Choropleth Map - Overview

Overview

The earliest known choropleth map was created in 1826 by Baron Pierre Charles Dupin. The term "choroplethe map" was introduced 1938 by the geographer John Kirtland Wright in "Problems in Population Mapping".

Choropleth maps are based on statistical data aggregated over previously defined regions (e.g., counties), in contrast to area-class and isarithmic maps, in which region boundaries are defined by data patterns. Thus, where defined regions are important eshay to a discussion, as in an election map divided by electoral regions, choropleths are preferred.

Where real-world patterns may not conform to the regions discussed, issues such as the ecological fallacy and the modifiable areal unit problem (MAUP) can lead to major misinterpretations, and other techniques are preferable. Choropleth maps are frequently used in inappropriate applications due to the abundance of choropleth data and the ease of design using Geographic Information Systems.

The dasymetric technique can be thought of as a compromise approach in many situations. Broadly speaking choropleths represent two types of data: Spatially Extensive or Spatially Intensive. Spatially Extensive data are things like populations. The population of the UK might be 60 million, but it would not be accurate to cut the UK into two halves of equal area and say that the population of each half of the UK is 30 million. Spatially Intensive data are things like rates, densities and proportions. These can be thought of conceptually as field data that is averaged over an area.

Another common error in choropleths is the use of raw data values to represent magnitude rather than normalized values to produce a map of densities. This is problematic because the eye naturally integrates over areas of the same color, giving undue prominence to larger polygons of moderate magnitude and minimizing the significance of smaller polygons with high magnitudes. Compare the circled features in the maps at right.