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Designing Great Visualizations

Designing Great Visualizations. Jock D. Mackinlay Director, Visual Analysis, Tableau Software. Outline. Examples from the history of visualization Computer-based visualization has deep roots Human perception is a fundamental skill Lessons for designing great visualizations

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Designing Great Visualizations

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  1. Designing Great Visualizations Jock D. Mackinlay Director, Visual Analysis, Tableau Software

  2. Outline • Examples from the history of visualization • Computer-based visualization has deep roots • Human perception is a fundamental skill • Lessons for designing great visualizations • Human perception is powerful • Human perception has limits • Use composition and interactivity to extend beyond these limits • Finally, great designs tell stories with data • Image sources: • www.math.yorku.ca/SCS/Gallery • www.henry-davis.com/MAPS

  3. Visual Representations are Ancient • 6200 BC: Wall image found in Catal Hyük, Turkey • Painting or map?

  4. Two Common Visual Representations of Data Presentations: Using vision to communicate • Two roles: presenter & audience • Experience: persuasive Visualizations: Using vision to think • Single role: question answering • Experience: active 1999: Morgan Kaufmann

  5. Maps as Presentation • 1500 BC: Clay tablet from Nippur, Babylonia • Evidence suggests it is to scale • Perhaps plan to repair city defenses

  6. Maps as Visualization • 1569: Mercator projection • Straight line shows direction

  7. William Playfair: Abstract Data Presentation • 1786:The Commercial and Political Atlas (Book) • 1801: Pie chart

  8. Dr. John Snow: Statistical Map Visualization • 1855: London Cholera Epidemic • It is also a presentation Broad StreetPump

  9. Charles Minard: Napoleon’s March • 1869: Perhaps the most famous data presentation

  10. Darrell Huff: Trust • 1955: How to Lie With Statistics (Book) • Trust is a central design issue • Savvy people will always question data views • Does a data view include the origin? • Is the aspect ratio appropriate?

  11. x x x x x x x x x x x x x x x x Jacques Bertin: Semiology of Graphics (Book) • 1967: Graphical vocabulary • Marks Points Lines Areas • Position • Statistical mapping • Retinal Color Size Shape Gray Orientation Texture

  12. Jacques Bertin (continued) • Visual analysis by sorting visual tables • Technology

  13. Jock Mackinlay: Automatic Presentation • 1986: PhD Dissertation, Stanford • Extended and automated Bertin’s semiology • APT: A Presentation Tool

  14. Scientific Visualization • 1986: NSF panel and congressional support Wilhelmson et al

  15. Richard Becker & William Cleveland • 1987: Interactive brushing Related marks Selection

  16. Information Visualization • 1989: Stuart Card, George Robertson, Jock Mackinlay • Abstract data • 2D & 3D interactive graphics • 1991: Perspective Wall & Cone Tree

  17. Task Book: Readings in Information Visualization • 1999: Over a decade of research • Card, Mackinlay, Shneiderman • An established process of visual analysis • Involves both data and view • Interactive and exploratory Data View DataTables Views RawData VisualStructures Data Transformations Visual Mappings View Transformations Human Interaction (controls)

  18. Chris Stolte • 2003: PhD Dissertation, Stanford • Extended the semiology from Bertin & Mackinlay • VizQL connected visualizations to databases • Accessible drag-and-drop interface VizQL View Query Data Interpreter Visual Interpreter

  19. Visual Analysis for Everyone • 2008: Tableau Customer Conference

  20. Human Perception is Powerful • How many 9s?

  21. Human Perception is Powerful • Preattentive perception:

  22. Traditional Use: Negative Values • However, mental math is slow

  23. Cleveland & McGill: Quantitative Perception More accurate Position Length Angle Slope Area Volume Color Density Less accurate

  24. Exploiting Human Perception

  25. Bertin’s Three Levels of Reading • Elementary: single value • Intermediate: relationships between values • Global: relationships of the whole

  26. Global Reading: Scatter View • Bertin image:A relationship you can see during an instant of perception

  27. Effectiveness Depends on the Data Type • Data type • Nominal: Eagle, Jay, Hawk • Ordinal: Monday, Tuesday, Wednesday, … • Quantitative: 2.4, 5.98, 10.1, … • Area • Nominal: Conveys ordering • Ordinal: • Quantitative: • Color • Nominal: • Ordinal: • Quantitative:

  28. Ranking of Tableau Encodings by Data Type Quantitative Position Length Angle Area Gray ramp Color ramp Color hue Shape Ordinal Position Gray ramp Color ramp Color hue Length Angle Area Shape Nominal Position Shape Color hue Gray ramp Color ramp Length Angle Area

  29. Human Perception is Limited • Bertin’s synoptic of data views • 1, 2, 3, n data dimensions • The axes of data views: ≠ Reorderable O Ordered T Topographic • Network views • Impassible barrier • Below are Bertin’s images • Above requires • Composition • Interactivity • First a comment about 3D

  30. 3D Graphics Does Not Break the Barrier • Only adds a single dimension • Creates occlusions • Adds orientation complexities • Easy to get lost • Suggests a physical metaphor

  31. Composition: Minard’s March • Two images:

  32. Composition: Small Multiples

  33. Composition: Dashboards

  34. Interactivity: Bertin’s Sorting of Data Views

  35. Interactivity: Too Much Data Scenario

  36. Interactivity: Aggregation

  37. Interactivity: Filtering

  38. Interactivity: Brushing

  39. Interactivity: Links

  40. Telling Stories With Data • What are the good school districts in the Seattle area? • Detailed reading • One school or school district at a time

  41. Telling Stories With Data (continued) • I needed a statistical map

  42. Telling Stories With Data (continued) • Positive trend views online • Easy to see that the district is stronger than the state • Harder to see that reading is stronger than math • Found the source data, which is a good thing about public agencies

  43. Telling Stories With Data (continued) • Reading is clearly better than math

  44. Telling stories with data (continued) • Moral: Always Question Data

  45. Telling Effective Stories • Trust: a key design issue • Expressive: convey the data accurately • Effective: exploit human perception • Use the graphical vocabulary appropriately • Utilize white space • Avoid extraneous material • Context: Titles, captions, units, annotations, …

  46. Stories Involve More Than Data • Aesthetics: What is effective is often affective • Style: Include information about who you are • Playful: Allow people to interact with the data views • Vivid: Make data views memorable

  47. Summary • Visualization & presentation • Human perception is powerful & limited • Coping with Bertin’s barrier • Composition • Interactivity • Sorting • Filtering • Aggregation • Brushing • Linking • Telling stories with data • Trust is a key design issue • Always question data

  48. Resources • My email: jmackinlay@tableausoftware.com • Edward Tufte (www.edwardtufte.com) • The Visual Display of Quantitative Information • Beautiful Evidence • Jacques Bertin • Semiology of Graphics, University of Wisconsin Press • Graphics and Graphic Information Processing, deGruyter • Colin Ware on human perception & visualization • Information Visualization, Morgan Kaufmann • William S Cleveland • The Elements of Graphic Data, Hobart Press

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