SIMS 247 Information Visualization and Presentation

1. SIMS 247Information Visualization and Presentation Prof. Marti Hearst January 25, 2002

2. What is Information Visualization? visualize: to form a mental image or vision of … visualize: to imagine or remember as if actually seeing. American Heritage dictionary, Concise Oxford dictionary

3. What is Information Visualization? ``Transformation of the symbolic into the geometric'' (McCormick et al., 1987) ``... augmenting … natural intelligence in the best possible way, ... finding the artificial memory that best supports our natural means of perception.'' (Bertin, 1983) The depiction of information using spatial or graphical representations, to facilitate comparison, pattern recognition, change detection, and other cognitive skills that make use of the visual system.

4. Information Visualization • Problem: • HUGE Datasets: How to understand them? • Solution • Take better advantage of human perceptual system • Convert information into a graphical representation. • Issues • How to convert abstract information into graphical form? • Do visualizations do a better job than other methods?

5. Visualization Success Stories Images from yahoo.com

6. The Power of Visualization 1. Start out going Southwest on ELLSWORTH AVE Towards BROADWAY by turning right. 2: Turn RIGHT onto BROADWAY. 3. Turn RIGHT onto QUINCY ST. 4. Turn LEFT onto CAMBRIDGE ST. 5. Turn SLIGHT RIGHT onto MASSACHUSETTS AVE. 6. Turn RIGHT onto RUSSELL ST. Image from mapquest.com

7. The Power of Visualization Line drawing tool by Maneesh Agrawala http://graphics.stanford.edu/~maneesh/

8. Visualization Success Story Mystery: what is causing a cholera epidemic in London in 1854?

9. Visualization Success Stories Illustration of John Snow’s deduction that a cholera epidemic was caused by a bad water pump, circa 1854. Horizontal lines indicate location of deaths. From Visual Explanations by Edward Tufte, Graphics Press, 1997

10. Visualization Success Stories Illustration of John Snow’s deduction that a cholera epidemic was caused by a bad water pump, circa 1854. Horizontal lines indicate location of deaths. From Visual Explanations by Edward Tufte, Graphics Press, 1997

11. Visualization in the Aftermath of 9/11

12. Six Degrees of Mohamed Attahttp://business2.com/articles/mag/0,1640,35253,FF.html

13. US Terrorism Response Org Charthttp://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart

14. US Terrorism Response Org Charthttp://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart

15. Flash Animation of Flight Paths http://www.usatoday.com/graphics/news/gra/gflightpath2/frame.htm

16. Purpose of Information Visualization To help: Explore Calculate Communicate Decorate

17. Two Different Primary Goals:Two Different Types of Viz Explore/Calculate Analyze Reason about Information Communicate Explain Make Decisions Reason about Information

18. Goals of Information Visualization More specifically, visualization should: • Make large datasets coherent (Present huge amounts of information compactly) • Present information from various viewpoints • Present information at several levels of detail (from overviews to fine structure) • Support visual comparisons • Tell stories about the data

19. This Course Visualization of large, abstract datasets Examples: text collections / digital libraries web clickstream data complex information systems your idea goes here

20. Why Visualization? Use the eye for pattern recognition; people are good at scanning recognizing remembering images Graphical elements facilitate comparisons via length shape orientation texture Animation shows changes across time Important to link with kinds of communication

21. A Key Question How do we Convert abstract information into a visual representation While still preserving the underlying meaning And at the same time providing new insight?

22. Key Tools • Animation • Using size to indicate quantity • Using color for distinguishing / selection • NOT FOR QUANTITY!!!! • Brushing and Linking • Providing multiple views

23. The Need for Critical Analysis • We see many creative ideas, but they often bomb in use • The hard part: how to apply it judiciously • Inventors usually do not accuratly predict how their invention will be used • This course will emphasize • Getting past the coolness factor • Examining usability studies

24. Example of finding the right application • The Treemap (Johnson & Shneiderman) • Idea: • Show a hierarchy as a 2D layout • Size on screen indicates relative size of underlying objects

25. Treemap applied to File System

26. Treemap Problems • Too disorderly • What does adjacency mean? • Aspect ratios uncontrolled leads to lots of skinny boxes that clutter • Color not used appropriately • In fact, is meaningless here • Wrong application • Don’t need all this to just see the largest files in the OS

27. Tennis Match Application

28. File System Application

29. Successful Application of Treemaps • Think more about the use • Break into meaningful groups • Fix these into a useful aspect ratio • Use visual properties properly • Use color to distinguish meaninfully • Only two colors: can distinguish one thing from another • Amount isn’t very important • Provide excellent interactivity • Access to the real data • Makes it into a useful tool

30. TreeMaps in Action http://www.smartmoney.com/maps http://www.peets.com/tast/11/coffee_selector.asp

31. A Good Use of TreeMaps and Interactivity www.smartmoney.com/marketmap

32. Treemaps in Peets site

33. Analysis vs. Communication • MarketMap use of treemaps allows for sophisticated analysis • Peets use of treemaps is more for presentation and communication • This is a key contrast for this course

34. Open Issues • Does visualization help? • The jury is still out • Still supplemental at best for text collections • A correlation with spatial ability • Learning effects: with practice ability on visual display begins to equal that of text • Does visualization sell? • Jury is still out on this one too! • This is a hot area! More ideas will appear!

35. What we are not covering • Scientific visualization • Statistics • Cartography (maps) • Education • Games • Computer graphics in general • Computational geometry

36. Readings • Textbook: Card, Mackinlay, and Shneiderman (Eds.) Information Visualization: Using Vision to Think • A course reader (not ready yet) • I have a large collection of books that can be borrowed. • There are many online resources

37. Software • We have special access to software • Will mainly be on SIMS lab machines • Spotfire • Table lens (Eureka) • Parallel Coordinates • Polaris

38. Prototyping Tools • Flash or Python + TK • Good for prototyping animation • Jazz toolkit • Written in java; supports zoom • Polaris toolkit • Can build up complex systems using XML or tcl scripting

39. Class Structure • A new topic each week • Student run • Lots of readings • Two or three assignments (50% total) • One final project (50%)

40. Each Week • Two students make a presentation • Lasts 1 to 1.5 hours • Summary, synthesis, & critical analysis of the contents of the readings • Use the questions shown on a later slide • Examples of relevant visualizations found on the web, in print, etc • Suggestions for how the ideas could be used, or why they should not

41. Each Week (cont.) • Non-presenting students • Read “all but one” of the assigned papers • Write up one page • The contents of the writeup will be determined in advance by the presenting students • The presenting students will provide feedback on the other students’ writeups • check, check-minus, check-plus

42. Each Week (cont.) • Prof will do one or more of: • Have a guest speaker • Show an interesting video • Bring up some interesting topic to discuss • Ask other students to show an assignment they did • (with advance warning)

43. Key Questions to Ask about a Viz • What does it teach/show/elucidate? • What is the key contribution? • What are some compelling, useful examples? • Could they have been done more simply? • Have there been usability studies done? What do they show?

44. For Next Week • (Prof will lecture once more) • Do the following readings: • RIIV: Chapter 1. Information Visualization • Carlis, J. V., and Konstan, J.K., Interactive Visualization of serial periodic data. • Bertin, Graphics and Graphic Information Processing • Kosslyn, S. M., Understanding Charts and Graphs. • J. Mackinlay Automating the Design of Graphical Presentations of Relational Information • Lohse, G.L., Biolsi, K., Walker, N., and Rueter, H.H., A Classification of Visual Representations • Optional: Treemap paper