1. 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 21, 2005

2. 2 Today Review Design Exercise High-interaction Graphics –Brushing, linking, highlighting Demonstration of EDA Systems

3. 3 Beyond Standard Techniques It’s often hard to beat: –Line graphs, bar charts –Scatterplots (or Scatterplot Matrix) –Tables A Darwinian view of visualizations: –Only the fittest survive –We are in a period of great experimentation; eventually it will be clear what works and what dies out. A bright spot: –Enhancing the old techniques with interactivity –Example: Spotfire Adds interactivity, color highlighting, zooming to scatterplots –Example: TableLens / Eureka Adds interactivity and length cues to tables

4. 4 Brushing & Linking Linking Visually indicating which parts of one data display correspond to that of another Brushing Allowing the user to move a region (brush) around the data display to highlight groups of data points. Usability issues: Selection, de-selection, setting values, appropriate widgets

5. Brushing and Linking At least two things must be linked together to allow for brushing –select a subset of points –see the role played by this subset of points in one or more other views Highlighting is key Example systems –Ahlberg & Sheiderman’s IVEE (Spotfire) –Graham Will’s EDV system

6. Linking types of assist behavior to position played (from Eick & Wills 95)

7. Baseball data: Scatterplots and histograms and bars (from Eick & 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

8. What can be 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?

9. 9 Slide adapted from Sarah Waterson Dynamic Queries Selecting value ranges of variables via controls with real time feedback in the display. Selection by pointing, not typing Immediate and continuous feedback Support browsing Details on demand Principles: Visual presentation of query’s components Visual presentation of results Rapid, incremental, and reversible control

10. 10 Slide adapted from Sarah Waterson Dynamic Queries Tight coupling Query components are interrelated in ways that preserve display invariants, reveal state of system Output of queries can be easily used as input to produce other queries. Eliminate distinction between commands/queries/input and results/tables/output Example: Interactive Scatterplots Multiple Names: Starfield, IVEE, Spotfire, HomeFinder

11. 11 Interaction with Scatterplots

12. 12 Interaction with Scatterplots

13. 13 Slide adapted from Sarah Waterson Home Finder: Text

14. 14 Slide adapted from Sarah Waterson Home Finder: Map

15. 15 Slide adapted from Sarah Waterson B&L Example DynaMap Cervical cancer rates from 1950-1970 - modify year, state, demographics

16. 16 Slide adapted from Sarah Waterson More Brushing & Linking Examples Districts of the city of Dublin showing areas with high levels of average income Linking altitude to grass and grain types in Scottish Districts

17. 17 Slide adapted from Sarah Waterson B&L Example Periodic Table of the Elements Adjust properties with sliders on the bottom to highlight matching elements. (Ahlberg,Williamson, Shneiderman ’92)

18. 18 Slide adapted from Sarah Waterson Pros & Cons Quick, easy, safe, & playful Good for novices & experts For exploration of large data sets Simple queries only So many controls…

19. 19 Effective Application of Brushing and Linking: PaperLens

20. 20 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

21. 21 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

22. 22 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

23. 23 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

24. 24 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

25. 25 PaperLens (Lee, Czerwinski, Robertson, Bederson ’05)

26. 26 Next Time Multidimensional Visualization