Evaluation of methods for classifying epidemiological data on choropleth maps in series
Evaluation of methods for classifying epidemiological data on choropleth maps in series
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DOI：
10.1111/1467-8306.00310
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发表时间：
2002-12-01
期刊：
影响因子：
--
通讯作者：
Pickle, L
中科院分区：
文献类型：
--
作者：
Brewer, CA;Pickle, L
我们的研究目标是确定哪种choropleth分类方法是最适合的流行病学率地图。我们在一个两部分实验中比较了七种方法，其中包括五十六名受试者的回答，该实验涉及九个系列的美国死亡率地图。受试者回答了广泛的一般地图阅读问题，涉及个别地图和一系列地图之间的比较。这些问题涉及不同尺度的地图阅读，从单个查点单位到区域，再到整个地图分布。分位数和最小边界误差分类方法最适合这些一般choropleth地图阅读任务。自然间断（詹克斯）和等间隔分类的混合版本形成了结果中的第二组，两者产生的响应都不到分位数的70%。在一系列地图上使用匹配的图例（如果可能的话）将地图比较的准确性提高了约28%。在choropleth分类仔细优化程序的优点似乎没有提供更简单的分位数方法的一般地图阅读任务中测试的报告实验的好处。
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.