Lecture 16: Interaction

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# Lecture 16: Interaction - PowerPoint PPT Presentation

## Lecture 16: Interaction

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##### Presentation Transcript

1. Lecture 16:Interaction April 2, 2013 COMP 150-2Visualization

2. Admin • Group Names • Group Presentation Time • Tim Berners Lee Talk • JC tutorial with D3 • Extra Credit – Talk by Liz Marai on Thursday • Attend the talk • Write me a short blurb • Read a paper of hers, and write a 1-page summary that is connected to her talk • Up to 2% of your final grade

3. Supporting Representation? • Interaction is vital to information visualization • Without interaction, visualization is static. With interaction, visualization can assist analytical thinking • In this context, visualization + interaction, interaction is the “little brother”

4. Supporting Interaction? • Information visualization is vital to interaction • Without representation, there is nothing to interact with. With representation, interaction can assist analytical thinking • In this context, interaction + visualization, representation is the “little brother”

5. Huh? • Does this work at all? • What’s wrong with this reasoning?

6. Questions?

7. 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

8. 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

9. 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?

10. Questions?

11. Exercise: • Name all the types of interactions that you have seen (or can think of) in visualizations • Consider the two aspects: • Interactions with the visualization ONLY • Interactions with the data (via the visualization)

12. Interaction Taxonomy • Not all interactions are data-driven, sometimes they are just used to modify the visual representation… • A taxonomy of interaction types based on a large survey of papers and systems (Yi et al. 2007) • The taxonomy emphasizes “user intent” as the categorization

13. 7 Types of Interactions • Select • Explore • Reconfigure • Encode • Abstract/Elaborate • Filter • Connect

14. 1. Select • “Mark something as interesting” • Hovering, popups, etc • Can be data-driven (i.e. using SQL or conjunctives)

15. 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?

16. 2. Explore http://www.visualthesaurus.com • “Show me something different” • Hyperlink, social network • Can be data-driven, but is a bit more complicated now…

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

18. 3. Reconfigure • “Show me a different arrangement” • Sorting, moving dimensions in Parallel Coordinates • Not data related – • purely visual

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

20. 3. Reconfigure Rearrange Sort

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

22. 4. Encode • “Show me a different representation” • Switching from bar-chart to line graph (assignment 2), changing font, changing orientation, etc. • Not data related • Important for thinking about the same data with different visualizations

23. 4. Encode

24. 5. Abstract / Elaborate • “Show me more or less detail” • Google map zooming, details on demand, popup lens • Possibly a combination • of data and • visualization

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

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

27. 6. Filter • “Show me something conditionally” • Dynamic query (Homefinder), Attribute Explorer, Google auto-complete • Could be data-driven • or visualization driven • http://research.microsoft.com/en-us/um/redmond/groups/cue/facetlens/

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

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

30. 6. Filter • Magic Lenses (Bier et al. 1993) • http://www.open-video.org/details.php?videoid=8167

31. 7. Connect • “Show me related items” • Brushing-and-linking (coordinated visualizations) • Does not need to be data-driven

32. 7. Connect Matkovic, IV 2008

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

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

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

36. Questions?