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![INFORMATION VISUALIZATION
INFO. VIS.
“ Using visual representations and interaction techniques,
which take advantage of the human eye’s
broad bandwidth pathway into the mind,
to allow users to see, explore, and understand
large amounts of information at once.”
[Wikipedia]](https://image.slidesharecdn.com/kristwinfovisintroexport2-120918105300-phpapp02/85/Information-Visualization-for-Knowledge-Discovery-12-320.jpg)
Uploaded byKrist Wongsuphasawat
Information Visualization for Knowledge Discovery
This document discusses information visualization and its uses for knowledge discovery through visual representations of data and user interactions. It provides examples of visualizations of different data types, such as maps, networks, temporal data. Visualizations can benefit data analysis by helping detect patterns and trends, and aid presentation by helping communicate information. However, they also carry drawbacks if used to mislead. The document promotes visualization tools like ManyEyes for collaborative data analysis.
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Information Visualization for Knowledge Discovery
- 1. INFORMATION VISUALIZATION For knowledgediscovery Krist wongsuphasawat @kristwongz Computer Engineering, Chulalongkorn University (Intania87, CP30) Dept. of Computer Science & Human-Computer Interaction Lab, Univ. of Maryland Data Visualization Scientist, Twitter
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- 9. INFORMATION VISUALIZATION INFO. VIS. Datavisualization visualization
- 10. A picture isworth a thousand words.
- 11. INFORMATION VISUALIZATION Data picture!
- 12. INFORMATION VISUALIZATION INFO. VIS. “ Using visual representations and interaction techniques, which take advantage of the human eye’s broad bandwidth pathway into the mind, to allow users to see, explore, and understand large amounts of information at once.” [Wikipedia]
- 13. INFORMATION VISUALIZATION Visual representation
- 14. Anscombe s quartet #1 #2 #3 #4 X Y X Y X Y X Y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
- 15. Anscombe s quartet #1 #2 #3 #4 Property Value Mean of X 11.0 Variance of X 10.0 Mean of Y 7.5 Variance of Y 3.75 Correlation between X and Y 0.816 Linear regression y = 3.0 +0.5x Identical statistics
- 16. Anscombe s quartet #1 #2 #3 #4 12! 10! 14! 14! 9! 10! 12! 12! 8! 7! 10! 10! 8! 6! 8! 8! 6! 5! 4! 6! 6! 4! 3! 4! 4! 2! 2! 2! 2! 1! 0! 0! 0! 0! 0! 5! 10! 15! 0! 5! 10! 15! 0! 5! 10! 15! 0! 10! 20! But very different
- 17. map of napoleon’smarch minard, 1812 • geography • course • time • direction • temperature • quantity of troops
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- 21. toys boys girls
- 22. INFORMATION VISUALIZATION Visual representation + User interactions click, drag, zoom, select, change color, etc.! !
- 23. INFORMATION VISUALIZATION =Mantra= Overview first Zoom and filter Details on demand
- 24. Stock market Map ofthe market
- 25. size = marketcap Stock A Stock B
- 26. size = marketcap Stock A Stock B color = price change
- 27. Map of theMarket
- 28. Market!falls!steeply!Feb!27,!2007,!! with!one!excep:on! Market falls 311 points July 26, 2007, with a few exceptions
- 29. Market falls 311points July 26, 2007, with a few exceptions
- 30. Market mixed, October22, 2007
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- 34. Disk inventory x http://www.derlien.com/
- 35. Social network relationships http://www.cs.umd.edu/hcil/socialaction
- 36. Person A Person B
- 37. Has a relationship PersonA Person B
- 38. Strong relationship Person A Person B
- 39. Strong relationship Person A Person B
- 40. Person A Person B
- 42. Video demo SocialAction
- 43. How people areconnected on facebook by country h;p://www.facebookstories.com/stories/1574/#color=con:nent&story=1&country=US!
- 44. How people areconnected on facebook by country h;p://www.facebookstories.com/stories/1574/#color=con:nent&story=1&country=US!
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- 47. Interactive charts Baby name voyager http://www.babynamewizard.com/voyager
- 48. temporal Data time time time
- 49. event! event! event! event! event! event! event! event! LIFE event! event! event! event! event! event! event! event!
- 50. Time Event type! ( 7:00 am, Wake up ) event! event! event! event! event! event! event! event! LIFE event! event! event! event! event! event! event! event!
- 51. event! event! event! event! event! event! event! event! LIFE event! event! event! event! event! event! event! event! “Event Sequence”
- 52. Electronic health records (7:00 am, Wake up ) ( 7:10 am, Shower ) ( 7:30 am, Breakfast )
- 53. Electronic health records (8:40 am, Arrival ) ( 8:50 am, ER) ( 9:15 am, ICU )
- 54. Event Sequences Medical Transportation Sports Education Web logs Logistics and more…
- 55. Patient transfers ARRIVAL Arrive the hospital EMERGENCY Emergency room ICU Intensive Care Unit FLOOR Normal room EXIT-ALIVE Leave the hospital alive EXIT-DEAD Leave the hospital dead
- 56. Save more lives! Pa:ent!ID:!45851733 !! Pa:ent!ID:!45851732 !! 12/02/2008!14:26 !Arrival! Pa:ent!ID:!45851731 !! 12/02/2008!14:26 !Emergency! 12/02/2008!14:26 !Arrival! Emergency Department 6,000+ 12/02/2008!22:44 !ICU! 12/02/2008!14:26 !Emergency! 12/02/2008!14:26 !Arrival! 12/05/2008!05:07 !Floor! 12/02/2008!22:44 !ICU! 12/02/2008!14:26 !Emergency! 12/08/2008!10:02 !Floor! 12/05/2008!05:07 !Floor! 12/02/2008!22:44 !ICU! 12/14/2008!06:19 !Discharge! 12/08/2008!10:02 !Floor! 12/05/2008!05:07 !Floor! ! 12/14/2008!06:19 !Discharge! 12/08/2008!10:02 !Floor! ! 12/14/2008!06:19 !Discharge! patients per month !
- 57. What happened tothe patients? Arrival ? ICU ? ?
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- 62. Video demo Data Analysiswith LifeFlow
- 63. INFORMATION VISUALIZATION Visual representation + User interactions click, drag, zoom, select, change color, etc.! !
- 64. INFORMATION VISUALIZATION classified by Network Data types 1D: Linear Tree 2D: Map Temporal 3D: Molecules, Biology Multi dimensional (4+ D)
- 65. Benefits of INFORMATION VISUALIZATION DATAANALYSIS PRESENTATION Detect pattern, trend, outliers! Communicate!
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- 71. DATA INFORMATION VISUALIZATION Visual representation + User interactions DATA ANALYSIS PRESENTATION Detect pattern, trend, outliers! Communicate! Krist wongsuphasawat krist.wongz@gmail.com @kristwongz http://kristw.yellowpigz.com
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- 73. DATA INFORMATION VISUALIZATION Visual representation + User interactions DATA ANALYSIS PRESENTATION Detect pattern, trend, outliers! Communicate! Krist wongsuphasawat krist.wongz@gmail.com @kristwongz http://kristw.yellowpigz.com