1. Information Visualization CSCI 6174: Open Problems in CS Fall 2011 Richard Fowler

2. Ya gotta visualize … • I see what you mean … • so, visualization can be considered not just a visual process, but a cognitive (thought) process as well • And a very large part of human brain taken up with visual system • and that part of the brain is still useful beyond “simply” getting an image of the world • … which is in fact pretty complicated

3. Visualization is … • Visualize: • “To form a mental image or vision of …” • “To imagine or remember as if actually seeing …” • Firmly embedded in language, if you see what I mean • (Computer-based) Visualization: • “The use of computer-supported, interactive, visual representations of data to amplify cognition” • Cognition is the acquisition or use of knowledge • Card, Mackinlay Shneiderman ’98 • Scientific Visualization: physical • Information Visualization: abstract

4. Visualization is not New • Cave guys, prehistory, hunting • Directions and maps • Science and graphs • e.g, Boyle: p = vt • … but, computer based visualization is new • … and the systematic delineation of the design space of (especially information) visualization systems is growing nonlinearly

5. Visualization and Insight • “Computing is about insight, not numbers” • Richard Hamming, 1969 • And a lot of people knew that already • Likewise, purpose of visualization is insight, not pictures • “An information visualization is a visual user interface to information with the goal of providing insight.”, (Spence, in North) • Goals of insight • Discovery • Explanation • Decision making

6. “Computing is about insight, not numbers” • Numbers – states, %college, income: State % college degree income State % college degree income

7. “Computing is about insight, not numbers” • Insights: • What state has highest income?, What is relation between education and income?, Any outliers? State % college degree income State % college degree income

8. “Computing is about insight, not numbers” • Insights: • What state has highest income?, What is relation between education and income?, Any outliers?

9. Not about Useless Visual Stuff - Clutter • “3d” adds nothing • (at best)

10. Detrimental useless stuff • USA Today

11. An Example, Challenger Shuttle • Presented to decision makers • To launch or not • Temp in 30’s • “Chart junk” • Finding form of visual representation is important • cf. “Many Eyes”

12. An Example, Challenger Shuttle • With right visualization, insight (pattern) is obvious • Plot o-ring damage vs. temperature

13. Insight … • Some examples ….

14. A Classic Static Graphics Example • Napolean’s Russian campaign • N soldiers, distance, temperature – from Tufte

15. For what it’s worth … • x

16. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Visualization Pipeline:Mapping Data to Visual Form • Visualizations: • “adjustable mappings from data to visual form to human perceiver” • Series of data transformations • Multiple chained transformations • Human adjust the transformation • Entire pipeline comprises an information visualization F -1 F Visual Perception Interaction

17. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Visualization Stages • Data transformations: • Map raw data (idiosynchratic form) into data tables (relational descriptions including metatags) • Visual Mappings: • Transform data tables into visual structures that combine spatial substrates, marks, and graphical properties • View Transformations: • Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping F -1 F Visual Perception Interaction

18. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Information Structure • Visual mapping is starting point for visualization design • Includes identifying underlying structure in data, and for display • Tabular structure • Spatial and temporal structure • Trees, networks, and graphs • Text and document collection structure • Combining multiple strategies • Impacts how user thinks about problem - Mental model F -1 F Visual Perception Interaction

19. A “Taxonomy” of Visualization Space Physical Data 1D, 2D, 3D Multiple Dimensions, >3 Trees Networks Interaction Dynamic Queries Interactive Analysis Overview + Detail Focus + Context Fisheye Views Bifocal Lens Distorted Views Alternate Geometry • Data Mapping: Text • Text in 1D • Text in 2D • Text in 3D • Text in 3D + Time • Higher-Level Visualization • InfoSphere • Workspaces • Visual Objects

20. 1D Linear Data

21. 1D Linear Data

22. 2D Map Data

23. 3D World Data

24. Multiple Dimensions > 3 • “Straightforward” 1, 2, 3 dimensional representations • E.g., time and concrete • Can extend to more challenging n-dimensional representations • Which is at core of visualization challenges • E.g., Feiner et al., “worlds within worlds”

