1. SIMS 247: Information Visualization and Presentation Marti Hearst
Feb 4, 2004

2. Today Perceptual Illusions Tufte’s Design Guidelines
How to Mislead with Visualizations
Infoviz Frameworks
Shneiderman’s Taxonomy
The Polaris System

3. Visual/Perceptual Illusions
People don’t perceive length, area, angle, brightness they way they “should”.
Some illusions have been reclassified as systematic perceptual errors
e.g., brightness contrasts (grey square on white background vs. on black background)
partly due to increase in our understanding of the relevant parts of the visual system
Nevertheless, the visual system does some really unexpected things.

4. Illusions of Linear Extent
Mueller-Lyon (off by 25-30%)
Horizontal-Vertical

5. Illusions of Area Delboeuf Illusion
Height of 4-story building overestimated by approximately 25%

6. Movement Illusion Moving Train Illusion
More than 300 people/year killed by trains in US
Many caused by misperceptions!
Large objects in motion appear to be moving more slowly than they really are.
Field-tested in the cab of a moving train
First looks like train is barely moving
Then takes over entire field of view (looming)

7. Illusion from Visual Comparison
Image from

8. Tufte Principles of Graphical Excellence Graphical excellence is
the well-designed presentation of interesting data – a matter of substance, of statistics, and of design
consists of complex ideas communicated with clarity, precision and efficiency
is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space
requires telling the truth about the data.

9. Tufte Principles Use High Data Density Number of items/area of graphic
This is controversial
White space thought to contribute to good visual design
Tufte’s book itself has lots of white space

10. Some Other Tufte Principles
Use multifunctioning graphical elements
Use small multiples
Show mechanism, process, dynamics, and causality
Avoid “chart junk”

11. A More Scientific Approach
Ellen Levy Jeff Zacks Barbara Tversky Diane Schiano, Gratuitous graphics? Putting preferences in perspective, Proceedings of CHI’96
Question: In what contexts do people find 3-D graphs of 2-D data both attractive and useful?
Method: Questionnaire examined 161 undergrad students' preferences for graphical display formats under several use scenarios
E.g.:
imagine yourself as a research scientist working at a high-tech firm. You have been given the chance to present the findings from you work to the Board of Directors of the company. Your research assistant has been assigned the job of preparing the slides that you will need. Given each of the scenarios listed below, decide which graphs (give your first and second choice) you think would be the best given the circumstances."

12. A More Scientific Approach
Ellen Levy Jeff Zacks Barbara Tversky Diane Schiano, Gratuitous graphics? Putting preferences in perspective, Proceedings of CHI’96

13. A More Scientific Approach
Ellen Levy Jeff Zacks Barbara Tversky Diane Schiano, Gratuitous graphics? Putting preferences in perspective, Proceedings of CHI’96
Summary of results:
Line graphs were preferred more for conveying trends than details, and more for promoting memorability than for immediate use
Bar graphs showed the opposite pattern
3-D graphs were preferred more for depicting details than trends, more for memorability than immediate use, and more for showing others than oneself
The reverse held for 2-D graphs

14. Low data-density example
Image from

15. “Chart Junk” example
Image from

16. Tufte’s Graphical Integrity
Some lapses intentional, some not
Lie Factor = size of effect in graph size of effect in data
Misleading uses of area
Misleading uses of perspective
Leaving out important context
Lack of taste and aesthetics

17. From Tim Craven’s LIS 504 course http://instruct. uwo

18. How to Exaggerate with Graphs from Tufte ’83
“Lie factor” = 2.8

19. How to Exaggerate with Graphs from Tufte ’83
Error:
Shrinking along both dimensions

20. Tufte’s Latest: Powerpoint Makes You Dumb

21. Tufte’s Powerpoint Arguments
“… may help speakers outline their talks, but convenience for the speaker can be punishing to both content and audience.”
“The standard PowerPoint presentation elevates format over content, betraying an attitude of commercialism that turns everything into a sales pitch.”
“At a minimum, a presentation format should do no harm. Yet the PowerPoint style routinely disrupts, dominates, and trivializes content.”

22. Tufte’s Powerpoint Arguments
Quotes Relevant to Infoviz:
“Visual reasoning usually works more effectively when relevant information is shown side by side. Often, the more intense the detail, the greater the clarity and understanding.”
Example: Cancer rate survival data
Shows a table and graphs with chartjunk
But no nicely-done graphs!
Wouldn’t a good graph help?
Does this argument really apply to powerpoint, or is it anti-chart-junk again?

23. Howard Wainer How to Display Data Badly (Video) http://www. dartmouth

24. Shneiderman’s Taxonomy
Data vs. Tasks
Data types:
Multi-dimensional: databases,…
1D: timelines,…
2D: maps,…
3D: volumes,…
Hierarchies/Trees: directories,…
Networks/Graphs: web,…
Document collections: digital libraries,…

25. Shneiderman’s Mantra
Overview first, then zoom & filter, then details on demand

26. Shneiderman’s Taxonomy
Data vs. Tasks
Task types:
Overview
Zoom
Filter
Details-on-Demand
Relate
History
Extract
What’s missing?

27. Polaris Framework
Goal: support interactive exploration of multi-dimensional relational databases
Nice overview of how to combine different standard visualizations into interactive systems.
Data types:
Nominal, ordinal, quantitative/interval
Groups into 3 pairs which map to different graph types
Punting on nominal – it’s difficult!
Supports design principles:
Small simultaneous multiples for comparison
Data-dense display
Allows proper use of “retinal properties” (term from Bertin)
Cleveland’s idea regarding mapping independent and dependent variables

28. Polaris Paper Two nice examples of exploratory data analysis
Analysts form hypotheses
Create views to confirm or refute
If refuted, follow leads to find new hypotheses
Look for different things
Trends
Anomalies

29. CFO of national coffee store chain
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30. Performance analysis of a parallel software library
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