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Data Visualization Principles: Color CSC444 Acknowledgments for todays lecture: Tamara Munzner, Miriah Meyer, Maureen Stone SOLUTION TO LAST WEEKS CHALLENGE https://cscheid.net/projects/d3-drills/ RECAP COLOR SPACES DEVICE DEPENDENT


  1. Data Visualization Principles: Color CSC444 Acknowledgments for today’s lecture: Tamara Munzner, Miriah Meyer, Maureen Stone

  2. SOLUTION TO LAST WEEK’S CHALLENGE https://cscheid.net/projects/d3-drills/

  3. RECAP

  4. COLOR SPACES

  5. DEVICE DEPENDENT

  6. RGB • Device-centric • What programs want, not what humans want

  7. HSV • Still device-centric

  8. HSL • Still device-centric • (supported in d3)

  9. DEVICE INDEPENDENT

  10. XYZ Color Space • “Optically linear” • CIE designed three reference spectra: X, Y, Z • Designed so that all visible colors have positive coordinates, and Y is “luminance”

  11. XYZ Color Space Let’s work through this

  12. Lab Color Space • “Perceptually uniform” • Euclidean distance corresponds, roughly, to perceptual distance ( very useful! )

  13. Polar Lab (or HCL) • “Perceptually uniform”, like Lab • Transform ab to polar coordinates: radius is Chroma, Angle is Hue • Conversion to/from RGB is complicated, but distances in HCL make sense, and it makes sense for humans • Like HSV, but good. All else being equal, think HCL first

  14. Demos http://cscheid.net/static/20120216/hsv_frame.html http://cscheid.net/static/20120216/xyz_frame.html http://cscheid.net/static/20120216/luv_frame.html http://cscheid.net/static/20120216/hcl_frame.html

  15. Let’s use consistent names in class Hue Saturation Luminance

  16. CONSEQUENCES FOR DESIGN

  17. “Get it right in black and white” –Maureen Stone

  18. If you’re going to show shape variation, do it with luminance

  19. If you’re going to show shape variation, do it with luminance

  20. If you’re going to show shape variation, do it with luminance

  21. (You can see stars better by looking away from them!)

  22. http://www.settheory.com/Glass_paper/color_motion.gif http://www.settheory.com/Glass_paper/ Kanizsa_observations.html

  23. Do not rely only on hue boundaries to depict shape

  24. Do not rely only on hue boundaries to depict shape

  25. Ware, Chapter 4

  26. Area a ff ects saturation perception Saturation a ff ects area perception

  27. Saturation a ff ects area perception

  28. Area a ff ects saturation perception Saturation a ff ects area perception Imagine the mess if you try to use both…

  29. Simultaneous contrast is a problem Quantize the plot if background is non-constant (This comes at a fidelity cost for the data)

  30. “Categorical” data • Sometimes there’s no implied relationship between di ff erent levels of a variable • Stimuli must look di ff erent, but “ only di ff erent” d3.scaleOrdinal(d3.schemeCategory10)

  31. Order these colors!

  32. You can’t… Order these colors!

  33. Order these colors!

  34. You can’t help but… Order these colors!

  35. You can’t help but… Order these colors!

  36. Be aware of implied and perceptually forced color relationships For categorical data, use color only when you have few categories (less than 10)

  37. The Dreaded Rainbow Colormap

  38. If you need going to use the rainbow colormap, use an isoluminant version, quantize it, or both Bad Better

  39. Infovis 2011

  40. Borkin et al., Infovis 2011

  41. Borkin et al., Infovis 2011 Colormap design matters very strongly

  42. COLORBREWER

  43. COLORGORICAL

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