25. Temporal Data

26. Trees, Networks, and Graphs • Connections between /among individual entities • Most generally, a graph is a set edges connected by a set of vertices • G = V(e) • “Most general” data structure • Graph layout and display an area of iv • Trees, as data structure, occur … a lot • E.g., Cone trees

27. Tree/Hierarchical Data • Workspaces • The Information Visualizer: An Information Workspace by G. R. Robertson, S. K. Card, J. M. Mackinlay, 1991 CACM

28. Networks • E.g., network traffic data

29. Visualization of NSFNET • Cox, D. & Patterson, R., NCSA, 1992

30. Routes of the Internet, 1/15/05 • The opte project • Earlier snapshot in permanent collection of NY Museum of Modern Art

31. 3-d hyperbolic tree of web sites using Prefuse

32. Abstract – Non-physical • Concept map • Graph of “conceptual” information • From Berners-Lee’s proposal to CERN for what is now called www, March 1989 • Manual “graph drawing” http://www.nic.funet.fi/index/FUNET/history/internet/w3c/proposal.html

33. FYI - Demo • http://thejit.org/

34. Text and Document Collection Structure • Derivation of relationships upon which display is to be based a challenge • E.g., Wise et al

35. Text and Document Collection Structure, e.g., Galaxy of News • x

36. Overview Strategies • Typically useful, or critical, to have “feel” for all data • Then, allows closer inspection in “context” of all data • Overview + detail, focus + context • Known from the outset of visualization • Bifocal Lens • Database navigation: An Office Environment for the Professional by R. Spence and M. Apperley • Shneiderman mantra • “overview first, zoom and filter, details on demand”

37. Focus+Context: Fisheye Views, 1 • Detail + Overview • Keep focus, while remaining aware of context • Fisheye views • Physical, of course, also .. • A distance function. (based on relevance) • Given a target item (focus) • Less relevant other items are dropped from the display • Classic cover • New Yorker’s idea of the world

38. Focus+Context: Fisheye Views, 2 • Detail + Overview • Keep focus while remaining aware of context • Fisheye views • Physical, of course, also .. • A distance function. (based on relevance) • Given a target item (focus) • Less relevant other items are dropped from the display • Or, are just physically smaller – distortion

39. Focus + Context – Spatial Distortion • Selectively reduce complexity as f(user’s viewpoint) • Spatial distortion • Project network on distorted space • Viewing “lens”

40. Focus + Context – Spatial Distortion • Selectively reduce complexity as f(user’s viewpoint) • Spatial distortion • Project network on distorted space • Viewing “lens” • Seamless transition

41. Focus + Context – Hyperbolic View • Again, selectively reduce complexity as f(user’s viewpt.) • Smooth change during interaction

42. Focus + Context – Hyperbolic View • Also, in 3 space • Demo

43. 3-d hyperbolic tree of web sites using Prefuse

44. Tools

45. IBM’s Many Eyes • Multiple visualizations

46. IBM’s Many Eyes • Visualization types

47. IBM’s Many Eyes • Life expectancy vs. health care costs • http://manyeyes.alphaworks.ibm.com/manyeyes/visualizations/life-expectancy-vs-per-capita-annu

48. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Visualization Pipeline:Mapping Data to Visual Form • Visualizations: • “adjustable mappings from data to visual form to human perceiver” • Series of data transformations • Multiple chained transformations • Human adjust the transformation • Entire pipeline comprises an information visualization F -1 F Visual Perception Interaction

49. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Visualization Stages • Data transformations: • Map raw data (idiosynchratic form) into data tables (relational descriptions including metatags) • Visual Mappings: • Transform data tables into visual structures that combine spatial substrates, marks, and graphical properties • View Transformations: • Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping F -1 F Visual Perception Interaction

50. User - Task Raw Information Visual Form Dataset Views Visual Mappings View Transformations Data Transformations Information Structure • Visual mapping is starting point for visualization design • Includes identifying underlying structure in data, and for display • Tabular structure • Spatial and temporal structure • Trees, networks, and graphs • Text and document collection structure • Combining multiple strategies • Impacts how user thinks about problem - Mental model F -1 F Visual Perception Interaction