With my limited experience in creating data visualizations prior to the start of this course, color has been the tool that I have most often not given enough consideration to. While I have used it to denote a value, or more often a difference in grouping or variable; this would often be overridden in favor of using color for a purely aesthetic value. Robert Simmon clearly demonstrates that these two things are not mutually exclusive, and that both elements can be important in creating an effective visualization.
His closing remarks about his views on being considered an artist really resonated with me. “It’s something I am somewhat uncomfortable with, because I feel like they think I am using my opinion, when I am really trying to represent things as accurately as possible.” I appreciate why he worries about the label of being artist and its potential connotations, but I am more inspired by the idea that he can be an artist without making artistic decisions that compromise on the best representation of the data. There are still artistic choices he is making within each of his visualizations, through which color pallet he might use, but the decision is based on careful consideration of the message that color is portraying to the reader. I also appreciate that he does not dismiss the aim to make something visually pleasing, and that it may in fact lead to a more memorable end design.
“Aesthetics matter: attractive things work better.”
Donald Norman, Emotional Design.
Simmon’s gives clear examples and honest rhetoric about why the most common systems have major shortfalls that should not be overlooked. The standout example of this for me was the rainbow palette.
Simmon’s states that when presenting on a linear scale you want the perceptual change to seem equal. Using the rainbow palette, if the color is near cyan or yellow, the change would seem much larger than if it was in the middle of the green area.
He continues with the observation that the pallete changes from dark to light to dark to light to dark again. His argument for the use of CIE Color Spaces is therefore not really an argument but an obvious choice for use in data visualizations. This is especially true when he continues to discuss how the use of CIE Color Spaces does not have hinder the consideration for both color blindness and the selecting semantically‐resonant colors.
Overall Simmons observations and examples will have a large impact on my process when considering colors in my future visualizations. My largest takeaway from the reading will be the examples, which I think I will print and display in my work space as a reminder of the huge impact the correct color choices can make.