Simmon Readings

This series of blog posts discusses, in the context of effectively visualizing data, certain aspects of color theory and patterns in the way humans perceive color. The first post discusses the differences between how computers generate color (as a combination of red, green, and blue) and how humans perceive color (with respect to lightness, hue, and saturation). In doing this, it illustrates the reasons why the RGB color space model is not intuitive for humans to use. As an alternative, it presents the idea of CIE color spaces, which correspond better with human intuition.

The second post begins discussing incorporating human perceptual patterns to create data visualizations that accurately reflect underlying data. Specifically, it notes how humans do not perceive color linearly along a rainbow (as they do on a greyscale gradient), explaining how data visualizations that map a rainbow color scheme to data overemphasize certain quantities and underemphasize others. In this same vein it notes (as the reading did last week) how human perception of color is influenced by the colors that surround it, making the same color look completely different when placed in contrasting surroundings.

The third post builds on the ideas discussed in the second post. While the second deals only with mapping color to data that is sequential in nature, the third describes what to do if the data is divergent, meaning it moves away from a central point in two different directions. The main solution here is viewing the divergent data as sequential data in two different directions, and accordingly mapping two contrasting gradients to the two sequences. The post also discusses categorical data, noting that here, rather than a gradual continuum, it is necessary to have colors that are as different from each other as possible. It notes that human perceptive ability generally limits the number of categories able to be differentiated by color to 12.

The fourth post discusses additional topics about using color to represent information, focusing mainly on aligning use of color with human intuition. It begins by mentioning that if colors are being used to represent a phenomenon that is already associated, physically or culturally, with certain colors, those existing associations should be preserved as best as possible. It also discusses issues in and techniques of displaying multiple datasets together, both with color; here, it is important to clearly differentiate the colors used between datasets (or between data and no data). The post finishes off by discussing situations where it may be necessary to break certain “rules” outlined previously, mentioning that it is always necessary to make critical judgement and respect aesthetics.

The fifth post concludes by detailing tools for practically implementing the ideas discussed in the previous posts.