A series of chart makeovers based on visualization research.

1. Design and Support Recommendations
from Data Visualization Research
July 17, 2014
Science Boot Camp Southeast
Raleigh, NC
#BootCampSE14
Angela Zoss, @duke_vis
Data Visualization Coordinator
Duke University Libraries

2. Using research to inform design
Expert suggestions/heuristics
http://bit.ly/tufte_vdqi http://bit.ly/bertin_sg http://bit.ly/few_smn

3. Using research to inform design
Empirical verification and elaboration
http://bit.ly/cleveland_egd http://bit.ly/ware_iv http://bit.ly/wilk_gg

4. Demo data for today
http://bit.ly/bcse_zoss

5. POSITION IS EVERYTHING.
@moritz_stefaner

6. The research shows…
• Rotated text is harder to read
Byrne, M. D. (2002). Reading vertical text:
Rotated vs. marquee. In Proceedings of the Human
Factors and Ergonomics Society 46th Annual Meeting;
Santa Monica,CA. Human Factors and
Ergonomics Society: 1633–1635.
http://dx.doi.org/10.1177/154193120204601722

7. The research shows…
• Rotated text is harder to read
Byrne, M. D. (2002). Reading vertical text:
Rotated vs. marquee. In Proceedings of the Human
Factors and Ergonomics Society 46th Annual Meeting;
Santa Monica,CA. Human Factors and
Ergonomics Society: 1633–1635.
http://dx.doi.org/10.1177/154193120204601722

8. Text orientation
State population from 2010 census
http://en.wikipedia.org/wiki/US_states_by_population
Easy in Excel

9. Text orientation
State population from 2010 census
http://en.wikipedia.org/wiki/US_states_by_population
Easy in Excel

10. Text orientation
Easy in Excel

11. The research shows…
• Rotated text is harder to read
• People are very good at reading bar length,
x/y position
Vernier acuity:The ability to see if two line segments are
collinear.Accurate to about 10 seconds of arc or 1/10 of a pixel.
Ware, C. (2013). Information visualization:perception for design,
third edition.Waltham, MA: Morgan Kaufmann Publishers.
http://www.sciencedirect.com/science/book/9780123814647

12. Perception of length/area
Easy in Excel

13. Perception of length/area
Easy in Excel

14. Perception of length/area
Easy in Excel

15. Perception of length/area
Advanced in Excel
http://peltiertech.com/Excel/Charts/DotPlot.html

16. The research shows…
• Rotated text is harder to read
• People are very good at reading bar length,
x/y position
• But even our positional acuity is no match for
high data density

17. Data density
State population vs. Number of seats in U.S. House
http://en.wikipedia.org/wiki/US_states_by_population
Easy in Excel

18. Data density
State population vs. Number of seats in U.S. House
http://en.wikipedia.org/wiki/US_states_by_population
?
Easy in Excel

19. Data density
Fill transparency at 100%
Easy in Excel

20. Data density
Fill transparency at 50%
Easy in Excel

21. Data density
Both axes converted to logarithmic scale
Easy in Excel

22. Data density
Ranges edited, vertical axis switched to base 2
Easy in Excel

23. Data density
Population mapped to horizontal axis,
seats in house mapped to size,
random “jitter” added as vertical axis values
Intermediate in Excel

24. The research shows…
• Rotated text is harder to read
• People are very good at
reading bar length,
x/y position
• But even our positional acuity
is no match for high data
density
• People are not as good at
differentiating angles, areas
Cleveland,W. S., & McGill, R. (1985).
Graphical perception and graphical
methods for analyzing scientific data.
Science,299(4716), 828-833.
http://dx.doi.org/10.1126/science.229.4716.828

25. Angles, wedges, circles
People are bad at comparing areas of shapes or judging certain relationships.
If precision is important or data values are very similar, use bars or scatter plots.
http://de.slideshare.net/vis4/making-
data-visualizations-a-survival-guide/25
http://de.slideshare.net/vis4/making-
data-visualizations-a-survival-guide/162
http://www.leancrew.com/all-this/2011/11/
i-hate-stacked-area-charts/

26. Visual math
http://eagereyes.org/criticism/visual-math-wrong
If the chart makes it hard to understand an important relationship between
variables, do the extra calculation and visualize that as well.
http://bit.ly/SFeAwzhttp://bit.ly/PszKw0

27. Avoid special effects
http://bit.ly/3dpiebad

28. COLOR IS DIFFICULT.
@moritz_stefaner

29. The research shows…
• People have trouble
differentiating between
more than 5-7 hues
?
?
http://colorbrewer2.org/
Healey, C. G. (1996). Choosing effective colours for
data visualization. In R.Yagel and G. M. Nielson (Eds.),
Proceedings of the 7th conference onVisualization '96
(VIS '96), 263-270.
http://dx.doi.org/10.1109/VISUAL.1996.568118

