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Information Visualization INFORMS Roundtable Ben Shneiderman Founding Director (1983-2000), Human-Computer Interaction Lab Professor,

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Presentation on theme: "Information Visualization INFORMS Roundtable Ben Shneiderman Founding Director (1983-2000), Human-Computer Interaction Lab Professor,"— Presentation transcript:

1 Information Visualization INFORMS Roundtable Ben Shneiderman Founding Director ( ), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institutes for Advanced Computer Studies & Systems Research

2 Human-Computer Interaction Laboratory Interdisciplinary research community - Computer Science & Psychology - Information Studies & Education

3 User Interface Design Goals Cognitively comprehensible: Consistent, predictable & controllable Affectively acceptable: Mastery, satisfaction & responsibility NOT: Adaptive, autonomous & anthropomorphic

4 Scientific Approach (beyond user friendly) Specify users and tasks Specify users and tasks Predict and measure Predict and measure time to learntime to learn speed of performancespeed of performance rate of human errorsrate of human errors human retention over timehuman retention over time Assess subjective satisfaction (Questionnaire for User Interface Satisfaction) Assess subjective satisfaction (Questionnaire for User Interface Satisfaction) Accommodate individual differences Accommodate individual differences Consider social, organizational & cultural context Consider social, organizational & cultural context

5 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 & communities Manuals, tutorials, training usableweb.com hcibib.org useit.com

6 Library of Congress Scholars, Journalists, Citizens Scholars, Journalists, Citizens Teachers, Students Teachers, Students

7 Visible Human Explorer (NLM) Doctors Doctors Surgeons Surgeons Researchers Researchers Students Students

8 NASA Environmental Data Scientists Scientists Farmers Farmers Land planners Land planners Students Students

9 Bureau of Census Economists, Policy makers, Journalists Economists, Policy makers, Journalists Teachers, Students Teachers, Students

10 NSF Digital Government Initiative Find what you need Find what you need Understand what you Find Understand what you Find UMd & UNC

11 Session 1: The Case for Information Visualization Seven types (1-, 2-, 3-, multi-dimensional data, temporal, tree and network data) Seven user tasks (overview, zoom, filter, details-on-demand, relate, history, and extract) Direct manipulation Dynamic queries

12 Session 2: Structured data Multidimensional and multivariate data Temporal data visualization Hierarchical and tree structured data Network information visualization

13 Session 3: User controls Zooming interfaces Focus+Context vs Overview+Detail Large Screen High Resolutions Displays 2D versus 3D desktops & workspaces Coordination of visualizations Other Challenges

14 Information Visualization The eye… the window of the soul, is the principal means by which the central sense can most completely and abundantly appreciate the infinite works of nature. Leonardo da Vinci ( )

15 Information Visualization Compact graphical presentation AND user interface for manipulating large numbers of items ( ), possibly extracted from far larger datasets. Enables users to make discoveries, decisions, or explanations about patterns (trend, cluster, gap, outlier...), groups of items, or individual items.

16 Information Visualization: Using Vision to Think Visual bandwidth is enormous Human perceptual skills are remarkable Trend, cluster, gap, outlier... Color, size, shape, proximity... Human image storage is fast and vast Opportunities Spatial layouts & coordination Information visualization Scientific visualization & simulation Telepresence & augmented reality Virtual environments

17 Information Visualization: US Research Centers Xerox PARC 3-D cone trees, perspective wall, spiral calendar table lens, hyperbolic trees, document lens, butterfly Univ. of Maryland dynamic queries, range sliders, starfields, treemaps zoombars, tight coupling, dynamic pruning, lifelines IBM Yorktown, AT&T-Lucent Technologies Georgia Tech, MIT Media Lab Univ. of Wisconsin, Minnesota, Calif-Berkeley Pacific Northwest National Labs

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19 Which of my high-spending customers are most profitable? Which customers should I target for cross-sell/up-sell?

