Information Visualization Part 1

1. ITM 734 Fall 2005 Information Visualization Part 1 Dr. Cindy Corritore Creighton University

2. principles of good graphics (Tufte) • data graphics should draw viewers attention to the substance and meaning of the data, not to something else • goal: help user reason about the data • relative rather than absolute judgements Principle 1: above all else, always show the data! Corritore, 2005

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4. principles of good graphics • chartjunk - non-data ink, decoration, over-redundancy • moire vibration - appearance of movement • grid - remove or mute • unnecessary 3-D Principle 2: remove chart junk Corritore, 2005

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7. principles of good graphics graphic content consists of: data ink and non-data ink • data ink is non-erasable core of graphic • text can be data ink • get rid of the rest as much as possible Corritore, 2005

8. principles of good graphics Data-Ink Ratio: data ink / total ink used Principle 3: maximize the data-ink ratio, within reason (erase non-data ink as much as possible) Corritore, 2005

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12. principles of good graphics • redundancy can go too far • bilateral symmetry - can reduce • have double redundancy - people just process first half anyways, then check to see other half is the same Principle 4: erase redundant data ink, within reason Corritore, 2005

13. What can be erased (redundant)? Corritore, 2005

14. Word ‘Year’ ’19’ Data labels on left or in columns No color or no borders Grid lines Corritore, 2005

15. principles of good graphics • proximity principle - integrate text and graphics • but be careful … Principle 5: integrate text and graphics, when possible. Corritore, 2005

16. We don’t estimate volume and area well – back barrel is much larger than actual 30% growth. Corritore, 2005

17. This is much better - Corritore, 2005

18. principles of good graphics • know a problem if you have to talk yourself through it “let’s see, if it is yellow, it is …” • often involve color as we don’t give visual ordering to colors • use varying shades of gray - see order better Principle 6: keep it simple and understandable to audience Corritore, 2005

19. Two charts of the same data (linguistic ability of Canadians correlated with primary language) Corritore, 2005

20. overall • focus on the data, not the chart elements • emphasize the important (not the unimportant)! Corritore, 2005

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22. problem …. Everyone spoke of an information overload, but what there was in fact was a non-information overload. Richard Saul Wurman, What-If, Could-Be (Philadelphia, 1976) Corritore, 2005

23. overview • increasingly common to actually have all of the data potentially available • how to map and use it becomes harder and harder • challenges: world of the computer and data and world of the human • bridge between the intuitive, creative, experience and the digital, analytical Solution: Involve the user! corritore, 734

24. challenges Corritore, 2005

25. challenge 1 1. growing volume of data with declining information content • provision of data ever cheaper and available • our ability to consume information largely unchanged • Key Issues: exploring, navigation, browsing, immersion/involvement of human and their perceptional apparatus Corritore, 2005

26. challenge 1 • interactive visualization interface for exploration of network fault data (network alarm data) • experienced network administrator looks for trends/patterns • interactive with filters Corritore, 2005

27. challenge 1 • large information spaces Corritore, 2005

28. challenge 2 2. convert appropriate data to relevant data: analysis and interpretation • summarize and compress without signif. loss of content • complex data analysis tools and models for analysis hard to use • goal: human involvement in processing and analysis of data • experience and intuition Corritore, 2005

29. challenge 2 • visual interface to model that assess customer perception of phone connections • 12 input parameters specifying circuit • user explores performance as a func. of any two parameters Corritore, 2005

30. challenge 2 • visual correlation between lightning strikes & network alarms • time series movie Corritore, 2005

31. challenge 3 • managing abstract problems/intangibles against increasingly short timescales • build a building - can see the progress; intangibles hard to visualize • better informed decisions • goal: retain overview of abstract problem while providing for immediate visibility of changes Corritore, 2005

32. challenge 3 • software development • each sphere a module (diameter - size) • lines are func. calls • change requests mapped to rate of spin Corritore, 2005

33. challenge 3 • five releases showing selected metrics • most recent @ top • points modules • as evolves, see changes in system • perhaps spikes overly convoluted modules Corritore, 2005

34. challenge 4 • communicate a vision - wide audience and increasingly conceptual • wider, less specialist audience; mix of technical, business, customer • hence, must provide a shared experience picture is worth 1,000 words Corritore, 2005

35. Goal: let human observe, manipulate, search, navigate, explore, filter, discover, understand, and interact with large volumes of data rapidly Corritore, 2005

36. shneiderman • King of Direct Manipulation • mantra: overview first, zoom and filter, details on demand Corritore, 2005

37. data types • 1D • lists, words • http://www.textarc.org/Alice2inWindow.html - Alice in Wonderland • fisheye – next week • 2D • map data (gis) • google earth (demo) • smartmoney.com - http://www.smartmoney.com/maps/?nav=dropTab Corritore, 2005

38. data types • 3D • scientific visualization (molecules, etc) • ThemeView - http://in-spire.pnl.gov/IN-SPIRE_Help/galaxy.html - shows documents and their relationships • galaxy view • themeview • task manager – • Digital library prototype http://student.ifs.tuwien.ac.at/~andi/libViewer/ Corritore, 2005

39. data types • 3D and file systems Corritore, 2005

40. data types • multi-dimensional • n-dimensional space – examples? • spotfire • temporal • time lines (stock markets, health care) Corritore, 2005

41. data types • temporal • variables over time • metaphors • River metaphor: Each attribute is mapped to a “current” in the “river”, flowing along the timeline Current width ~= strength of theme River width ~= global strength Color mapping (similar themes – same color family) Time line Corritore, 2005

42. A company’s patent activity Corritore, 2005

43. extended exploration Linking a river to a histogram Comparing two rivers Corritore, 2005

44. critique Strong points: • Intuitive exploration of temporal changes and relations • Evalutation + improvements • Applicable to general attributes Weak points: • Limited number of themes / attributes • Interpolated values / outer attributes misleading • No ability to reorder currents • Performance issues Corritore, 2005

45. spiral Example • Spokes (months) and spiral guide lines (years) • Planar spiral • Distinguishable patterns (rainy season / 1984) Chimpanzees Monthly food consumption 1980-1988 Corritore, 2005

46. data types • temporal – • Time Searcher (http://www.cs.umd.edu/hcil/timesearcher/videos/ts2_HCILsoh2005R.html) – movie • lifelines - http://www.cs.umd.edu/hcil/lifelines/latestdemo/chi.html Corritore, 2005

47. data types • trees • hierarchies (file structure) • magnifind • lexusnexus - http://www.lexis-nexis.com/lncc/hyperbolic/default.htm • Cop - http://ai.bpa.arizona.edu/COPLINK/demo/Visualization.htm • Visual Thesauru Corritore, 2005

48. data types • network – look at these next week Corritore, 2005

49. challenges • multiple data input • combine visual and text • show relationships • large information spaces – overview then details • collaboration? • navigation must be accurate • all elements must be interactive • new paradigms …… Corritore, 2005