Foundational Reading

The papers and books the rest of the library keeps quoting, in roughly the order they are worth reading. Albers teaches you to distrust a swatch seen in isolation; Brewer and Harrower turned that…

Summary

The papers and books the rest of the library keeps quoting, in roughly the order they are worth reading. Albers teaches you to distrust a swatch seen in isolation; Brewer and Harrower turned that into schemes for maps; Borland and Taylor, Light and Bartlein, and Crameri made the case against the rainbow; Kovesi and Moreland showed how to build something better; Ottosson gave front-end color a space that behaves. The papers are short, the books reward a slow read, and each one changes what you notice.

Entries

  1. Interaction of Color: Albers’ course in seeing color relatively, in Yale’s complete digital edition with interactive plates. (source)
  2. A Field Guide to Digital Color: Color from perception through displays, print, and design — a bridge between color science and practice. (source)
  3. Color Sequences for Univariate Maps: Theory, Experiments and Principles: Experiments separating reading values from reading shape: lightness carries form, hue helps read values. (source)
  4. Color Use Guidelines for Mapping and Visualization: The chapter that sorted map schemes into sequential, diverging, and qualitative — the typology ColorBrewer uses. (source)
  5. ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps: Why ColorBrewer exists, and how its schemes were tested on real maps rather than swatches. (source)
  6. Escaping RGBland: Selecting Colors for Statistical Graphics: The case for building statistical palettes in HCL rather than RGB, and the basis of R’s colorspace package. (source)
  7. Rainbow Color Map (Still) Considered Harmful: The classic case against rainbow maps: no perceptual order, hidden structure, and invented edges. (source)
  8. How NOT to Lie with Visualization: The rainbow draws contours the data lacks; match the colormap to the task and the data’s spatial frequency. (source)
  9. The End of the Rainbow? Color Schemes for Improved Data Graphics: An early plea to earth scientists to drop rainbow maps for ColorBrewer-style schemes that CVD readers can use. (source)
  10. The Misuse of Colour in Science Communication: How rainbow and red–green maps distort scientific figures, and what to use instead. (source)
  11. Good Colour Maps: How to Design Them: The paper behind ColorCET: a test image that exposes flat spots in a colormap, and a recipe for uniform ones. (source)
  12. Diverging Color Maps for Scientific Visualization: The paper behind the cool-warm map: interpolate diverging scales in Msh so the midpoint stays neutral. (source)
  13. True Colors of Oceanography: The paper introducing cmocean: one colormap per ocean variable, chosen to match how each quantity behaves. (source)
  14. How We Designed the New Color Palettes in Tableau 10: How Tableau’s default categorical palette was rebuilt: balanced lightness, nameable hues, and a default’s tradeoffs. (source)
  15. Choosing Colors for Data Visualization: A primer: color’s first jobs are to label and group, and axes and background are part of the palette too. (source)
  16. Color Naming Models for Color Selection, Image Editing and Palette Design: A model of the names people give colors, used to test whether a palette’s swatches are nameably distinct. (source)
  17. Selecting Semantically-Resonant Colors for Data Visualization: Colors that match what a category means — blue for water, yellow for banana — make charts faster to read. (source)
  18. Modeling Color Difference for Visualization Design: How far apart two colors must be to look different depends on the mark: bars, points, and lines differ. (source)
  19. Mapping Color to Meaning in Colormap Data Visualizations: Viewers read darker as more — unless a map looks semi-transparent, and then more opaque reads as more. (source)
  20. Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps: Nine colormaps tested on distance judgments: viridis holds up at every span; single-hue blues blur small gaps. (source)
  21. A Perceptual Color Space for Image Processing: The post introducing OKLab: where CIELAB’s hue and lightness slip, and the transform that fixes them. (source)
  22. Frequently Asked Questions About Color: The Color and Gamma FAQs: luminance versus lightness, gamma, and the CIE terms color code often gets wrong. (source)
  23. Color Appearance Models: The textbook on appearance models beyond CIELAB — CIECAM02 and kin — and the viewing effects they predict. (source)
  24. Information Visualization: Perception for Design: The textbook on vision for visualization design, with chapters tying color theory to rules for coding data. (source)