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

2. Corritore, 2005 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!

3. Corritore, 2005

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

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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

8. Corritore, 2005 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)

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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

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

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

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

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

17. Corritore, 2005 This is much better -

18. Corritore, 2005 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

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

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

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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)

23. corritore, 734 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!

24. Corritore, 2005 challenges

25. Corritore, 2005 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

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

27. Corritore, 2005 challenge 1 large information spaces

28. Corritore, 2005 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

29. Corritore, 2005 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

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

31. Corritore, 2005 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

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

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

34. Corritore, 2005 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

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

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

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

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

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

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

41. Corritore, 2005 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

42. Corritore, 2005 A company’s patent activity

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

44. Corritore, 2005 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

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

46. Corritore, 2005 data types temporal – –Time Searcher (http://www.cs.umd.edu/hcil/timesearcher/vide os/ts2_HCILsoh2005R.html) – moviehttp://www.cs.umd.edu/hcil/timesearcher/vide os/ts2_HCILsoh2005R.html –lifelines - http://www.cs.umd.edu/hcil/lifelines/latestdem o/chi.html http://www.cs.umd.edu/hcil/lifelines/latestdem o/chi.html

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

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

49. Corritore, 2005 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 ……