Patrick Serr
Reading #5 - 10/25/17
———————
Through a series of blog posts under the heading “Subtleties of Color,” Roberts Simmons examines the ways in which human perception and cognition should affect the choices made by designers when creating data visualizations. His points are rooted in the study of numerous texts by thinkers across disciplines (as outlined in the sixth and final segment of the series) as well as scientific papers and working models proposed by professional organizations (primarily cartographers and NASA scientists). His language is clear enough to be understood by the layperson, while the visual examples he provides, along with specific step-by-step instructions for using tools, provides a level of richness suitable for students and professionals to glean novel insights.
“Subtleties of Color” is broken into six sections: an introduction to color theory and the HCL color model which more is closely attuned to human perception, three sections describing color strategies appropriate to different types of data sets, and finally a wrap-up of useful tools and his sources.
The middle three sections — “The ‘Perfect’ Palette,” “Different Data, Different Colors,” and “Connecting Color to Meaning” — all cover new some new theoretical ground compared to previous class readings, and are worth close examination. Simmons’ straightforwardness regarding visual strategies is refreshing and provides the aspiring designer with helpful rules of thumb. Understanding that degrees of lightness are more easily perceived by the eye explains his model for palettes: combine a linear, proportional change in lightness with a simultaneous change in hue and saturation. His selection of visual examples reinforces the point while sparking inspiration for the number of ways in which this might be contextually applied to different data sets. By going on to explain the difference between color usage in sequential data sets versus what he terms “divergent” and “qualitative” (or “categorical”) data sets makes both intuitive sense and is satisfying to see in practice.
I especially appreciated his acknowledgement in Part 4 of the ways in which theoretical models and color rules run into real-life data to produce unexpected results. His discussion of a topographical map with a “heat map” palette from red to light yellow illuminates how even a perfectly logical set of color choices may require tweaking based on the figure-ground relationships created by specific visualizations.
Simmons’ discussion of the role aesthetics, while understandably vague, stand as the one point at which he diverges tonally from the pragmatic tone of the rest of his writing. His advice — “I can only encourage you to keep your eyes open” — is accurate but leaves something to be desired. Where other authors referenced in our course reading have taken a stab at identifying some of the aspects of what separates beautiful or tasteful visualizations from the mundane or mediocre, Simmons seems content to move on. Perhaps he would better serve the reader by either omitting discussion of aesthetics altogether or dedicating an entire segment to a more developed discussion.