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Mapping the Truth

MAPPING THE TRUTH map readers, scope the range breaks; map makers, understand options and tradeoffs; everybody, classification matters. here are examples of two different datasets each classified by three different, common, methods -with way different results... more on the matter QUANTILE STANDARD DEVIATION EQUAL INTERVAL unevenly spaced, evenly filled buckets evenly spaced (to a stat geek), unevenly filled buckets evenly spaced, unevenly filled buckets % MULTI-ETHNIC per county, u.s. census bureau 0% 30% REALLY SKEWED! AVERAGE AGE per county, u.s. census bureau 20 years old 58 years old SUPER NORMAL! © John Nelson, IDV Solutions Will always depict variability, even if there is very little variability in the data. Useful for comparing map elements against a baseline average (as such, you'd want a "diverging" color scheme, but whatever). Useful for "getting" the map quickly and easy, though there may not be much to get. Results in a reliably lively map but can be misleading (and the legend may seem arbitrary). Will invariably result in a visually bland map unless the data are really flatly distributed (which is rare). Tends to tease out visual variation well even with clumped data, but is bad news for bi-modal data. Consider this method if the data is Usually a poor option for social data, but works alright for environmental variables like temperature which are readily thought of in, say, chunks of Good luck explaining the legend. highly clumped but you still need to tease out visual variation. ten.

Mapping the Truth

shared by johnmnelson on Jan 13
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Depending on where you insert your range breaks, a map can tell wildly different stories. Here are examples of two example datasets mapped using three common classification methods. Check out how di...


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