1. SIMS 247 Information Visualization and Presentation Prof. Marti Hearst September 14, 2000

2. Schedule for Next Few Weeks TodayToday –Critique assignment assigned –Visualization as Rearranging Sept 19Sept 19 –Matt Ward on multi-attribute visualization, Xmdv tools Sept 21Sept 21 –Al Inselberg on parallel coordinates –EDA assignment assigned (2 weeks to do it) Sept 26Sept 26 –Text visualization lecture Sept 28Sept 28 –Anna Wichansky on database visualization Oct 3Oct 3 –Warren Sack on visualizing conversations; Rachna Dhamija on visualization for security

3. Visual 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%)Mueller-Lyon (off by 25-30%) Horizontal-VerticalHorizontal-Vertical

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

6. What are good guidelines for Infoviz? Use graphics appropriatelyUse graphics appropriately –Don’t use images gratuitously –Don’t lie with graphics! Link to original data –Don’t conflate area with other information E.g., use area in map to imply amount Make it interactive (feedback)Make it interactive (feedback) –Brushing and linking –Multiple views –Overview + details Match mental modelsMatch mental models

7. Rearranging Techniques Ask what-if questions spontaneously while working through a problemAsk what-if questions spontaneously while working through a problem Control the exploration of subsets of data from different viewpointsControl the exploration of subsets of data from different viewpoints (Following slides from Information Visualization by Robert Spence)

8. Eye-HairColorData

9. Eye-HairColorData

10. Eye-HairColorData

11. Eye-HairColorData

12. TitanicData

13. TableLens Visualizing a very large spreadsheetVisualizing a very large spreadsheet Dynamic rearrangement allows for insightsDynamic rearrangement allows for insights Nice use of focus+contextNice use of focus+context (watch video)(watch video)

14. Brushing and Linking More interactive techniquesMore interactive techniques –Highlighting –Brushing and Linking Example systemsExample systems –Graham Will’s EDV system –Ahlberg & Sheiderman’s IVEE (Spotfire)

15. Brushing An interactive techniqueAn interactive technique –select a subset of points –see the role played by this subset of points in one or more other views At least two things must be linked together to allow for brushingAt least two things must be linked together to allow for brushing

16. Highlighting (Focusing) Focus user attention on a subset of the data within one graph (from Wills 95)

17. Highlighting: selection within one graph (from Schall 95)

18. Link similar types of graphs: Brushing a Scatterplot Matrix (Figure from Tweedie et al. 96; See also Cleveland & McGill 84, 88)

19. Link different types of graphs: Scatterplots and histograms and bars (from Wills 95)

20. Baseball data: Scatterplots and histograms and bars (from Wills 95) select high salaries avg career HRs vs avg career hits (batting ability) avg assists vs avg putouts (fielding ability) how long in majors distribution of positions played

21. What was learned from interaction with this baseball data? –Seems impossible to earn a high salary in the first three years –High salaried players have a bimodal distribution (peaking around 7 & 13 yrs) –Hits/Year a better indicator of salary than HR/Year –High paid outlier with low HR and medium hits/year. Reason: person is player-coach –There seem to be two differentiated groups in the put-outs/assists category (but not correlated with salary) Why?

22. Linking types of assist behavior to position played (from Wills 95)