Robert Simmon, Subtleties of Color
Solving the problem of representing numbers with color, Simmon theorizes, is one that can be achieved with basic principles of color theory. I appreciated the simple but effective example of the Mars image directly translating to a paint by number, where human effort completed a task while the computation lagged behind. It illustrates the power that the psychology and biology of color are so ingrained in our human experience, it's a shame we ever manage to deceive these natural tendencies.
But aesthetic appeal, reliability, and understanding become intrinsically linked in the patterns and perceptions. Simmons highlights effectively some of these possible misperceptions and then outlines principles to guide better decision making and best practices - even if only in the most relative terms- for using color to "illuminate data".
Purpose of data visualization- any data visualization- is to illuminate data. To show patterns and relationships that are otherwise hidden in an impenetrable mass of numbers.
Color can be interpreted only through perception. Like many other readings, internal bias, biological limitations, and cultural associations can all effect how we use and interpret color, and the goal is to avoid, at the very least, the very worst of these by considering the lightness, hue and saturation of each element. Association and relativity, as well as basic judgment about known data being represented (like the example to use a scale from yellow to dark blue to show ocean depth, versus primary color scale that is typically used, and undertstanding limitations of perceptions- we can't see a dark yellow, doesn't register by our retina), all come in to play.
"Color has an objective reality, but there is no perfectly objective view of color"-
Simmons helps break down best practices with a "perfect palette" theory and some best practices for application:
a kind of spiral in color space that cycles through a variety hues while continuously increasing in lightness
Our vision system is primarily driven by lightness * hue and saturation are secondary, most important is that light is varied perceptually accurate.
Sequential data: data that that has low value to high value is best represented by alight to dark scale- navy to slate blue.
Divergent data: profit and loss- divergent palettes- our visual systems are better at picking up dark, saturated colors, so use neutral color as central points- this way you highlight outliers, and prevent any association with either for middle values.
Qualitative data: land cover, political parties (european), want colors as distinct as possible to differentiate categories with 7 +-2 as the ideal palette range.
Consider accessibility such as low vision and color blindness- avoid red/green/ brown palettes.
Consider presentation and accessibility and refer to Tufte, karen Brewer, Colin Ware.
use intuitive, semanically associated colors, matched palettes, figure-ground- use it with cultural references, as hierarchy for layering, as transition points that aren't diverging data
One useful practice is to use color to differentiate data from no data, "so before we're even aware the eye is discerning what's what."
Ben Shneiderman philosophy: overview first, zoom in filter, details on demand (for interactive data visualizations) Use color for hierarchy and layers to show relationships.
-color brewer- select palette based on data type
-chroma.js- tool that interpolates LCH space
-nasa color tool- build palette, pick hue, palette wheel
Simmon tip and final thought:
use standard design tools- grouping, careful use of line, typography, color- to make more coherent visualizations