Natesh Pillai points us to this recent article, “The misuse of colour in science communication,” which begins:
The accurate representation of data is essential in science communication. However, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour-vision deficiency remain prevalent in science. These include, but are not limited to, rainbow-like and red–green colour maps.
Yes, rainbow color scheme is well known to be horrible, and there are some alternatives.
I sent this to Jessica Hullman to get her thoughts, and she wrote:
I’ve never been a big color perception person, but yes, that’s generally what’s been assumed and taught in visualization research. Though in the last few years there have been a few studies that looked at this, inspired in part by how many scientists refuse to give them up (maybe there’s some utility we just haven’t thought of yet?) and found reasons to think rainbow color maps are not as awful as previously thought.
I just last week saw this one presented at IEEE VIS, which varies the task (inference—specifically how well people can tell which visualizations were produced by the same model—rather than just perceiving data values through color), and finds: Contrary to conventional guidelines, participants were more accurate when viewing colormaps that cross a variety of uniquely nameable colors. I haven’t had time to read closely, but there was some discussion about whether the argument that crossing more nameable colors is helpful can really be made if they didn’t control for the number of discriminable steps (steps for which there is a just-noticeable difference) in the color ramp.
Another one suggests they’re not bad for judging differences in gradients of scalar fields.
Some other work finds that people are consistent in how they implicitly discretize rainbow scales, but also finds some data-specific differences in implicit discretization, concluding it’s more complicated than we thought to evaluate them.
While I’m still teaching students that they are generally a bad idea, I tell students this doesn’t mean there won’t be scientific applications where they do work ok. For instance, phase diagrams where you can look to see where all the colors meet (e.g., here). In general, there would seem to be few visualization guidelines that are always true.