Essentials of Data Visualization

E7ab9c918935168aae0bd07b503b9284?s=47 Geoff McGhee
December 06, 2016
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Essentials of Data Visualization

Talk to Worldview Stanford's "Behind and Beyond Big Data" executive workshop

E7ab9c918935168aae0bd07b503b9284?s=128

Geoff McGhee

December 06, 2016
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Transcript

  1. Communicating with Data Company A Company B Company C $110

    86 62 Sales Name Million Geoff McGhee Bill Lane Center for the American West, Stanford University Behind and Beyond Big Data Worldview Stanford Dec 6, 2016
  2. • Why Data Visualization? • How Visualization Works • How

    to Communicate Data More Effectively • What’s on the Horizon
  3. A Decade in Infographics and Multimedia ME

  4. flight patterns data art- something in this… but what? http://www.aaronkoblin.com/project/flight-patterns/

  5. http://www.bewitched.com/historyflow.html

  6. http://hint.fm/projects/flickr/

  7. None
  8. • Big data and data visualization are changing news reporting

    and presentation • New players and skillsets are joining newsrooms • We need to learn more about telling stories with data • Good tools for narrative visualization don’t exist yet Journalism in the Age of Data (2010) datajournalism.stanford.edu
  9. None
  10. http://bit.ly/1TS1nQ8

  11. Data Visualization Back to Basics

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  13. THEN we drew pictures of data William Playfair, 1786

  14. THEN we drew pictures of data NOW we use software

    code to generate visualizations of (possibly fluctuating) data using predefined rules
  15. Visual Encoding of Information But the same fundamental process

  16. • “Utilizes one of the channels to our brain that

    have the highest bandwidths: our eyes”
 – Robert Kosara • Bypass language centers, go direct to the visual cortex • Leverage ability to recognize patterns, visual sense-making • Create mental models of phenomena… both literal and metaphorical Map of New Brainland by Unit Seven via Flickr How Visualization Works Why Visualize Information?
  17. Literal

  18. Fernanda Viegas and Martin Wattenberg

  19. Metaphorical

  20. The New York Times

  21. • Converting – “encoding” – information into graphical marks •

    Charts, yes, but something more fundamental Lines, shapes, patterns, size, position, shade, hue • The “charts” we know are compounds of those marks How Visualization Works Visual Encoding of Information Company A Company B Company C $110 86 62 Sales Name Million
  22. Classical visualizations were essentially done “by seat of the pants”

    Creators intuited what would work, and it mostly did 20th Century researchers analyzed patterns and put 
 practices to the test Bertin, Cleveland, Mackinlay et al. How Visualization Works Visual Encoding of Information Sémiologie Graphique
 Jacques Bertin, 1967 Graphical Perception
 Cleveland and McGill, 1984 Structure of Design Space
 Card and Mackinlay, 1997
  23. Group Exercise Identify the visual encodings •

  24. Linda Eckstein, Fortune

  25. John Tomanio, Fortune

  26. John Tomanio, Fortune

  27. Wittgenstein Center, Vienna Institute for Demography

  28. Wittgenstein Center, Vienna Institute for Demography

  29. The Wall Street Journal

  30. Gregory Hubacek for GOOD Magazine

  31. How Visualization Works Visual Encoding of Information The two kinds

    of graphical perception: Attentive Preattentive
  32. How Many Number 5’s? Preattentive Processing

  33. Now How Many? Preattentive Processing

  34. • We’re much better at quickly detecting SHADE variations than

    SHAPE differences • First example required reading, because complex shape of a numeral like “5” is an attentive attribute. • Second example: “5’s” are clearly visible when we highlight them in a different shade. Intensity of color is a preattentive attribute. This rapidly occurs below the level of consciousness. • When you find yourself actually reading a chart to try to understand what it says, that’s attentive processing Preattentive Processing How Visualization Works
  35. Preattentive Attributes Can be organized into four main categories: Source:

    Colin Ware: “Information Visualization: Perception for Design” (2004) Preattentive Processing How Visualization Works Color Form Position Movement
  36. Not All Preattentive Attributes Behave the Same Way All preattentive

    attributes can indicate distinctness, but not all have a clear hierarchy that can be utilized for comparing values.
 • Quantitative or Ordinal Values: 
 1, 2, 3... or S, M, L... 
 Operators =,≠,<,> • Categorical Values: 
 N, S, E, W... Apples, Oranges
 Operators =,≠ > > ? Using Scales to Show Hierarchy How Visualization Works
  37. “Quantitative” Visual Attributes Showing X “is greater than” Y Color

