Information Visualization: See Patterns, Gain Insights & Make Decisions
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MedStar Innovation Institute Conference (October 2011)
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Information Visualization: See Patterns, Gain Insights & Make Decisions
- 1. Information Visualization: See Patterns, Gain Insights & Make Decisions Ben Shneiderman ben@cs.umd.edu @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies University of Maryland College Park, MD 20742
- 2. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
- 3. Design Issues • Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands • Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help • Collaboration & Social Media www.awl.com/DTUI Fifth E dition: 2010 • Help, tutorials, training • Search • Vis u alization
- 4. Information Visualization • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery
- 5. Leader in Info Visualization
- 6. UVA C MU UMinn PA RC UMD GA Tech
- 7. Business takes action • General Dynamics buys MayaViz • Agilent buys GeneSpring • Google buys Gapminder • Oracle buys Hyperion • Microsoft buys Proclarity • InfoBuilders buys Advizor Solutions • SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports ) • IBM buys (Cognos buys Celequest) & ILOG • TIBCO buys Spotfire
- 8. Spotfire: Retinol’s role in embryos & vision
- 9. h ttp :/ re gis tration.s p otfire .com / val/ e fau lt_ e d u .as p / e d
- 10. 10M - 100M pixels Large d is p lays for s ingle or m u ltip le u s e rs
- 12. 1M-pixels & less S m all m ob ile d e vice s
- 13. Information Visualization: Mantra • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand
- 14. Information Visualization: Data Types • 1-D Linear SciViz . Document Lens, SeeSoft, Info Mural • 2-D Map GIS, ArcView, PageMaker, Medical imagery • 3-D World CAD, Medical, Molecules, Architecture • Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords, • Temporal LifeLines, TimeSearcher, Palantir, DataMontage InfoViz • Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap • Network Pajek, JUNG, UCINet, SocialAction, NodeXL infosthetics.com flowingdata.com infovis.org www.infovis.net/index.php?lang=2
- 15. 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
- 16. Anscombe’s Quartet 1 2 3 4 x y x y x y x y Property Value 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Mean of x 9.0 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Variance of x 11.0 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Mean of y 7.5 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 Variance of y 4.12 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.816 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Linear regression y = 3 + 0.5x 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
- 18. Temporal Data: TimeSearcher 1.3 • Time series • Stocks • Weather • Genes • User-specified patterns • Rapid search
- 19. Temporal Data: TimeSearcher 2.0 • Long Time series (>10,000 time points) • Multiple variables • Controlled precision in match (Linear, offset, noise, amplitude)
- 20. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
- 22. LifeLines2: Align-Rank-Filter & Summarize
- 23. Treemap: Gene Ontology + Space filling + Space limited + Color coding + Size coding - Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/
- 24. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap
- 25. Market falls steeply Feb 27, 2007, with one exception
- 26. Market falls steeply Sept 22, 2011, some exceptions
- 27. Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down
- 28. Market rises, September 1, 2010, Gold contrarians
- 29. Market rises, March 21, 2011, Sprint declines
- 30. Treemap: Newsmap (Marcos Weskamp) newsmap.jp
- 31. Treemap: Supply Chain www.hivegroup.com
- 32. Treemap: Supply Chain www.Centrifugesystems.com
- 33. Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com
- 34. Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/
- 35. Treemap (Voronoi): NY Times - Inflation www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
- 38. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information
- 39. SocialAction • Integrates statistics & visualization • 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst) • Identified desired features, gave strong positive feedback about benefits of integration www.cs.umd.edu/hcil/socialaction P e re r & S h ne id e rm an, C H I2008, IE E E C G &A 2009
- 40. Footprints of Human A ctivity • Footprints in sand as Caesarea
- 41. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 42. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 43. NodeXL: Import Dialogs www.codeplex.com/nodexl
- 44. Tweets at #WIN09 Conference: 2 groups
- 46. WWW2011 Twitter Community: Grouped
- 47. CHI2010 Twitter Community www.cod e p le x.com / e xl/ nod
- 48. Flickr clusters for “mouse” C omputer Mickey A nimal
- 49. Flickr networks
- 50. ‘GOP’ tweets, clustered (red-Republicans)
- 51. N o Location P h ilad e lp h ia P ate nt Te ch N avy S BIR (fe d e ral) P A D C E D (s tate ) R e late d p ate nt 2: F e d e ral age n cy P h arm ace u tical/ e d ical M 3: E nte rp ris e P itts b u rgh M e tro 5: Inve ntors 9: U nive rs itie s 1 0: P A D C E D 1 1 / 2: P h il/ itt m e tro cn ty 1 P 1 3-1 5: S e m i-ru ral/ ral cnty ru 1 7: F ore ign co u ntrie s 1 9: O th e r s tate s We s tingh ou s e E le ctric
