric map

From Wiki.GIS.com

The dasymetric map is a method of thematic mapping, which uses areal symbols to spatially classify volumetric data. The method was developed and named by Tyan-Shansky and popularised by J.K. Wright.

A dasymetric map depicts quantitative areal 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, with the latter becoming overwhelmingly popular in modern cartography and for general use outside the discipline. In contrast, 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.

Cartographers use dasymetric mapping for population density over other methods because of its ability to realistically place data over geography. Similar to choropleth maps, a dasymetric map utilizes standardized data, but places areal symbols by taking into consideration actual changing densities within the boundaries of the map. To do this, ancillary information is acquired, which means the cartographer steps statistical data according to extra information collected within the boundary. If appropriately approached it is far superior to choropleth maps in relaying statistical data within areas of interest. Dasymetric mapping corrects for error, termed 'ecological fallacy', that may occur with choropleth mapping.

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 being mapped, while zonal boundaries on choropleth maps have units established for more general purposes (e.g., states within the U.S.). The cartographer generates dasymetric zones by using ancillary information. This 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; Slocum 1999), but the wide range of dasymetric procedures have been under researched.

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. 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 the way for use of so-called critical GIS and to make dasymetric mapping techniques more easily applicable with modern technology.

"Dasymetric Mapping is a technique in which attribute data that is organized by a large or arbitrary area unit is more accurately distributed within that unit by the overlay of geographic boundaries that exclude, restrict, or confine the attribute in question. For example, a population attribute organized by census tract might be more accurately distributed by the overlay of water bodies, vacant land, and other land-use boundaries within which it is reasonable to infer that people do not live."[1]

[edit] References

• Bielecka, E., A Dasymetric population density map of Poland.
• Eicher, Cory L.; 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 <http://www.highbeam.com>.