    Intensity Size Line Length Line Thickness Quantitative vs. Qualitative How Visualization Works
  38. “Qualitative” or “Categorical” Visual Attributes Showing X “is similar to

    or different from” Y Hue Shape Enclosure Added Marks Quantitative vs. Qualitative How Visualization Works
  39. Pie Angle Size (area)
 Hue or intensity (opt.) Bar/Column Size

    (length of bar) Hue or intensity (opt.) 2D position (neg vals.) Line 2D Position Hue or intensity (for mult. lines) Scatter Plot 2D Position Hue or intensity (opt.) Added marks (icons, etc) Company A Company B Company C $110 86 62 Sales Name Million Text Table Text (yes, text) Numbers 2D Position Added marks (rules, bgs) Bubble Size (area) Hue or intensity 2D Position Network Graph 2D Position Added marks How Visualization Works Charts: Visual Encoding at Work
  40. • Position (teams, records, payroll) • Number (record, payroll) •

    Icons/Text (teams) • Line (connection between record and payroll) • Angle (relationship betw. record and payroll) • Color (relationship between record and payroll) • Line thickness (size of payroll) How Many Encodings? https://fathom.info/salaryper/
  41. “I think that there’s a sense that data [visualization] is

    something more like a medium, something that can be used to tell stories, and to do all of the things that a medium can do, to delight and inspire...” – Eric Rodenbeck, Journalism in the Age of Data (2010)
 datajournalism.stanford.edu
  42. But...

  43. http://bit.ly/286Rmmq

  44. http://bit.ly/1NH3q5Q

  45. Communication Medium?

  46. None
  47. Learning to Tell Stories “You can do beautiful things with

    computers and lots of data that look very, very nice, and are almost completely incomprehensible.” datajournalism.stanford.edu
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  49. Economist

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  53. How to Lie with Visualization

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  57. Example of misleading: Trump bar charts

  58. Example of misleading: Trump bar charts

  59. Example of misleading: Trump bar charts

  60. Example of misleading: Trump bar charts

  61. Ways to communicate with data 1. Explain Your Visual Encodings

    to the User
  62. Explain Your Encodings with a Legend, Key, Scale

  63. None
  64. Ways to communicate with data 2. Reduce Noise

  65. Practice Noise Reduction: The “Data-Ink Ratio” https://darkhorseanalytics.com/blog/data-looks-better-naked/

  66. Rainbow palette is popular in scientific visualization But it lacks

    a visual hierarchy Practice Noise Reduction: Clarity in Symbology
  67. Ways to communicate with data 3. Use Narrative Sequences to

    Tell a Story
  68. The “Stepper” Present Main Points of a Visualization in a

    Sequence http://stanford.io/25zXkdc
  69. The “Stepper” Present Main Points of a Visualization in a

    Sequence http://stanford.io/1G2EopE
  70. “Scrollytelling” Present Main Points of a Visualization in a Sequence

    http://stanford.io/1lZ1vXO
  71. “Scrollytelling” Present Main Points of a Visualization in a Sequence

    http://stanford.io/1qppbav
  72. Other Narrative Formats Simulators and Models http://stanford.io/1lZ1vXO

  73. Other Narrative Formats Simulators and Models http://projects.aljazeera.com/2013/syrias-refugees/index.html

  74. Ways to communicate with data 4. Annotate Liberally to Explain

    What’s Shown
  75. • As powerful as visualization can be to enable rapid

    sense-making, many visualizations require a lot of context and interpretation, especially static, printed visualizations. • Think of the annotation layer as like the narrator’s voiceover in a documentary film. In fact, if you make a motion video, that might literally what it would be. Raise Your Narrative Voice The New York Times The Annotation Layer
  76. http://nyti.ms/1qfwceV

  77. http://nyti.ms/1qfwceV

  78. Ways to communicate with data 5. Where Possible, Offer Record-Level

    Detail
  79. • Also known as “details on demand,” interactive visualizations are

    richer when they give you as much info as possible on individual measures • In that sense, they are database front-ends in addition to graphical summaries. Visualizations are Databases Where Possible, Offer Record-Level Detail
  80. Where Possible, Offer Record-Level Detail

  81. http://citynature.stanford.edu/ Where Possible, Offer Record-Level Detail

  82. http://citynature.stanford.edu/ Where Possible, Offer Record-Level Detail

  83. Ways to communicate with data 1. Explain Your Symbology/Visual Encodings

    to the User 2. Reduce Noise 3. Use Narrative Sequences to Tell a Story 4. Annotate Liberally to Explain What’s Shown 5. Where Possible, Offer Record-Level Detail
  84. The Next Frontier of Data Visualization Algorithm Visualization

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  87. rock n poll

  88. Questions? @mcgeoff gmcghee@stanford.edu west.stanford.edu 


  89. Thank you! @mcgeoff gmcghee@stanford.edu west.stanford.edu