Information Visualization:See Patterns, Gain Insights & Make Decisions                     Ben Shneiderman                ...
Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH      (www.cs.umd.ed...
Design Issues•   Input devices & strategies     • Keyboards, pointing devices, voice     • Direct manipulation     • Menus...
Information Visualization•   Visual bandwidth is enormous    • Human perceptual skills are remarkable      • Trend, cluste...
Leader in Info Visualization
UVA              C MUUMinn            PA RC                            UMD          GA          Tech
Business takes action•   General Dynamics buys MayaViz•   Agilent buys GeneSpring•   Google buys Gapminder•   Oracle buys ...
Spotfire: Retinol’s role in embryos & vision
h ttp :/ re gis tration.s p otfire .com / val/ e fau lt_ e d u .as p        /                                e    d
10M - 100M pixels                            Large d is p lays                    for s ingle or m u ltip le u s e rs
100M-pixels & more
1M-pixels & less                   S m all m ob ile d e vice s
Information Visualization: Mantra•   Overview, zoom & filter, details-on-demand•   Overview, zoom & filter, details-on-dem...
Information Visualization: Data Types           •   1-D LinearSciViz .                                  Document Lens, See...
Anscombe’s Quartet          1                        2                    3                        4x             y       ...
Anscombe’s Quartet          1                        2                    3                        4x             y       ...
Anscombe’s Quartet
Temporal Data: TimeSearcher 1.3•   Time series     • Stocks     • Weather     • Genes•   User-specified      patterns•   R...
Temporal Data: TimeSearcher 2.0•   Long Time series (>10,000 time points)•   Multiple variables•   Controlled precision in...
LifeLines: Patient Histories       www.cs.umd.edu/hcil/lifelines
LifeLines2: Contrast+Creatine
LifeLines2: Align-Rank-Filter & Summarize
Treemap: Gene Ontology+ Space filling+ Space limited+ Color coding+ Size coding- Requires learning        (Shneiderman, AC...
Treemap: Smartmoney MarketMap         www.smartmoney.com/marketmap
Market falls steeply Feb 27, 2007, with one exception
Market falls steeply Sept 22, 2011, some exceptions
Market mixed, February 8, 2008Energy & Technology up, Financial & Health Care down
Market rises, September 1, 2010, Gold contrarians
Market rises, March 21, 2011, Sprint declines
Treemap: Newsmap (Marcos Weskamp)                     newsmap.jp
Treemap: Supply Chain                www.hivegroup.com
Treemap: Supply Chain         www.Centrifugesystems.com
Treemap: Spotfire Bond Portfolio Analysis                 www.spotfire.com
Treemap: NY Times – Car&Truck Sales        www.cs.umd.edu/hcil/treemap/
Treemap (Voronoi): NY Times - Inflationwww.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
State-of-the-art network visualization
Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & condition...
SocialAction•   Integrates statistics      & visualization•   4 case studies, 4-8 weeks      (journalist, bibliometrician,...
Footprints of Human A ctivity• Footprints in sand as Caesarea
NodeXL:    Network Overview for Discovery & Exploration in Excelwww.codeplex.com/nodexl
NodeXL:Network Overview for Discovery & Exploration in Excel            www.codeplex.com/nodexl
NodeXL: Import Dialogs  www.codeplex.com/nodexl
Tweets at #WIN09 Conference: 2 groups
WWW2010 Twitter Community
WWW2011 Twitter Community: Grouped
CHI2010 Twitter Community    www.cod e p le x.com / e xl/                          nod
Flickr clusters for “mouse”                         C omputer   Mickey                                A nimal
Flickr networks
‘GOP’ tweets, clustered (red-Republicans)
N o Location                    P h ilad e lp h ia                                                                  P ate ...
N o Location                     P h ilad e lp h ia  Innovation Clusters: People, Locations, Companies                    ...
Analyzing Social Media Networks with NodeXLI. Getting S tarted with A nalyzing S ocial Media Networks    1 . Introd u ctio...
Social Media Research FoundationR 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...
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 v...
29th Annual Symposium    May 23-24, 2012 www.cs.umd.edu/hcil
For More Information•   Visit the HCIL website for 400 papers & info on videos            www.cs.umd.edu/hcil•   Conferenc...
For More Information•   Treemaps     • HiveGroup: www.hivegroup.com     • Smartmoney: www.smartmoney.com/marketmap     • H...
Information Visualization: See Patterns, Gain Insights & Make Decisions
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Information Visualization: See Patterns, Gain Insights & Make Decisions

