Evaluation of methods for classifying epidemiological data on choropleth maps in series

Cynthia A. Brewer, Linda Pickle

Research output: Contribution to journalReview articlepeer-review

245 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)662-681
Number of pages20
JournalAnnals of the Association of American Geographers
Issue number4
StatePublished - Dec 2002

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Earth-Surface Processes


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