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 Street Pump

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

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 Selection Related marks

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

17. 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 Transformations Data Raw Data Data Tables Human Interaction (controls) Visual Mappings View Transformations View Task Visual Structures Views

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

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. Length Position Cleveland & McGill: Quantitative Perception More accurate Less accurate AngleSlope Volume Area ColorDensity

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. Nominal Position Shape Color hue Gray ramp Color ramp Length Angle Area 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

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