1. Marti Hearst SIMS 247 SIMS 247 Lecture 24 Course Recap April 30, 1998

2. Marti Hearst SIMS 247 Where Have We Been? Information VisualizationInformation Visualization –recent surge of interest more online information more computing power –a developing area many exciting new ideas, but little theory little empirical validation or evaluation Tufte’s InfluenceTufte’s Influence

3. Marti Hearst SIMS 247 Graphic Display of Abstract Data Data typesData types –nominal, ordered, quantitative Anatomy of a graphAnatomy of a graph –framework, content, labels, background –graphs, charts, maps, diagrams Conventional graphsConventional graphs –when to use which –how not to mislead

4. Marti Hearst SIMS 247 Hypothetical Graphs length of page length of access URL # of accesses length of access # of accesses length of access length of page 0 5 10 15 20 25 30 35 40 45 short medium long very long days # of accesses url 1 url 2 url 3 url 4 url 5 url 6 url 7 # of accesses

5. Marti Hearst SIMS 247 Mapping Types in Charts one-to-one one-to-manymany-to-many

6. Marti Hearst SIMS 247 How to show link patterns in web access example? Problem: only shows one step Think about this for next time.

7. Marti Hearst SIMS 247 Graphing Multivariate Information How to handle more than 3 variables?How to handle more than 3 variables? –multifunctioning elements –multiple views –brushing and linking –animation

8. Marti Hearst SIMS 247 Multiple Views: Star Plot (Discussed in Feinberg 79. Works better with animation. Example taken from Behrans & Yu 95.)

9. Marti Hearst SIMS 247 Linked Scatterplots

10. Marti Hearst SIMS 247 Chernoff Experiment (Marchette)

11. Marti Hearst SIMS 247 Overlaying Space and Time (Minard’s graph of Napolean’s march through Russia)

12. Marti Hearst SIMS 247 Multiple Dimensions: Parallel Coordinates (earthquake data, color indicates longitude, y axis severity of earthquake, from Schall 95)

13. Marti Hearst SIMS 247 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

14. Marti Hearst SIMS 247 Restrict the range of parameter settings. How many constraints away from success? (Tweedie et al. 96) Coding seems complex initially, but suits the designers’ needs and is easily learned.

15. Marti Hearst SIMS 247 Dynamic Queries Instead of a formal database languageInstead of a formal database language Explore a dataset interactivelyExplore a dataset interactively Use graphical devices to interactively update a visualizationUse graphical devices to interactively update a visualization –Examples Ahlberg & Shneiderman 93 Filmfinder, etc. Roth et al. 96 VISAGE Woodruff et al. DataSplash Fishkin, Stone, Bier et al. Magic Lenses/Toolglass

16. Marti Hearst SIMS 247 VISAGE display (Roth et al. 96)

17. Marti Hearst SIMS 247 Click-through operators Example: change underlying color (Bier et al. 93) OriginalChange Fill Color Change Outline Color

18. Marti Hearst SIMS 247 Directly View and Change Font Characteristics (Bier et al. 93)

19. Marti Hearst SIMS 247 Viewing Huge Datasets Problem:Problem: –The computer display is a small window through which to view huge datasets Standard Solution:Standard Solution: –Display a portion at a time Problems: lose the context, get lost, comparisons are difficult,... Alternative Solution:Alternative Solution: –Focus + Context

20. Marti Hearst SIMS 247 Focus + Context Another solution:Another solution: –Use pixels more carefully Focus + ContextFocus + Context –Show a lot of information at once Details are too small to be visible –Focus on a subset of interest Make this subset large enough to be viewed

21. Marti Hearst SIMS 247 Focus + Context Data Types TablesTables HierarchiesHierarchies NetworksNetworks MapsMaps Artificial “worlds”Artificial “worlds”

22. Marti Hearst SIMS 247 Viewing Huge Tables: Table Lens (Rao & Card 94)

23. Marti Hearst SIMS 247 Distortion Types Different distortions for different data types yield different effectsDifferent distortions for different data types yield different effects –cartesian, polar coordinates, hyperbolic Leung & Apperley’s TaxonomyLeung & Apperley’s Taxonomy –distinguish focus+context from distortion f+c requires a POI function

24. Marti Hearst SIMS 247 Distortion Techniques Computation must take care not to let the magnified part overlap or cover up the de-magnified partComputation must take care not to let the magnified part overlap or cover up the de-magnified part The boundary between the magnified and the demagnified parts of the viewThe boundary between the magnified and the demagnified parts of the view –Some techniques have an abrupt boundary –Some are more gradual

25. Marti Hearst SIMS 247 Noik’s Demonstration

26. Marti Hearst SIMS 247 Elements of Fisheye Views Focus, or Point of Interest (POI)Focus, or Point of Interest (POI) –user-selected Importance Function (API)Importance Function (API) –user-specified or pre-determined number of railway connections height in hierarchy population of city Function for measuring distance between objectsFunction for measuring distance between objects

27. Marti Hearst SIMS 247 Properties of Graphs Edges can be directedEdges can be directed –can go from A to B, but not from B to A –use arrows to show directedness Graphs can have cyclesGraphs can have cycles –can get back to B when starting from B A B C DE

