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A map (see map image and map) that portrays a single distribution for census tracts, counties or other areal units; presents each areal unit as homogeneous; divides the data into discrete categories; and typically describes spatial variation with graytones ordered according to a darker-means-more scheme (see figure). Although most choropleth maps depict quantitative distributions like population density, median income or the percentage rate of population growth, qualitative choropleth maps are useful for showing distributions like dominant religion or form of government.
{img src=show_image.php?name=bkhumgeofig8.gif }
choropleth map (right) user classification and gray tone area symbols to describe geographic pattern of percentage data (left)
Easily created with mapping software, the quantitative choropleth map is widely misused (Monmonier, 1993, pp. 160-2). Desktop mapping packages typically offer the choropleth map as the \'default\' display: unless a user specifies otherwise, the software generates a choropleth map, usually with five categories, each in a different colour. Lacking an easily and consistently decoded sequence, highly varied hues require frequent reference to the map key. While statistical mapping packages usually organize hues according to wavelength — as on temperature maps, which describe gradations from cold to hot with a blue-green-yellow-orange-red sequence — choropleth maps in newspapers and popular magazines might reflect the whim of an artist who prefers orange-blue-yellow-red-green. By contrast, a graphically logical light-to-dark sequence of graytones promotes convenient, reliable decoding based on the readily understood darker-means-more metaphor.
When used to portray population size and other count data, choropleth maps are potentially misleading, especially when areal units vary widely in size or population. For example, a choropleth map of population requires division by land area, so that graytones (an intensity symbol) represent population density (an intensity measure). Moreover, a choropleth map of the number of aged residents becomes a pale shadow of population because higher counts occur in comparatively populous enumeration areas. To make the mapped distribution informative, the map author must convert each area\'s count to a percentage of its total population.
The reliability of a choropleth map depends on the homogeneity of its areal units, the graphic logic of its symbols, and the class intervals that assign places to symbols (Wright, 1942). Although computer-plotted symbols that vary continuously from light to dark obviate classification (Tobler, 1973), \'unclassed\' choropleth maps are not easy to reproduce consistently or decode (Mak and Coulson, 1991).
Although choropleth maps usually describe a single distribution, bivariate choropleth maps (Olson, 1981) use a comparatively complex system of colours or patterns to describe simultaneously a pair of variables and their correlation. The key is a square array in which the rows represent categories for one variable and the columns show divisions of the other variable. Typically the cell at the upper right represents the highest category for both variables, whereas the cell at the lower left represents coincidence of lowest values. Sometimes called a cross map because of its focus on cross-correlation, or covariation, these complex maps can repay careful study by readers who understand the technique. (MM)
References Mak, K. and Coulson, M.R.C. 1991: Map-user response to computer-generated choropleth maps: comparative experiments in classification and symbolization. Cartography and Geographic Information Systems 18: 109-24. Monmonier, M. 1993: Mapping it out: expository cartography for the humanities and social sciences. Chicago: University of Chicago Press. Olson, J.M. 1981: Spectrally encoded two-variable maps. Annals of the Association of American Geographers 71: 259-76. Tobler, W. 1973: Choropleth maps without class intervals? Geographical Analysis 5: 262-5. Wright, J.K. 1942: Map makers are human: comments on the subjective in maps. Geographical Review 32: 527-44. |
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