# Take Care of your Choropleth Maps

Over the last week I had some fun playing with choropleth maps. Thereby I analyzed the following US poverty map, which was recently published at the Guardian data blog:

To be honest, the first time I saw this map I didn’t thought much about it. Ok, poverty is highest in south central of the United States, especially near Mexican border. But recently I used the same data to demonstrate a choropleth map that I created from-scratch and I was really surprised to see a somewhat different picture:

Naturally, I wanted to know where the differences come from and spent some time to investigate. Actually, I think there are two big fails in the Guardian map (which was made using Google Fusion tables).

## Don’t mess around with your class limits

The values in the poverty data range from 6.6% to 22.7% and the map shows them divided into five classes. If one would compute the exact equidistant class limits between the minimum and maximum value one would come up with the following classes (the gray bar is used to indicate the data range):

I’m not sure if this is the default behaviour of Google Fusion Tables or the editors choice, but the Guardian map used the class limits 6-9%, 9-12%, 12-15%, 15-18% and 18-23%. Due to the round numbers one might think that they are easier to understand than the fractioned numbers above, but this comes at the high price of distorted class distribution:

Note that the fifth class (which shows the poorest states) is blown up while the first class is a bit under-represented. Given the highly political topic, I’d argue that while we’re trying to map inequality, we should at least use equally distributed classes.

## Don’t mess around with your class colors

The second big failure of the map is the choice of colors. This colors were used for the Guardian map:

Obviously there’s a large jump between the first and second class and an enormous jump between the fourth and fifth color. The fourth color looks like taken from a completely different gradient and is hardly distinguishable from the third color. Again, I’m not sure if this is some kind of default in Google Fusion tables, but maybe they were just hand-picked.

Instead, in my map I simply used equidistant colors from a HSV gradient:

But, as mentioned in the comments below, even equidistant HSV colors are not the best option. The problem is that humans perception of brightness differs from the arithmetical lightness of HSV colors.

To demonstrate this difference, let’s compare the equidistant HSV colors to a hand-picked color scale from colorbrewer2.org:

Quite a different picture, isn’t it?