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MedStar Innovation Institute Conference (October 2011)

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  • "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.
  • Information Visualization: See Patterns, Gain Insights & Make Decisions

    1. 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. 2. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
    3. 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. 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. 5. Leader in Info Visualization
    6. 6. UVA C MUUMinn PA RC UMD GA Tech
    7. 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. 8. Spotfire: Retinol’s role in embryos & vision
    9. 9. h ttp :/ re gis tration.s p otfire .com / val/ e fau lt_ e d u .as p / e d
    10. 10. 10M - 100M pixels Large d is p lays for s ingle or m u ltip le u s e rs
    11. 11. 100M-pixels & more
    12. 12. 1M-pixels & less S m all m ob ile d e vice s
    13. 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. 14. Information Visualization: Data Types • 1-D LinearSciViz . 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, DataMontageInfoViz • 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. 15. Anscombe’s Quartet 1 2 3 4x y x y x y x y10.0 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 5.7613.0 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 8.8411.0 8.33 11.0 9.26 11.0 7.81 8.0 8.4714.0 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 12.5012.0 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. 16. Anscombe’s Quartet 1 2 3 4x y x y x y x y Property Value10.0 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 11.013.0 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 4.1211.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.81614.0 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 12.5012.0 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
    17. 17. Anscombe’s Quartet
    18. 18. Temporal Data: TimeSearcher 1.3• Time series • Stocks • Weather • Genes• User-specified patterns• Rapid search
    19. 19. Temporal Data: TimeSearcher 2.0• Long Time series (>10,000 time points)• Multiple variables• Controlled precision in match (Linear, offset, noise, amplitude)
    20. 20. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
    21. 21. LifeLines2: Contrast+Creatine
    22. 22. LifeLines2: Align-Rank-Filter & Summarize
    23. 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. 24. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap
    25. 25. Market falls steeply Feb 27, 2007, with one exception
    26. 26. Market falls steeply Sept 22, 2011, some exceptions
    27. 27. Market mixed, February 8, 2008Energy & Technology up, Financial & Health Care down
    28. 28. Market rises, September 1, 2010, Gold contrarians
    29. 29. Market rises, March 21, 2011, Sprint declines
    30. 30. Treemap: Newsmap (Marcos Weskamp) newsmap.jp
    31. 31. Treemap: Supply Chain www.hivegroup.com
    32. 32. Treemap: Supply Chain www.Centrifugesystems.com
    33. 33. Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com
    34. 34. Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/
    35. 35. Treemap (Voronoi): NY Times - Inflationwww.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
    36. 36. State-of-the-art network visualization
    37. 37. 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
    38. 38. 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
    39. 39. Footprints of Human A ctivity• Footprints in sand as Caesarea
    40. 40. NodeXL: Network Overview for Discovery & Exploration in Excelwww.codeplex.com/nodexl
    41. 41. NodeXL:Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
    42. 42. NodeXL: Import Dialogs www.codeplex.com/nodexl
    43. 43. Tweets at #WIN09 Conference: 2 groups
    44. 44. WWW2010 Twitter Community
    45. 45. WWW2011 Twitter Community: Grouped
    46. 46. CHI2010 Twitter Community www.cod e p le x.com / e xl/ nod
    47. 47. Flickr clusters for “mouse” C omputer Mickey A nimal
    48. 48. Flickr networks
    49. 49. ‘GOP’ tweets, clustered (red-Republicans)
    50. 50. 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 cyP 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 sWe s tingh ou s e E le ctric
    51. 51. 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 ncyP 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 sWe s tingh ou s e E le ctric
    52. 52. Analyzing Social Media Networks with NodeXLI. 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 isII. 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 ingIII 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
    53. 53. Social Media Research FoundationR 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 ipM ap , m e as u re & u nd e rs tand s ocial m e d iaS 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
    54. 54. 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
    55. 55. 29th Annual Symposium May 23-24, 2012 www.cs.umd.edu/hcil
    56. 56. 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
    57. 57. 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

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