In our Data Visualization 101 series, we cover each chart type to help you sharpen your data visualization skills.
For a general data refresher, start here.
For quickly visualizing variables across a region of the world, there are few better tools than the heat map.
What It Is
A heat map (or choropleth map) is a thematic map in which the area inside recognized boundaries—such as states, counties, or territories—is shaded in proportion to the data being represented. They can be used to represent population density, diversity, average temperatures, per-capita income, social views, and many other geographically important measurements.
Heat maps are often used to display election results. This map from the 2008 U.S. presidential election shows county-by-county results, shaded by percentage won. Barack Obama is in blue; John McCain is in red.
Where They Came From
Many factors needed to come together before the world was ready for the heat map, and that included modern cartography. Not until accurate mapping was commonplace—and enough data was available from those distant places—did the heat map become feasible.
French mathematician, engineer, economist, and politician Charles Dupin (1784–1873) was probably the first to use the heat map in 1826. His map, shaded in black and white, showed the distribution of illiteracy in France.
When to Use It
Heat maps are perfect for representing data where a defined boundary is relevant to the data. In the 2008 U.S. election map above, for example, each county has a measurable say in the election results, which is ultimately defined by that boundary.
The map below, however, illustrates an important detail, which could be relevant to your data.
The map visualizes the population density for counties of the British Isles. While it is good for an approximation of density, it is important to realize that the shading detailed here is an average across each county. In some cases, this will greatly misrepresent actual population density on the ground. (For example, areas directly surrounding Dublin, Ireland, are sparsely populated, but the dense population at the city center makes the entire county appear full.)
Best Practices for Designing Heat Maps
Now that you have a basic understanding of heat maps, let’s look at 4 tips to get the most out of using them.
1. Use a simple map outline.
These lines are meant to frame the data, not distract.
2. Select colors appropriately.
Some colors stand out more than others, giving unnecessary weight to that data. Instead, use a single color with varying shade or a spectrum between two analogous colors to show intensity. (An important consideration here is selecting colors and contrasts visible to individuals with color blindness.) Also remember to intuitively code color intensity according to values.
3. Use patterns sparingly.
A pattern overlay that indicates a second variable is acceptable, but using multiple is overwhelming and distracting.
4. Choose appropriate data ranges.
Select 3-5 numerical ranges that enable fairly even distribution of data between them. Use +/- signs to extend high and low ranges.