28. Marti Hearst SIMS 247 Sarkar & Brown 94 Paris Metro, importance corresponds to number of connections

29. Marti Hearst SIMS 247 Sarkar & Brown 94 distortion level 5 vs. 2

30. Marti Hearst SIMS 247 Perspective Wall (Mackinlay et al. 91)

31. Marti Hearst SIMS 247 Force-Directed Placement (Amir 93, based on Fruchterman and Rheingold 90)

32. Marti Hearst SIMS 247 All About Trees

33. Marti Hearst SIMS 247 Hyperbolic Browser Focus + Context TechniqueFocus + Context Technique –detailed view blended with a global view First lay out the hierarchy on Poincare’ mapping of the hyperbolic planeFirst lay out the hierarchy on Poincare’ mapping of the hyperbolic plane Then map this plane to a diskThen map this plane to a disk Use animation to navigate along this representation of the planeUse animation to navigate along this representation of the plane Start with the tree’s root at the centerStart with the tree’s root at the center

34. Marti Hearst SIMS 247 Hyperbolic Tree Browser (Lamping et al. 95)

35. Marti Hearst SIMS 247 Cluster-graphs (Eades & Qingwen 96) tree-like between planesgraph-like within planes

36. Marti Hearst SIMS 247 ConeTrees (Robertson et al. 91)

37. Marti Hearst SIMS 247 ConeTrees (Robertson et al. 91)

38. Marti Hearst SIMS 247 Hyperbolic ConeTrees (Munzner et al. 96)

39. Marti Hearst SIMS 247 Multi-Trees (Furnas & Zachs 94) Often we want more than one view on a treeOften we want more than one view on a tree Multi-trees convert the view of a dag (directed acyclic graph) into a set of overlapping treesMulti-trees convert the view of a dag (directed acyclic graph) into a set of overlapping trees

40. Marti Hearst SIMS 247 Why do Evaluation? To tell how good or bad a visualization isTo tell how good or bad a visualization is –People must use it to evaluate it –Must compare against the status quo –Something that looks useful to the designer might be too complex or superfluous for real users For iterative designFor iterative design –Interface might be almost right but require adjustments –The interactive components might have problems To advance our knowledge of how people understand and use technologyTo advance our knowledge of how people understand and use technology

41. Marti Hearst SIMS 247 Visual Properties Hue based boundary determined preattentively regardless of variation in form (left). However, the converse is not true (right).

42. Marti Hearst SIMS 247 Accuracy Ranking of Quantitative Perceptual Tasks (Mackinlay 88 from Cleveland & McGill) Position Length AngleSlope Area Volume ColorDensity More Accurate Less Accurate

43. Marti Hearst SIMS 247 Visual Illusions Mueller-Lyon (off by 25-30%)Mueller-Lyon (off by 25-30%) Horizontal-VerticalHorizontal-Vertical

44. Marti Hearst SIMS 247 Pan and Zoom

45. Marti Hearst SIMS 247 Space-Scale Diagrams (Furnas & Bederson 95) We can think of this in terms of 1D tooWe can think of this in terms of 1D too When zoomed out, you can see wider set of pointsWhen zoomed out, you can see wider set of points

46. Marti Hearst SIMS 247 Why Text is Tough As the man walks the cavorting dog, thoughts arrive unbidden of the previous spring, so unlike this one, in which walking was marching and dogs were baleful sentinals outside unjust halls. How do we visualize this?

47. Marti Hearst SIMS 247 BEAD (Chalmers 96) An example layout produced by Bead, seen in over-view, of 831 bibliography entries from CHI, CSCW and UIST conferences. The dimensionality (the number of unique words in the set) is 6925 and the layout stress is 0.16. After a search for ‘cscw or collaborative’ we see the pattern of occurrences coloured dark blue, mostly to the right. The central rectangle is the visualiser’s motion control.

48. Marti Hearst SIMS 247 Example: Themescapes (Wise et al. 95) Themescapes (Wise et al. 95)

49. Marti Hearst SIMS 247 Kohonen Feature Maps (Lin 92, Chen et al. 97) (594 docs)

50. Marti Hearst SIMS 247 InfoCrystal (Spoerri 93) A C B D 1 34 27 9 201 # of docs containg A, C, and B # of docs containg A # of docs containg B and D

51. Marti Hearst SIMS 247

52. Marti Hearst SIMS 247 SeeSoft: Changes of Lines of Code over Time (Eick 94)

53. Marti Hearst SIMS 247 Guest Lectures Color: Maureen StoneColor: Maureen Stone DB Pan & Zoom: Allison WoodruffDB Pan & Zoom: Allison Woodruff Design: Delle MaxwellDesign: Delle Maxwell 3D Interaction: Tamara Munzner3D Interaction: Tamara Munzner Animation: Bay-wei ChangAnimation: Bay-wei Chang Interactive Design: Robert ReimannInteractive Design: Robert Reimann Automated Graph Layout: Mike SchiffAutomated Graph Layout: Mike Schiff Data Mining and Viz: Ronny KohaviData Mining and Viz: Ronny Kohavi Texture and Visual Search: Ruth RosenholtzTexture and Visual Search: Ruth Rosenholtz

54. Marti Hearst SIMS 247 … and Finally Class Projects!!!