Lecture 12: Interaction and Analysis User Interface Methods and Tasks

1. Lecture 12:Interaction and AnalysisUser Interface Methods and Tasks October 19, 2010 COMP 150-12Topics in Visual Analytics

2. Lecture Outline Decision Matrix ( • Interaction and Analysis • Definition • Interaction with data and problem • Relationship between interaction and problem-solving • Types of analysis problems • Analytical methods • Interaction with visual interfaces • Basic interaction types • Sample interaction methods • Interaction with other people • Collaborative analysis (CSCW) • Can interaction exist without visualization? • Design flow of interactive visualization tools

3. Interaction Elements • A few existing taxonomies • Low-level interaction techniques

4. Interaction Elements • A few existing taxonomies • Dimensions of Interaction Techniques • Tweedie (1997) • Spence (2007) • Interaction Operations • Ward and Yang (2004) • User Tasks • Zhou and Feiner (1998) • Amar, Eagan, Stasko (2005)

5. Interaction Types • Yi, Kang, and Stasko (2007) • Select: • Mark something as interesting • Explore: • Show me something else • Reconfigure: • Show me a different arrangement • Encode: • Show me a different representation • Abstract/Elaborate: • Show me more or less detail • Filter • Show me something conditionally • Connect • Show me related items

6. 1. Select • Select: mark something as interesting • Dust & Magents • http://www.cc.gatech.edu/gvu/ii/dnm/

7. 1. Select • Select: mark something as interesting • TableLens

8. 1. Select • Questions: • What are you selecting? One item at a time? • Selecting of a value? • Selecting of a range? • Selecting of a position on the screen?

9. 2. Explore • Show me something else • Scroll bars • Panning • Direct-Walk • Hyperlink traversal • Visual Thesaurus (http://www.visualthesaurus.com/)

10. 3. Reconfigure • Show me a different arrangement • Sorting in TableLens

11. 3. Reconfigure • Show me a different arrangement • Baseline adjustment in a stacked histogram

12. 3. Reconfigure • Show me a different arrangement • Geotime

13. 3. Reconfigure • Show me a different arrangement • Data Mountain

14. 3. Reconfigure • Show me a different arrangement • Reducing occlusion (jitter)

15. 4. Encode • Show me a different representation • Spotfire, Tableau, Xmdv (switching visualization) • Changing color encoding • Changing other encodings: • Size, orientation, font, shape, etc.

16. 5. Abstract / Elaborate • Show me more or less detail • SequoiaView (Cushion Treemap) – drill up/down

17. 5. Abstract / Elaborate • Show me more or less detail • Sunburst

18. 5. Abstract / Elaborate • Show me more or less detail • SeeIT (tool tips)

19. 5. Abstract / Elaborate • Show me more or less detail • Probes

20. 5. Abstract / Elaborate • Show me more or less detail • Zooming in geographical visualizations

21. 6. Filter • Show me something conditionally • Dynamic HomeFinder

22. 6. Filter • Show me something conditionally • Attribute Explorer

23. 6. Filter • Show me something conditionally • Name Voyager • http://www.babynamewizard.com/name-voyager

24. 7. Connect • Show me related items • Highlight associations and relationships • Show hidden data items that are relevant to a specific item

25. 7. Connect • Show me related items • Coordinated Multiple Views (CMV) • Brushing and Linking

26. 7. Connect • Show me related items • Snap-Together Visualization

27. 7. Connect • Show me related items • Snap-Together Visualization (system architecture)

28. 7. Connect • Show me related items • Collaborative Brushing and Linking • http://www.youtube.com/watch?v=E9izFMJ5yms

29. Discussion • Other interactions?

30. Discussion • Other interactions? • Undo, redo? • Change configurations and settings of a system/visualization?

31. Discussion • Interactions that encode multiple goals • Pad++ • (http://www.youtube.com/watch?v=62KcJ09k7cE) • Magic Lens

32. Discussion • Higher-level concepts • Compare?

33. Discussion • Higher-level concepts • Compare? • Filter data to compare items of interest • Reconfigure to compare two subsets • Encode variables for adding contrast

34. Questions?

35. Discussion • Was that really a taxonomy of interactions? Or was it a taxonomy of visualizations? • Are the two separable?

36. Interaction Or Visualization • If someone asks you to design a system to analyze the census, where do you start?

37. Interaction-Centric? • Can we consider interactions first before thinking of visual representations? • What if interactions == analysis tasks?

38. Case Study: Brushing and Linking • The linchpin in most visualizations that utilize multiple coordinated views. • Spotfire, GeoVISTA, JIGSAW, etc. • However, when used in a collaborative environment, it’s purpose becomes slightly different even though the implementation is (mostly) the same. [Isenberg et al. 2009] • Hypothesis: the nature of Brushing and Linking is to coordinate between different perspectives of the same data elements, especially for data of high dimensionality. • It is now easier to consider a system design around this…

39. Analytic Activity in Information Visualization • Wehrend and Lewis (1990) • Identify • Locate • Distinguish • Categorize • Cluster • Distribution • Rank • Compare • Within and between relations • Associate • Correlate

40. Analytic Activity in Information Visualization • Roth and Mattis (1990) • Display functions • Vary presentation of data based on whether users desire exact value lookup, comparison, or correlation • Distribution functions • How to distribute a dataset within the presentation

41. Analytic Activity in Information Visualization • Zhou and Feiner (1998) • Two Intents: • Inform • Elaboration and summarization of data • Enable • Data exploration and derivation of relationships

42. Analytic Activity in Information Visualization • Card and Pirolli (2005)

43. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Retrieve value • Filter • Compute derived value • Find extremum • Sort • Determine range • Characterize distribution • Find anomalies • Cluster • Correlate

44. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Retrieve value • What are the values of attributes X, Y, Z in the data points A, B, C? • Filter • Which funds under-performed the S&P 500 last year? • Compute derived value • What is the average income of CS grad students? • Find extremum • Which car has the highest MPG? • Sort • Order the cars by horse power

45. Analytic Activity in Information Visualization • Amar, Eagan, Stasko (2005) • Determine range • What is the length of this film? Who are the actors in this movie? • Characterize distribution • What is the age distribution of shoppers who purchase cars with 40+ MPG? • Find anomalies • Who are the outliers? • Cluster • Which cars are similar to each other in MPG, horse power, and price? • Correlate • Is there a relationship between horse power and MPG?

46. Higher-level Tasks • What do we want to do in analysis? • Decision making under uncertainty • Better understand a domain or a problem • Identify the trends of a phenomenon • Forecast the future • Etc. • Gaps from high to low level to interaction level? • What’s missing?

47. Questions?

48. Interaction Costs • Lam (2008), survey of 484 papers • Decision costs to form goals • System-power costs to form system operations • Multiple input mode costs to form physical sequences • Physical-motion costs to execute sequences • Visual-cluttering costs to perceive state • View-change costs to interpret perception • State-change costs to evaluate interpretation

49. Interaction Costs

50. 1. Decision costs to form goals • When interfaces become more powerful and display more data points, users usually need to • decide to focus on a subset of data, and • interface options