1. SIMS 247 Information Visualization and Presentation Marti Hearst February 15, 2002

2. Readings for This Week Eick, S. G. and Wills, G. J. High interaction graphics. B. Shneiderman, Dynamic Queries for Visual Information Seeking C. Ahlberg and B. Shneiderman, Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays Joseph M. Hellerstein, et al. Interactive Data Analysis: The Control Project K. Fishkin, M. C. Stone, Enhanced Dynamic Queries via Movable Filters

3. Brushing and Linking Interactive technique  Highlighting  Brushing and Linking Example systems  Graham Will’s EDV system  Ahlberg & Sheiderman’s IVEE (Spotfire)

4. Brushing 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 brushing

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

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

7. 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

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

9. 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?