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Essentials of Data Visualization

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

Geoff McGhee

December 06, 2016
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  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. • 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
  4. THEN we drew pictures of data NOW we use software

    code to generate visualizations of (possibly fluctuating) data using predefined rules
  5. • “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?
  6. • 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
  7. 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
  8. How Visualization Works Visual Encoding of Information The two kinds

    of graphical perception: Attentive Preattentive
  9. • 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
  10. 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
  11. 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
  12. “Quantitative” Visual Attributes Showing X “is greater than” Y Color

    Intensity Size Line Length Line Thickness Quantitative vs. Qualitative How Visualization Works
  13. “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
  14. 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
  15. • 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/
  16. “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
  17. 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
  18. Rainbow palette is popular in scientific visualization But it lacks

    a visual hierarchy Practice Noise Reduction: Clarity in Symbology
  19. • 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
  20. • 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
  21. 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