20

21 Dynamic Queries: HomeFinder

22 Information Visualization: Mantra Overview, zoom & filter, details-on-demand

23 FilmFinder

24

25

26

27 Dynamap: Choropleth maps

28 Dynamap: Choropleth maps

29

30 Influence Explorer Tweedie, Spence et al. CHI 96

31 Information Visualization: Data Types 1-D Linear Document Lens, SeeSoft, Info Mural, Value Bars 2-D Map GIS, ArcView, PageMaker, Medical imagery 3-D World CAD, Medical, Molecules, Architecture Multi-Dim Parallel Coordinates, Spotfire, XGobi, Visage, Influence Explorer, TableLens, DEVise Temporal Perspective Wall, LifeLines, Lifestreams, Project Managers, DataSpiral Tree Cone/Cam/Hyperbolic, TreeBrowser, Treemap Network Netmap, netViz, SeeNet, Butterfly, Multi-trees (Online Library of Information Visualization Environments) otal.umd.edu/Olive

32 Overview Gain an overview of the entire collection Zoom Zoom in on items of interest Zoom Zoom in on items of interest Filter Filter out uninteresting items Filter Filter out uninteresting items Details-on-demand Select an item or group and get details when needed Details-on-demand Select an item or group and get details when needed Relate View relationships among items Relate View relationships among items History Keep a history of actions to support undo, replay, and progressive refinement History Keep a history of actions to support undo, replay, and progressive refinement Extract Allow extraction of sub-collections and of the query parameters Information Visualization: Tasks

33 Information Visualization: Design Guidelines Direct manipulation strategies Visual presentation of query components Visual presentation of results Rapid, incremental and reversible actions Selection by pointing (not typing) Immediate and continuous feedback Reduces errors Encourages exploration

34 Visual Information Seeking: Design Principles Dynamic queries Visual query formulation and immediate output Rapid, incremental and reversible actions Sliders, buttons, selectors Starfield display Complete overview: ordinal & categorical variables as axes Colored points of light reveal patterns Zoom bars to focus attention Tight coupling to preserve display invariants No errors Output becomes input Details-on-demand

35 Session 2: Structured data Multidimensional and multivariate data Temporal data visualization Hierarchical and tree structured data Network information visualization

36 Parallel Coordinates One vertical bar per dimension Each point becomes a set of connected lines True multidimensional technique Needs powerful interface for filtering, marking, coloring Powerful technique but long learning period

37 Parallel Coordinates (Parallax-Inselberg)

38 TableLens/Eureka and Infozoom Two compact views of tables Learned easily TableLens: local enlargement of areas of interest, creation of subtables Infozoom: shows distributions, allows progressive filtering Different orientation (vertical/horizontal)

39 TableLens/Eureka

40 InfoZoom

41 InfoZoom

42 Temporal data visualization :LifeLines Parallel lines color/size coded & grouped in categories Zooming or hierarchical browsing allows focus+context Examples Youth histories & medical records Personal resumes, student records & performance reviews Challenges Aggregation & alerts Overview & detail views Easy import & export Labeling (Plaisant et al., CHI96)

43 LifeLines

44 Lifelines: Customer records Temporal data visualization Medical patient histories Medical patient histories Customer relationship management Customer relationship management Legal case histories Legal case histories

45 Perspective wall (Xerox Parc) Mackinlay et al, CHI91

46 TimeSearcher

47 Hierarchical data Hierarchical data Challenge: understand relationships without getting lost Challenge: understand relationships without getting lost Explicit vs. implicit depictions of trees Explicit vs. implicit depictions of trees Connections vs. containmentsConnections vs. containments Size limitations – breadth and depth Size limitations – breadth and depth Tree Visualizations

