Reading #5

Subtleties of Color

By Robert Simmon

The use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not that simple, that’s often how colors are chosen in the real world. As a result, many visualizations fail to represent the underlying data as well as they could.

Read the blog series
And/or watch the lecture here

  1. Cultural Connotations of color use: Are there ways to create universal understanding using color or do the complexities of cultural connotations create make this too difficult.

  2. Experiential Color: How can color be used to provide the viewer with a phenomenological or experiential understanding of a visualization?

  3. Color as falsity: Colors can easily change how people understand data or mislead people to believe something that isn't true. How can color be used to lie about things that enhance the understanding of the data?

  1. For the National Land Cover Database’s map of “Land Cover Classes” why have any variation in shades for categories? (If “urban” is shown through four shades of red, what is the benefit to having four as opposed to one?)

  2. “Visualizations should be as easy as possible to interpret, so try to find a color scheme that matches the audience’s preconceptions and cultural associations.”
    Aside from using color to represent data that is based on physical occurrences which have a color already associated with them, is there an existing guide to what colors are associated with certain cultures?
    Is there a way graphic designers gauge an audience’s preconceptions and cultural associations?

  3. In creating intuitive palettes, do rules of lightness, as outlined in “Part 3: Different Data, Different Colors” still apply?

  4. How could the Layering technique (“Instead, use muted colors to limit the range of hues and contrast in one dataset, and then overlay additional data, such as the contour lines and shaded relief of a topographic map combined with land cover, roads, and buildings.”) be applied to a data set that is not map dependent?

  1. “The 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.”
    • Is the only explicit purpose of data visualization to reveal the meaning of an “impenetrable mass of numbers?” Are data visualizations with any sort of design bias not doing their job properly? What about data visualizations that represent a relatively small data set but shed a new light on the information or make a commentary about scale? Does that fall under the category of “impenetrable?”

  2. How do the different color spaces speak to each other / are there pros and cons to using different spaces for different media or is there one that is objectively the most useful? Is RBG really as flawed as Simmon makes it sound?

  3. Many of Simmon’s points apply only to data visualizations in which many colors are needed – is it important to use vastly differentiable colors on a smaller scale? If a designer is working with a palette of two or three colors, is it necessary to have them be evenly spread out along the spectrum, even if the colors lose relevance to the subject matter?