In this week's reading the topic is a quick primer on color theory. All the content is premised on a truism introduced at the beginning of the post, that "The purpose update of visualization - any data visualization- is to illuminate data. To show patterns and relationships that are otherwise hidden in an impenetrable mass of numbers."
Simmons with the rudiments of color spaces their respective deficiencies. RGB, a color space I've used most of my life, clearly had uneven brightness which (prior to this talk) I'd never noticed. HSV is somewhat better but has uneven brightness and again there is uneven coloring perceptual space given to colors like greens. Both of these can also numerically represent imperceptible colors such as dark yellow. A solution for this is the CIE color space. Specifically in this talk he Advocates CIE lch which seems functionally to be similar to HSV but with a color engine that takes into account the wetware that will do the perceiving.
Regardless of our choice of color spaces, and although CIE is very good in accounting for various visual deficiencies, it still does not speak to other issues such as our perception of gray scale (how it varies with surrounding grays) as well as red-green color blindness. This in particular was an issue I'd never considered prior to this talk, but made all the more salient when one of his colleagues admitted to not being able to decipher side-by-side spectrums (red-green vs. brown) which Simmons admitted to using professionally.
Following this, he enumerated several different use cases for various data types, introducing several clear examples for three data type categories: sequential, divergent and qualitative. Each of which are (in the majority of cases) best served by ramps, two ramps with a middle ground and several distinct colors respectively.
With these mechanics out of the way the rest of the post/talk deals with issues of palette colors and how they can serve or diminish intelligibility. Under this rubric he introduces a handful of axioms which I found quite useful as a beginner. These range from the more obvious such as using intuitive colors (I.e. blue for water) and usage of different colors for complementary data sets so that you can reduce unnecessary ink (Ex: the combined dataset where he could forgo drawing coastlines). And included some I would have been less likely to consider such as the impact of culturally associated colors.
What struck me most about this whole presentation is the degree to which the subject matter still seems to be something that professionals have to internalize as opposed to a set of best practices which covers the 99%. By way of example I showed a few of my friends the sand dunes imagery which Simmons suggested was problematic and without exception they all interpreted the data correctly.