- 52. N o Location P h ilad e lp h ia Innovation Clusters: People, Locations, Companies P ate nt Te ch N avy S BIR (fe d e ral) P A D C E D (s tate ) R e late d p ate nt 2: F e d e ral age ncy P h arm ace u tical/ e d ical M 3: E nte rp ris e P itts b u rgh M e tro 5: Inve ntors 9: U nive rs itie s 1 0: P A D C E D 1 1 / 2: P h il/ itt m e tro cnty 1 P 1 3-1 5: S e m i-ru ral/ ral cnty ru 1 7: F ore ign co u ntrie s 1 9: O th e r s tate s We s tingh ou s e E le ctric
- 53. Analyzing Social Media Networks with NodeXL I. Getting S tarted with A nalyzing S ocial Media Networks 1 . Introd u ction to S ocial M e d ia and S ocial N e tworks 2. S ocial m e d ia: N e w Te ch nologie s of C ollab oration 3. S ocial N e twork Analys is II. NodeXL Tutorial: Learning by Doing 4. Layou t, Vis u al D e s ign & Lab e ling 5. C alcu lating & Vis u alizing N e twork M e trics 6. P re p aring D ata & F ilte ring 7. C lu s te ring &G rou p ing III S ocial Media Network A nalys is C as e S tudies 8. E m ail 9. Th re ad e d N e tworks 1 0. Twitte r 1 1 . F ace b ook 1 2. WWW 1 3. F lickr 1 4. You Tu b e 1 5. Wiki N e tworks www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
- 54. Social Media Research Foundation R e s e arch e rs wh o want to - cre ate op e n tools - ge ne rate & h os t op e n d ata - s u p p ort op e n s ch olars h ip M ap , m e as u re & u nd e rs tand s ocial m e d ia S u p p ort tool p roj cts to e colle ction, analyze & vis u alize s ocial m e d ia d ata. smrfoundation.org
- 55. UN Millennium Development Goals To b e ach ie ve d b y 201 5 • E rad icate e xtre m e p ove rty and h u nge r • Ach ie ve u nive rs al p rim ary e d u cation • P rom ote ge nd e r e qu ality and e m p owe r wom e n • R e d u ce ch ild m ortality • Im p rove m ate rnal h e alth • C om b at H IV/ S , m alaria and oth e r d is e as e s AID • E ns u re e nvironm e ntal s u s tainab ility • D e ve lop a glob al p artne rs h ip for d e ve lop m e nt
- 56. 29th Annual Symposium May 23-24, 2012 www.cs.umd.edu/hcil
- 57. For More Information • Visit the HCIL website for 400 papers & info on videos www.cs.umd.edu/hcil • Conferences & resources: www.infovis.org • See Chapter 14 on Info Visualization Shneiderman, B. and Plaisant, C., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Fifth Edition (2010) www.awl.com/DTUI • Edited Collections: Card, S., Mackinlay, J., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think Bederson, B. and Shneiderman, B. (2003) The Craft of Information Visualization: Readings and Reflections
- 58. For More Information • Treemaps • HiveGroup: www.hivegroup.com • Smartmoney: www.smartmoney.com/marketmap • HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap • Spotfire: www.spotfire.com • TimeSearcher: www.cs.umd.edu/hcil/timesearcher • NodeXL: nodexl.codeplex.com • Hierarchical Clustering Explorer: www.cs.umd.edu/hcil/hce • LifeLines2: www.cs.umd.edu/hcil/lifelines2 • Similan: www.cs.umd.edu/hcil/similan
Notas do Editor
- "The IN Cell Analyzer automated microscope was used to identify proteins influencing the division of human cells. After the images were analyzed, quantitative results were transferred to Spotfire DecisionSite. This screen revealed the previously unknown involvement of the retinol binding protein RBP1 in cell cycle control.(Stubbs S, & Thomas N. 2006 Methods in Enzymology; 414:1-21.) Retinol a form of Vitamin A plays a crucial role in vision and during embryonic development"
- Contrast and Creatinine dataset In some diagnostic radiology procedures, patients are injected contrast material. However, some patients develop adverse side effects to the contrast material. One serious side effect is renal failure, which is detected by high creatinine levels in a patient's blood. This adverse effect usually occur within two weeks after the radiology contrast. WHC is interested in finding the proportion of patients who exhibit this condition in historical records. Screenshots 1-aligned-ranked.png: We align by the 1st occurrence of radiology contrast and rank by the number of creatinine high (CREAT-H) events to bring the most severe patients to the top. We realize two things: (1) some patients have more than 1 "Radiology Contrast" events, and (2), some patients have consistently high creatinine readings (chronic kidney failure). 2-aligned(all)-distribution-selected.png We align by all occurrences of raiology contrast, and then show the temporal summary of CREAT-H events. The patients are presented in 4 exclusive sets in the summary: those who have CREAT-H only before alignment, only after alignment, both before and after, and neither. We then select from the "only after" summary the patients who have at least one CREAT-H event within 2 weeks of any "Radiology Contrast" event. There are 421 patients.
- Live Demonstration
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
- Figure 13.20. NodeXL cluster visualization showing three Flickr tag clusters, each representing a different context for “mouse”. Figure 13.21. NodeXL display of Isolated clusters for three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse Disney character.
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.