Visualizing Ten Years of Quantitative Color Schemes

Visualizing Ten Years of Quantitative Color Schemes

Travis White
University of Kansas
#nacis2015

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Nathaniel V. KELSO

October 15, 2015
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  1. None
  2. (color that represents quantitative data) Qc = Quantitative color schemes

  3. Existing research & resources indicate how Qc should be used…

  4. Existing research & resources indicate how Qc should be used…

    How are mapmakers using Qc?
  5. Qc Map Review & Evaluation Key parameters ∙2004-2013 ∙geographic journals

    from multiple fields ∙no cartographic articles
  6. Qc Map Review & Evaluation Key parameters ∙2004-2013 ∙geographic journals

    from multiple fields ∙no cartographic articles ∙at least one map selected per article ∙univariate maps only ∙no elevation maps
  7. Qc Map Review & Evaluation Key parameters ∙2004-2013 ∙geographic journals

    from multiple fields ∙no cartographic articles ∙at least one map selected per article ∙univariate maps only ∙no elevation maps 440 Qc maps from 315 articles
  8. Human Human & Physical Physical

  9. 100% 80% 60% 40% 20% 0% 2004 2005 2006 2007

    2008 2009 2010 2011 2012 2013 Qualitative color scheme rates 11% 29% 42% 71%
  10. Question 1 Which Qc schemes were used? How appropriately? By

    whom?
  11. Question 1 Which Qc schemes were used? How appropriately? By

    whom? Question 2 Which colors were used? How appropriately? By whom?
  12. Question 1 Which Qc schemes were used? How appropriately? By

    whom? Question 2 Which colors were used? How appropriately? By whom?
  13. 2 Qc mapper personas:

  14. Persona 1 ∙maps human data ∙works w/ unipolar data ∙prefers

    choropleth (enum. units) ∙uses fewer data classes 2 Qc mapper personas:
  15. Persona 1 ∙maps human data ∙works w/ unipolar data ∙prefers

    choropleth (enum. units) ∙uses fewer data classes 2 Qc mapper personas: Persona 2 ∙maps physical data ∙works equally between polarities ∙prefers surface data ∙uses more data classes
  16. Useful color assessment requires accurate color identification, but… consistent accurate

    sampling not possible with unclassed schemes: Cloetingh 2007 , Koch 2009, Fischer 2001, Salvati 2013
  17. Gimpel 2004, Combes 2005, Wahl 2012, Holand 2013 However… consistent,

    accurate sampling is possible with classed Qc
  18. Classed Qc sampling process Photoshop CC 2015 sRGB IEC61966-2.1 color

    profile RGB color image mode example: Escutia 2005 Confirm the color profile, copy legend to a new layer Edit out pixel noise
  19. Classed Qc sampling process Photoshop CC 2015 sRGB IEC61966-2.1 color

    profile RGB color image mode example: Escutia 2005 Average the edited color pixel values Point sample the averaged color values [eyedropper]
  20. DISCLAIMER Sampling & evaluations based on digital versions of maps,

    not original map files. Qc scheme appearances are inconsistent across display devices.
  21. Sequential Diverging non-spectral Spectral Traffic Other Qc Overview 5 primary

    schemes
  22. Sequential Red/Orange/Yellow ∙n = 99 (31% of all Qc maps)

    ∙primary scheme for choropleths ∙preferred scheme for human data
  23. Sequential Green/Blue/Purple ∙n = 99 (31% of all Qc maps)

    ∙primary scheme for choropleths ∙preferred scheme for human data
  24. Sequential Yellow/Orange/Brown ∙n = 99 (31% of all Qc maps)

    ∙primary scheme for choropleths ∙preferred scheme for human data
  25. Sequential Examples

  26. Sequential Examples

  27. Sequential Examples

  28. Diverging non-spectral (critical class) ∙n = 86 (27% of all

    Qc maps) ∙common on both choro & surface maps ∙more frequently used for physical data
  29. Diverging non-spectral (critical break) ∙n = 86 (27% of all

    Qc maps) ∙common on both choro & surface maps ∙more frequently used for physical data
  30. Diverging non-spectral Examples

  31. Diverging non-spectral Examples

  32. Spectral (non-diverging) ∙n = 76 (24% of all Qc maps)

    ∙overwhelmingly used on surface maps ∙overwhelmingly used for physical data
  33. Spectral (diverging) ∙n = 76 (24% of all Qc maps)

    ∙overwhelmingly used on surface maps ∙overwhelmingly used for physical data
  34. Spectral Example

  35. Spectral Example

  36. Spectral Example

  37. Traffic (diverging & non-diverging) ∙n = 26 (8% of all

    Qc maps) ∙more common on surface maps ∙used for half of all human + physical data
  38. Traffic Example

  39. Other (hypso, thermal, undefined…) ∙n = 35 (11% of all

    Qc maps) ∙no noteworthy patterns
  40. Other Example

  41. Other Example

  42. Accentuating the positive ∙Sequential & Diverging schemes used appropriately ∙Qc

    schemes rarely lost in visual hierarchy ∙Color deficiency not too problematic (ignoring Spectral & Traffic schemes)
  43. Accentuating the negative ∙Too many classes! ∙Poor color grading (cannot

    distinguish colors within a scheme) ∙Poor matching btwn data type & Qc (e.g., unipolar data + diverging scheme) ∙Illogical sequencing of colors ∙Image quality matters!
  44. Recommendations ∙Educate & Inform! mappers need instruction and guidance ∙Improve

    graphics publication guidelines ∙Improve or limit software/package offerings ∙A Brewer for non-choro mapping (?)
  45. “GOOD ENOUGH” is not good enough!

  46. Appropriate Qc usage ≠ GOOD DESIGN

  47. Thank You! Expanded slides & RGB profiles available at: References

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