In this week's reading, Robert Simmon presents foundational color theory in the context of data visualization, specifically focusing on his extensive expertise in topographical map-making. In his blog post and lecture, Simmon does a masterful job conveying concise information in an impactful way. This being my first exposure to color theory, this reading fascinated me and highlighted (pun intended) decision points regarding color that I never before considered, but are crucial to our work as data designers.
Simmon explains how color can be so powerful and expressive in displaying quantitative information, but how it can also easily mislead. A great microcosm of this interplay comes through in his lecture, when he is referencing a debate between himself and a fellow NASA visualizer who is an advocate of the rainbow palette. His coworker sticks with the rainbow palette because it does a better job at "showing detail," but Simmon's claim is that it manufacturers detail that isn't present in the data itself rather than illuminating insight inherent to the information being analyzed.
Color theory, and Simmon's orientation to this theory, rests on how we perceive color, and the relationship between colors. Simmon shows us that there is an "existing solution" around what colors we should and should not use - that our existing research largely answers the question on how we perceive color (looking at saturation, hue, and lighting), but highlights how it can be misused in visualizations. Most notably for me, how diverging or qualitative palettes can attribute unequal jumps in value to equal jumps in the data, simply due to how we interpret and perceive the color choices for those values (this is his contention around the rainbow palette I explained above). His common-sense approaches to ascribing value to colors (using colors that have cultural or logical significance to the data being examined) also adds to the breadth of his approach, and one I find no real qualms with. I will definitely be using his perspective (and palettes) in my visualizations moving forward.