30. Qualitative color classes
With categorical/qualitative variables (e.g, states, genders,
political parties), use at most 5-7 hues.
States by population and house seats, colored by region
Requires 4 separate data series
Intermediate in Excel

31. The research shows…
• People have trouble
differentiating between
more than 5-7 hues
• People have trouble
differentiating between
more than 5-7 shades
http://colorbrewer2.org/
Gilmartin, P. and E. Shelton. (1990). Choropleth maps on
high resolution CRTs:The effects of number of classes and
hue on communication. Cartographica,26(2), 40-52.
http://dx.doi.org/10.3138/W836-5K13-1432-4480

32. Ordered color classes
With classed/graduated variables (e.g., rating scores, age groups,
any number that has been split into groups), use at most 5-7 shades.
States by population and house seats, colored in 4 x-axis value groups
Requires 4 separate data series
Intermediate in Excel

33. Continuous color
With continuous variables (e.g., population, rainfall, timestamp),
the only option in Excel is conditional formatting of cells.
https://www.census.gov/hhes/migration/data/acs/state-to-state.html
Migrations between states, colored continuously
Easy in Excel

34. Continuous color
Depending on your data, a continues gradient might group too many
elements into a small range of lightness. You can also transform the
data (e.g., log) or create discrete classes for more control.
Migrations between states, colored in 4 classes
Requires conditional formatting “classic” rules for value ranges
Intermediate in Excel

35. The research shows…
• People have trouble differentiating between more
than 5-7 hues
• People have trouble differentiating between more
than 5-7 shades
• Rainbow color gradients are very problematic

36. “Rainbow Color Map (Still)
Considered Harmful”
Borland, D., &Taylor, R.M. (2007). Rainbow
color map (still) considered harmful. IEEE
Computer Graphics and Applications,27(2), 14-17.
http://dx.doi.org/10.1109/MCG.2007.323435

37. Perceptual ordering.
(a) We can easily place the gray paint chips in order
based on perception,
(b) but cannot do this with the colored chips.
Borland & Taylor (2007)
Confusing

38. Spatial contrast sensitivity function.
We can see detail at much lower contrast in the
(a) luminance-varying gray-scale image than with the
(b) rainbow color map.
Borland & Taylor (2007)
Obscuring

39. Four data sets visualized with
(a) rainbow,
(b) gray-scale,
(c) black-body radiation
Apparent sharp gradients in the
data in (a) are revealed as rainbow
color map artifacts...
The sharp gradient found at the
center of the second data set... is
not found in the corresponding
image with the rainbow color map.
Borland & Taylor (2007)
Actively misleading

40. Related: Salience
Rainbows also cause salience problems; some colors in the inner
part of the rainbow “pop out” more than colors at the extremes.
http://dx.doi.org/10.1038/nmeth.1762 http://mycarta.wordpress.com/
2012/12/21/comparing-color-palettes/

41. Instead of rainbows…
Solution: Use a single hue, varying luminance
If you want color to show a numerical value, use
a range that goes from white to a highly saturated
color in one of the universal color categories.
http://shar.es/CfbSd
http://www.flickr.com/photos/sadrzy/4154089647/

42. ColorBrewer
Cynthia Brewer, PhD – Penn State
http://colorbrewer2.org/

43. The research shows…
• People have trouble differentiating between more
than 5-7 hues
• People have trouble differentiating between more
than 5-7 shades
• Rainbow color gradients are very problematic
• For highest contrast, limit colors and vary luminance

44. Color versus position
Do you really need color at all?
Intermediate in Excel

45. Color versus position
Do you really need color at all?
Intermediate in Excel

46. Color versus position
Do you really need color at all?
Easy* in Excel
*Creating charts with manually-selected y values is easy and very useful.
Adding labels, though, can be harder. Sometimes it’s easiest to add them manually.

47. Visual contrast
If you use color sparingly,
you can save it for contrast.
Intermediate
in Excel

48. Visual contrast
Contrast is important to direct attention and improve clarity.
It can also shield against projection/printing issues or color
interference effects.
http://shar.es/CWktB

49. Times to use color

50. Times to use color
• When position is insufficient (lines crossing)
as opposed to:
http://vis4.net/blog/posts/doing-the-line-charts-right/

51. Times to use color
• When position is insufficient (lines crossing)
• When it will aid pre-attentive processing
Count the 4s.
173658103837575063348181736401016254
539319123938525616173943987139874619
319586716628309897273164613984019358
094285976205897629835921873589321759
871059283198254781237598698127359812!

