Divide and Color What do each of these thematic maps have in common? They are all mapping the exact same data. What makes them so different? They are using different classification methods. One of the first things that pops up when whipping up a thematic data visualization that has discrete range brakes (commonly a choropleth map, but not necessarily) is how do I bucketize my numbers? Picking range breaks to drive your color categorization can range anywhere from arbitrary and predefined to rigorously statistical and dynamic, and the various options will generate very different looking results.
Some Options There are lots of ways to carve datasets into discrete classes. I’ll go over three of them...
- Quantile Breaks the data into equally filled groups
- Standard Deviation Breaks the data into statistical chunks diverging from the mean
- Equal Interval Breaks the data into equally distant groups
Distribution When choosing a method of classifying your dataset into discrete ranges, there a couple of things to consider off the bat. First, what does the data distribution look like (if it is dynamic, what does it generally look like?)? Is it skewed toward one extreme or the other? Is it relatively normal (bell shaped on a histogram)? Are there outliers to consider? Applying various classification methods can create very different impressions of the data. Any interface is a manifestation of tradeoffs, let’s take a look at some examples...
Does the "Quantile" map below tell the truth? Yes. I can clearly see the locations of higher and lower proportions of multi-ethnic US residents, even regional trends and abrupt shifts. Does the "Equal Interval" map tell the truth? Yes. I get a clear indication that most places in the US are, proportionally, pretty low in multi-ethnic residents.
The gist: Evenly spaced, unevenly filled buckets.
Quantile for Normal data
Quantile yields a pretty high-contrast map, that is reliably good looking. The fact that the data is normally distributed doesn’t really matter –each bucket has the exact same number of counties, but you’ll notice that in order to accommodate that, the ranges have to span varying distances. The gist: Unevenly spaced, evenly filled buckets.
Standard Deviation for Normal data
Trusty old Standard Deviation. It is going to look alright in most cases, but it really shines when applied to normal datasets. You’ve got the mean in the middle and you chunk it out from there based on standard deviation distances in either direction from the mean. Beautiful. Also, don’t do what I did –you should put actual values in your legend instead of the math nerd standard deviation breaks. And while we’re at it, it’s often a good idea to pick a diverging color scheme for data that is classified by Standard Deviation. Pick a neutral color for the mean (center) range and then transition to one color on the left and another color on the right. ColorBrewer gives some nice background here along with a rocking tool to generate your own cartographic color schemes. The gist: Evenly spaced (to a statistician), unevenly filled buckets.
Equal Interval for Skewed data
Equal Interval falls apart pretty easily. If the data is remotely skewed then it’s feast or famine for the evenly spaced color buckets. In this case most of the buckets are largely empty while the low end bucket (0% – 6% multi-ethnic) is jam packed. Equal Interval is more fair to the population as a whole but does not capture smaller scale fluctuations. To be fair, just because all the eggs are in one basket and the map is largely monochromatic doesn’t mean that it’s useless. You could argue that is a a perfectly fair treatment of the data because it illustrates the predominant characteristic of the data: it’s highly skewed to the lower end. The gist: Evenly spaced, unevenly filled buckets.
Quantile for Skewed data
Quantile to the rescue. When buckets are defined by an equal number of member counties, Now Devil’s Advocate. It could be argued that this method implies a false or misleading heterogeneity if the data. While the vast number of counties have a proportionally tiny multi-ethnic population, this method could imply a greater variance (as compared to the Equal Interval example above). It’s just not fair. Devil’s Advocate, Advocate. How could you get any more fair than groups of equal size? Plus the result illustrates a finer articulation of the variance. Just remember, when reading a map, read those legends and take the range breaks for what they are worth. Quantile is a good illustration of that. The gist: Unevenly spaced, evenly filled buckets.
Standard Deviation for Skewed data
Standard Deviation. It is still providing valuable visual breaks when applied to highly skewed data. But I can never get too cozy with it because it is so darn hard to explain. The gist: Evenly spaced (to a statistician), unevenly filled buckets.
- Determine overall population range (highest value – lowest value) for the value of interest…
- Determine range break distance (population range / desired # of breaks)…
- Insert break every Nth value.