How to choose a color palette for choropleth maps
As soon as we imported the data for our Choropleth map and got to step 3: "Visualize" in the map creation process, Datawrapper lets us choose a color palette for our map:
This simple tool is more powerful than it looks. The colors we choose have a huge impact on our map: How it is perceived, how well our statement is communicated and how honest we present the data. The following article explains how to use the color palette and what the different options mean for our map design.
We will make two important decisions in the color palette tool:
1. Which color palette?
2. And how many stops?
1. Which color palette?
The first thing we'll see in the color palette tool is the option to choose other color palettes than the one that is selected by default. We can choose between four sequential color palettes (at the top) and three diverging color palettes.
Sequential color palettes are great when our numbers go from low to high, like unemployment data, life expectancy or birth dates. These palettes often have bright colors on one side and dark colors or a completely different color on the other side of the scale. Often, bright colors represent the low values while dark colors represent the high values.
Diverging color palettes can be used when both extremes and the middle point of our scale are interesting for our map. We can use it for social studies or show how our regions compare to an average value (e.g. "Countries that are more and less religious than the world average"). We can also use it for elections, especially in two-party-countries like the US: Each side of the palette can represent one party.
Changing the colors
If we just want to slightly change the color palettes, we can double-click on the color markers and change the colors directly. If we're looking for completely different color palettes, we can use the "Import" feature in the "Colors"-menu. A dialogue will open and we can paste our own color hex code strings in.
More "Colors"-menu options
There are three more options in the "Colors"-menu. If we want to use the same color scale in several maps, we can also export our color scales. Just click on "Colors" and then "Export".
In case the scale is "the wrong way round", we can reverse the color scale. That's especially useful for diverging maps: e.g. if we want to show low values with the red color of the US-Republicans and the high values with a Democratics-blue. Since Datawrapper doesn't know that, our colors might be exactly reversed when we choose the red-blue color palette. A click on "Colors" and then "Reverse" will help. There's no need to completely re-do our data.
The third option is to show only discrete colors on our color scale. By default, the color is shown as a gradient, with each value being assigned an own color. A click on "Colors" and then "Discrete" changes that, and similar values are shown on the map with the same color:
2. How many stops?
When creating a color palette, deciding on how many stops our color palette should have is the most confusing part. So what does Datawrapper mean with "Stops" and how do they work?
The number of stops is the number of (equally big) parts on our color palette which cover the same amount of our values. Let's untangle that.
Maybe you're saying "What's the problem? I have a high value and a low value. Just give the high value a dark color and the low value a bright color, and fill all counties in between in a linear way." That's what we get when we click on "Stops" and then on "min/max".
It's a good option when the distribution of data between our high and low value is very even. Often, however, we have a distribution like the following. Here we plot the number of counties in the US with a certain unemployment rate. We see that most of the counties have a pretty low unemployment rate – but there are also some outliers with a very high unemployment rate of 15% – 26%.
The Min-Max-map takes every value between the minimum and the maximum value and assigns it a color between the brightest and the darkest color in a linear way. Because of our uneven distribution and the outliers, our map looks like this then. It's a great map when we want to draw attention to the outlier counties in the US. They stand in high contrast to the rest of pretty-same-looking yellow-greenish counties. But besides that, we can't really see the geographical patterns here.
Our map would be better if more counties would be filled with the turquoise-medium-blue colors that are almost not used yet. We can achieve that with increasing the number of stops. When we choose the "min/medium/max" option, we see that a new number appears in the middle of our scale:
This "6" is the median of our values: Half of the counties have a higher unemployment rate than 6%, half of them have a lower unemployment rate.
Our map looks darker now. That's because half of the counties are filled with a color that's left of our median on the color scale, and half of the counties are filled with the colors in the right half of our color scale. In our Min/Max-map that we created earlier, only 3% of the counties were filled with the colors in the right half of our color scale. So with adding a stop, we "diversified" the colors that are used for filling the counties.
Quartiles, Quintiles, Deciles
The idea is the same when we increase the number of stops: In the option "Min/Medium/Max", our color scale was divided into two parts, with each of them covering half of the data. When we choose " Quartiles", we add two more stops, dividing the color scale and our data into four parts. "Quintiles" divides color palette and data into five parts, and "Deciles" into ten parts.
So, how many stops should we use?
It's a good idea to find a compromise between honesty and usefulness. The Min/Max-map is honest because it shows the values on a linear scale and draws immediate attention to the outliers. But maybe that's not what our article is about. Maybe we actually want to talk about the geographical pattern: The low unemployment rate in states like Texas, Kansas, and Nebraska; the Black Belt in the south of the US. To show these pattern, we'll need the the Quartiles, Quintiles or Deciles map.
The more stops we add, the more our map will use very bright color and very dark colors; increasing the contrast of the overall map. That makes it appealing to always use the maps with the most stops: It just looks more dramatic.
But it also makes our reader think that the differences are stark in areas where they're actually not stark at all and less stark in areas where they actually are very stark. To illustrate that, let's zoom into the Decile map. Nye County and Yuma County have both a similar dark blue color, but their Unemployment rates are vastly different. They are are 13.8 percentage points apart. La Plata County, on the other hand, is filled with a light green, suggesting an unemployment rate that's on a completely different level than Nye County's. But Nye County and La Plata County separate just 5.3 percentage points.
So if we have the goal to create a map on which we can point out the geographical pattern, AND we try to not imply too stark differences that are not there, we would probably go with a Quartiles map as a compromise.
In this tutorial, we looked at the color palette tool for Datawrapper Choropleth maps: Which kind of color palettes exist (diverging and sequential), how to change them, and when to use how many stops in your color palette. And we've learned that it's important to find a good compromise between drawing attention to the facts that you want to draw attention to and using the data in an honest way.