The Right Colors Make Data Easier To Read

Sharon Lin And Jeffrey Heer at HBR Blog: “What is the color of money? Of love? Of the ocean? In the United States, most people respond that money is green, love is red and the ocean is blue. Many concepts evoke related colors — whether due to physical appearance, common metaphors, or cultural conventions. When colors are paired with the concepts that evoke them, we call these “semantically resonant color choices.”
Artists and designers regularly use semantically resonant colors in their work. And in the research we conducted with Julie Fortuna, Chinmay Kulkarni, and Maureen Stone, we found they can be remarkably important to data visualization.
Consider these charts of (fictional) fruit sales:

The only difference between the charts is the color assignment. The left-hand chart uses colors from a default palette. The right-hand chart has been assigned semantically resonant colors. (In this case, the assignment was computed automatically using an algorithm that analyzes the colors in relevant images retrieved from Google Image Search using queries for each data category name.)
Now, try answering some questions about the data in each of these charts. Which fruit had higher sales: blueberries or tangerines? How about peaches versus apples? Which chart do you find easier to read?…
To make effective visualization color choices, you need to take a number of factors into consideration. To name just two: All the colors need to be suitably different from one another, for instance, so that readers can tell them apart – what’s called “discriminability.” You also need to consider what the colors look like to the color blind — roughly 8% of the U.S. male population! Could the colors be distinguished from one another if they were reprinted in black and white?
One easy way to assign semantically resonant colors is to use colors from an existing color palette that has been carefully designed for visualization applications (ColorBrewer offers some options) but assign the colors to data values in a way that best matches concept color associations. This is the basis of our own algorithm, which acquires images for each concept and then analyzes them to learn concept color associations. However, keep in mind that color associations may vary across cultures. For example, in the United States and many western cultures, luck is often associated with green (four-leaf clovers), while red can be considered a color of danger. However, in China, luck is traditionally symbolized with the color red.
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Semantically resonant colors can reinforce perception of a wide range of data categories. We believe similar gains would likely be seen for other forms of visualizations like maps, scatterplots, and line charts. So when designing visualizations for presentation or analysis, consider color choice and ask yourself how well the colors resonate with the underlying data.”