52. Times to use color
• When position is insufficient (lines crossing)
• When it will aid pre-attentive processing
173658103837575063348181736401016254
539319123938525616173943987139874619
319586716628309897273164613984019358
094285976205897629835921873589321759
871059283198254781237598698127359812!
Count the 4s.

53. Times to use color
• When position is insufficient (lines crossing)
• When it will aid pre-attentive processing
• When it reinforces semantic content
Lin, S., Fortuna, J., et al. (2013). Selecting semantically-resonant
colors for data visualization. In B. Preim, P. Rheingans, & H.Theisel,
Proceeedings of Eurographics Conference onVisualization (EuroVis) 2013.
http://idl.cs.washington.edu/papers/semantically-resonant-colors/
http://www.babynamewizard.com/voyager

54. Times to use color
• When position is insufficient (lines crossing)
• When it will aid pre-attentive processing
• When it reinforces semantic content
• When you want to encourage comparisons over
multiple figures
http://vallandingham.me/small_multiples_with_details.html

55. MORE ABOUT CHARTS

56. Data Visualization LibGuides
• Data visualization:
http://guides.library.duke.edu/datavis
• Visualization types:
http://guides.library.duke.edu/vis_types
• Top 10 dos and don’ts for charts and graphs:
http://guides.library.duke.edu/topten
• Visual communication:
http://guides.library.duke.edu/visualcomm

57. Good Chart Makeover Examples
The Why Axis chart remakes
http://thewhyaxis.info/remakes/
Storytelling With Data visual makeovers:
http://www.storytellingwithdata.com/search/
label/Visual%20Makeover

58. On the web
• Bad examples:
WTF Viz, http://wtfviz.net/
• Good examples:
Thumbs Up Viz, http://thumbsupviz.com/
• Ask for help:
Help Me Viz, http://helpmeviz.com/

59. CONDUCTING VISUALIZATION RESEARCH

60. Types of Research
• techniques
develop new
visual
metaphors
Speckmann, B., &Verbeek, K. (2010). Necklace maps. IEEE
Transactions onVisualization and Computer Graphics,16(6), 881-889.
http://dx.doi.org/10.1109/TVCG.2010.180

61. Types of Research
• techniques
• systems
develop new
software
Meyer, M., Munzner,T., & Pfister, H. (2009). MizBee:A
multiscale synteny browser. IEEETransactions onVisualization and
Computer Graphics,15(6), 897-904.
http://dx.doi.org/10.1109/TVCG.2009.167

62. Types of Research
• techniques
• systems
• design studies
study how a specific
group or field uses
visualization Goodwin, S., Dykes, J., et al. (2013). Creative user-
centered visualization design for energy analysts and
modelers. IEEETransactions onVisualization and
Computer Graphics,19(12), 2516-2525.
http://dx.doi.org/10.1109/TVCG.2013.145

63. Types of Research
• techniques
• systems
• design studies
• evaluations
study how humans
generally interact
with visualizations
Ziemkiewicz, C., & Kosara, R. (2010). Laws of attraction:
From perceived forces to conceptual similarity. IEEE
Transactions on Visualization and Computer Graphics,16(6),
1009-1016.
http://dx.doi.org/10.1109/TVCG.2010.174

64. Types of Research
• techniques
• systems
• design studies
• evaluations
• theories/models
develop a new theory of
visualization
Jansen,Y., & Dragicevic, P. (2013).An
interaction model for visualizations beyond
the desktop. IEEETransactions onVisualization
and Computer Graphics,19(12), 2396-2405.
http://dx.doi.org/10.1109/TVCG.2013.134

65. QUESTIONS? SUGGESTIONS?
angela.zoss@duke.edu
twitter.com/duke_vis

66. BONUS: COLOR THEMES FOR POSTERS,
INFOGRAPHICS, SLIDESHOWS, WEBSITES, ETC.

67. Theme
• Pick two or three main colors that complement each
other to add visual interest
• Maintain high visual contrast throughout
• Do not use a background image

71. http://visual.ly/10-commandments-color-theory

72. BONUS: DEVELOPMENT OF A VIS PROJECT

73. http://bit.ly/pubmedvis

74. Changes over time?
Heat map

75. Changes over time?
Radial technique

76. Changes over time?
Line chart of ranks

77. Changes over time?
Stacked line

78. Changes over time?
Streamgraph
http://raw.densitydesign.org/

80. Correlations or flows
Alluvial diagram
http://raw.densitydesign.org/

81. Correlations or flows