Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series

Cynthia A. Brewer , Department of Geography , Pennsylvania State University

Annals of the Association of American Geographers , 92(4), 2002, pp. 662-681

Abstract:
Our research goal was to determine which choropleth classification methods are most suitable for epidemiological rate maps. We compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps. Subjects answered a wide range of general map-reading questions that involved individual maps and comparisons among maps in a series. The questions addressed varied scales of map-reading, from individual enumeration units to regions to whole-map distributions. Quantiles and minimum boundary error classification methods were best suited for these general choropleth map-reading tasks. Natural breaks (Jenks) and a hybrid version of equal-intervals classing formed a second grouping in the results, both producing responses less than 70 percent as accurate as for quantiles. Using matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent. The advantages of careful optimization procedures in choropleth classification seem to offer no benefit over the simpler quantile method for the general map-reading tasks tested in the reported experiment. Key Words: choropleth, classification, epidemiology, maps.