Escaping RGBland: Selecting Colors for Statistical Graphics. Zeileis, A., Hornik, K., & Murrell, P. 53(9):3259–3270.
Escaping RGBland: Selecting Colors for Statistical Graphics [link]Paper  doi  abstract   bibtex   
Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines) – e.g.,~as in bar plots, pie charts, mosaic displays or heatmaps – it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Based on the perceptually-based Hue-Chroma-Luminance (HCL) color space suitable color palettes are derived for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).
@article{zeileisEscapingRGBlandSelecting2009,
  title = {Escaping {{RGBland}}: Selecting Colors for Statistical Graphics},
  author = {Zeileis, Achim and Hornik, Kurt and Murrell, Paul},
  date = {2009-07},
  journaltitle = {Computational Statistics \& Data Analysis},
  volume = {53},
  pages = {3259--3270},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2008.11.033},
  url = {https://doi.org/10.1126/science.1229881},
  abstract = {Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines) -- e.g.,~as in bar plots, pie charts, mosaic displays or heatmaps -- it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Based on the perceptually-based Hue-Chroma-Luminance (HCL) color space suitable color palettes are derived for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-5094127,~to-add-doi-URL,cognitive-structure,scientific-communication,semantics,technology-mediated-communication,visual-notation,visualization},
  number = {9}
}

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