48 Tree Visualization Toolkits

49 Spacetree + Familiar & animated + Space limited + Focuses attention -Requires some learning

50 Hyperbolic trees Visually appealing Visually appealing Space limited Space limited 2-level look-ahead 2-level look-ahead Easy affordances Easy affordances Hard to scan Hard to scan Poor screen usage Poor screen usage Too volatile Too volatile Lamping et al. CHI 95

51 Startree Toolkit

52 CamTree - ConeTree Xerox PARC

53 Treemap - view large trees with node values +Space filling +Space limited +Color coding +Size coding -Requires learning TreeViz (Mac, Johnson, 1992) NBA-Tree(Sun, Turo, 1993) Winsurfer (Teittinen, 1996) Diskmapper (Windows, Micrologic) Treemap97 (Windows, UMd) Treemap 3.0 (Java)

54

55 Treemap - Stock market, clustered by industry

56 Treemap – Product catalogs

57 Treemap – Monitoring

58 Million-Item Treemap

59 GRIDL – Hierarchical Axes

60 Network Visualization Arbitrarily connected items Nodes-links-paths-clusters Problems Layout as size grows Clutter vs clusters External relationships Geography Taxonomies Entrieva SemioMap NetMap

61 Web Browsing

62 Communication Networks

63 Enterprise Networks

64 Treemap – Directed, Acyclic Graphs

65 Session 3: User controls Zooming interfaces Focus+Context vs Overview+Detail Large Screen High Resolutions Displays 2D versus 3D desktops & workspaces Coordination of visualizations Other Challenges

66 Fisheye views & Zooming User Interfaces Distortion to magnify areas of interest User-control, zoom factors of 3-5 Multi-scale spaces Zoom in/out & Pan left/right Smooth zooming Semantic zooming Overviews + details-on-demand Stasko, GATech

67 GlassEye – Zooming Exploration (see Hochheiser paper )

68 DateLens

69 PhotoMesa

70 PhotoFinder

71 Snap-Together Visualization Allow coordination designers to create novel layouts that combine existing visualizations Allow users to navigate large datasets conveniently

72 Snap in Java – with Builder snap.cs.vt.edu

73 Hierarchical Clustering Explorer

74 High-Resolution, Wall-Size Displays graphics.stanford.edu/~francois/

75 Spectrum of 3-D Visualizations Immersive Virtual Environment with head-mounted stereo display and head tracking Desktop 3-D for 3-D worlds medical, architectural, scientific visualizations Desktop 3-D for artificial worlds Bookhouse, file-cabinets, shopping malls Desktop 3-D for information visualization cone/cam trees, perspective wall, web-book SGI directories, Visible Decisions, Media Lab landscapes XGobi scatterplots, Themescape, Visage Chartjunk 3-D: barcharts, piecharts, histograms

76 Themescape Wise et al., see also

77 Starlight Battelle – Pacific Northwest National Lab

78 Mineset

79 WebBook-WebForager Card, Robertson, George and York, CHI 96

80 Microsoft: Task Gallery research.microsoft.com/ui/TaskGallery/

81 Clockwise3d

82 Clockwise3d

83 Other challenges: Labeling Excentric Labeling Excentric Labeling

84 Other challenges: Universal Usability Helping novice users get started 508 / disabled users Range of devices, network speed, etc.

85 Human-Computer Interaction Laboratory 20 th Anniversary Open House May 29-30, 2003

86 For More Information Visit the HCIL website for 300 papers & info on videos (www.cs.umd.edu/hcil) Visit the HCIL website for 300 papers & info on videos (www.cs.umd.edu/hcil) See Chapter 15 on Info Visualization Shneiderman, B., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Third Edition (1998) (www.awl.com/DTUI) See Chapter 15 on Info Visualization Shneiderman, B., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Third Edition (1998) (www.awl.com/DTUI) Book of readings: Card, S., Mackinlay, J., and Shneiderman, B. Information Visualization: Using Vision to Think (1999) Book of readings: Card, S., Mackinlay, J., and Shneiderman, B. Information Visualization: Using Vision to Think (1999)


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