GEOG 486
Cartography and Visualization

Choropleth Maps

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Choropleth maps are probably the most commonly created type of map in GIS cartography. Their popularity is due to two main reasons: (1) choropleth mapping capabilities are implemented in most every GIS software package; and (2) much of the data that geographers and GIScientists work with is collected and aggregated into enumeration units, which form the basis for choropleth maps (see Figure 4.cg.3,below). Recall that choropleth maps give the map reader the impression that the phenomenon of interest is continuous (i.e., present throughout the areal unit) and abruptly changing (i.e., that the phenomenon is present at the same intensity throughout each areal unit, but changes abruptly at the area's borders).

The first choropleth map, a map of France, created in 1826 by C. Dupin.
Figure 4.cg.3 The first choropleth map, created by Charles Dupin in 1826 to depict literacy by department in France (Robinson 1982). Departments are one of the main administrative districts in France.
Credit: Friendly, 2004

An enumeration unit is an area defined for a particular purpose (often other than collecting data) and within which data are collected and aggregated. Some common examples of enumeration units include school districts (created to help manage the assignment of students to particular schools within a city or metropolitan area), counties (created as a form of local government) or census tracts (created to help manage the complicated task of counting the population). Typically, the boundaries of enumeration units do not correspond to breaks in the statistical surface of the data that are collected and aggregated to each unit (e.g., the population density does not suddenly change when we cross the border from Los Angeles county to Orange county in southern California). However, there are some cases where enumeration units do provide a good reflection of the structure in the statistical surface (e.g., in the case of income tax rates, which do change abruptly from state to state).

Choropleth maps typically use either differences in color value (sometimes in combination with hue) or differences in spacing (e.g., the intensity of a hatched pattern) to represent differences in the phenomenon being mapped (see Figure 4.cg.4,below). Generally, we use a darker or more closely spaced pattern to represent larger quantities of the phenomenon and a lighter or more sparsely spaced pattern to represent smaller quantities. One empirical study has shown that in most cases (especially with light map backgrounds), map readers do assume that "dark means more and light means less" (McGranaghan 1993).

Two different visual representations of the same data.
Figure 4.cg.4 Here, we map the same data variable with two different visual variables. Which variable do you think is more effective for showing varying intensities of motor vehicle accident mortality?

Although choropleth maps are quite easy to create, there are several issues that you should be aware of and consider when you are thinking about using a choropleth symbolization for representing the phenomenon you would like to map:

Enumeration Units
One important issue is that the size of enumeration units can be quite variable. This issue is important because larger symbols will dominate the visual appearance of the map and can exaggerate the importance of particular enumeration units. If we chose to map raw counts with a choropleth map, you may see counties such as San Bernardino county (the largest county by area in California) dominate the map, as the county has a relatively large population along with a relatively large area. However, if what we are really interested in is investigating the locations where people are more likely to die as the result of a motor vehicle accident, we should really be looking at rates, as it makes sense that anywhere there is a larger population, you would probably find a larger number of deaths due to motor vehicle accidents, as there are typically a larger number of any count pertaining to people in areas with larger populations. For this reason, in choropleth maps, we typically want to avoid mapping raw counts, but instead transform the data so that we are mapping densities, rates or ratios that allow us to make more realistic comparisons between unevenly sized units. This is not to say that there is never a good reason for mapping raw counts, just that other symbolization methods may be more appropriate (e.g., graduated or proportional symbols).

A comparison of two maps to show that rates or ratios, instead of raw counts, allow for more realistic comparisons between unevenly sized units.
Figure 4.cg.5 The map at the left shows the count of motor vehicle deaths by county in California. As we would expect, the larger numbers of deaths occur in the more populous counties of the Los Angeles, San Francisco and San Diego metropolitan areas. The map of rates at the right shows a very different picture of risk of dying in a motor vehicle accident: the highest rates are in non-metropolitan California.

Modifiable Areal Unit Problem
Because of the arbitrary nature of the boundaries of most enumeration units, we can find ourselves facing the modifiable areal unit problem (MAUP) (Openshaw 1984). Simply put, MAUP arises when different aggregations of individual counts (i.e., drawing the boundaries of enumeration units in different ways) produce different spatial patterns (see Figure 4.cg.6, below). Although there is no 'solution' to MAUP, if we can find data at different scales and that are aggregated to different units, we can create multiple maps that tell a more complete story about the distribution of the phenomenon we are working with. We might also choose to use other types of symbolization, e.g., dot maps (see Lesson 5) or dasymetric maps (see concept below in this concept gallery), that can tell us more about the spatial distribution of our phenomenon of interest. Incidentally, using multiple representations (whether using the same symbolization method or different methods) can also help us better understand where we have mismatches between the breaks in the statistical surface and breaks in the geographical surface.

A set of three maps to show that different aggregations of count data can affect the appearance of a choropleth map created from the aggregated data.
Figure 4.cg.6 The three maps above show how different aggregations of count data (e.g., using three different types of enumeration units) can affect the appearance of a choropleth map created from the aggregated data. All three maps use the same color scheme and classification, but the aggregation units contain different counts of the phenomenon. As you see, the effects of MAUP can significantly alter the appearance of the final map.

Data Classification and Map Appearance
Finally, there is the issue of the effect of different data classification decisions (e.g., classification method or the number of classes employed) on the appearance of the map pattern. We discuss this issue in detail in the next section of this concept gallery below, but we should briefly discuss the potential for unclassed choropleth maps. Although there has been some discussion among cartographers about using color values that are proportional to the data values represented in the map (i.e., creating unclassed maps as Tobler suggested in (1973)), the consensus today among cartographers is that it is difficult for map readers to extract quantitative information from static unclassed color value maps, so most cartographers still prefer to classify their data, especially in maps where map readers may need to extract an individual value or compare regions. A final point to note is that in classed choropleth maps, it is important to ensure that symbols are visually differentiable from each other (i.e., that the value differences between symbols are large enough to avoid confusion). This should be evaluated within the context of the map, as simultaneous contrast (the effect of surrounding symbols on the appearance of an interior symbol) can change a symbol's differentiability.

Recommended Readings

If you are interested in investigating this subject further, I recommend the following:

  • Crampton, J. 2004. "Are choropleth maps good for geography?" GeoWorld, Jan. 2003, p. 58.
  • McGranaphan, M. 1993. "A cartographic view of spatial data quality. Cartographica, 30(2): 8-19.
  • Herzog, A.P. 2003. "MAPresso Java Applet"..

    You can check out an unclassed choropleth map using the MAPresso applet created by Adrian Herzog. Note that it might take some time for the applet to completely download, so be patient.