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Information Visualization - Origins, Visualizer, and Toolbox

This lecture covers the origins of information visualization, the use of the information visualizer tool, and the toolbox of perceptual coding and interaction. The assignment instructions include identifying the top 25 systems related to each topic, creating a presentation using PowerPoint, and reflecting on search strategies.

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Information Visualization - Origins, Visualizer, and Toolbox

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  1. Lecture 8 • Topic Assignment • Information Visualization – Origins • Information Visualizer • Visualization of Hierarchical Data

  2. Assignment Instructions • Topics • TBA • Goal • Identify “Top 25” Systems related to each Topic • Use searchCrystal to find systems www.searchcrystal.com and create free account for Full Version • Save different result lists • Compare and edit result lists to produce list of 25 systems • Email instructor final list from within searchCrystal Task: figure out how to prune result list in searchCrystal • Identify “Top 1”System for Each Topic • Categorize in terms • Perceptual Coding and Types of Interaction Used

  3. Assignment Instructions • Create Presentation  Powerpoint • Reflect on your Search Strategies • Effective Search Terms • Select “Best” System for each Topic Presentation Template http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisOnline/PresentationTemplate.ppt – Provide Screenshots • Categorize using Perceptual Coding and Types of Interaction Toolbox • DUE = Monday Noon Week 11 • Host Powerpoint file online and email instructor URL

  4. Recap – Information Visualization – “Toolbox” Perceptual Coding Interaction Information Density

  5. Information Visualization – Origins • 1 Thought Leaders • Bertin, French cartographer, "The Semiology of Graphics (1967/1983) • Tufte (1983) emphasizes maximizing the density of useful information • 2 Statistical Visualization • Tukey (1977) “Exploratory Data Analysis”: rapid statistical insight into data • Cleveland and McGilll (1988) "Dynamic Graphics for Statistics“ • Analysis of multi–dimensional, multi–variable data • 3 Scientific Visualization • Satellites sending large quantities of data  how to better understand it? • 4 Computer Graphics and Artificial Intelligence • Mackinlay (1986) formalized  Bertin's design theory; added psychophysical data, and used to generate automatic design of data • 5 User Interface and Human Computer Interaction • Card, Robertson & Mackinlay (1989) coined “Information Visualization” and used animation and distortion to interact with large data sets in a system called the “Information Visualizer”

  6. Stacked Scatterplots - Brushing

  7. SeeSoft – Software Visualization Linked Displays Line = single line of source code and its length Color = different properties

  8. Information Retrieval Need for Low-Cost, Instant Access of Objects in Use

  9. Information Retrieval  Low-Cost Information Workspace • Lower Cost of Info Access • Large Workspace • Rooms • 3D and Animation • Agents: delegate workload • Search • Organize  Cluster agent • Interacting  Interactive Objs • Real-Time Interaction • Rapid Interaction tuned to Human Constants • Visual Abstractions • Cone Tree for hierarchies • Perspective Wall for linear structures

  10. Information Visualizer – Persistent Rooms

  11. Information Visualizer – Summary • Reduce Information Access Costs • Increase Screen Space  Rooms Create Visual Abstractions ConeTree PerspectiveWall • Increase Information Density  3Dand Animation • Overload Potential • Create “Focus + Context” display with Fisheye Distortion • Logarithmic Animation to rapidly move Object into Focus • Object Constancy • Shift Cognitive Load to Perceptual System • Tune System Response Rates to “Human Constants” • 0.1 second, 1 second, 10 seconds  Cognitive Co-Processor

  12. Recap – Interaction – Mappings + Timings • Mapping Data to Visual Form • Variables Mapped to“Visual Display” • Variables Mapped to“Controls” • “Visual Display” and “Controls”Linked • Interaction Responsiveness “0.1” second • Perception of Motion • Perception of Cause & Effect “1.0” second  Status Feedback “10” seconds • Point & click, parallel requests

  13. Dynamic Queries & Starfields Two Most Important Variables Mapped to “Scatterplot” Other Variables Mapped to “Controls” “Visual Display” and “Controls” Linked

  14. Dynamic Queries & Starfields Download Video(… will take a while) or http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/videos/ and right click on “filmFinder.mpeg” and save

  15. Dynamic Queries & Starfields • Which Pre–attentive, Early Visual Processes Used? • Motion, Position, Color, (Size) • How to choose two dimensions of Starfield? • “Encode more important information more effectively” • Choose two variables of most interest / importance

  16. Dynamic Queries & Starfields • Advantages of Dynamic Queries over traditional query language such as SQL • Make Query Formulation Easy = Interact with Sliders and Visual Objects (SQL = Structured Query Language is difficult to master) • Support Rapid, Incremental and Reversible Exploration • Shift Cognitive Load to Perceptual System • Selection by Pointing • Tight Coupling of Interface Components • Link and Continuously Update the displays showing specific “states” of software (“page number” and “scrollbar position” linked)  Linked Display and Controls • Immediate Visual Feedback • Avoid “Null set” by having current selection limit further query refinement • Progressive Query Refinement • Details on Demand

  17. Starfields Perceptual Coding Interaction

  18. Hierarchical Information • Pervasive • File / Directory systems on computers • Classifications / Taxonomies / Controlled Vocabularies • Software Menu structure • Organization charts • … • Main Visualization Schemes • Indented Outlines • Good for Searching Bad for Structure • Node-Link Trees • Top-to-Bottom Layout • 2D • 3D : ConeTree • Radial Layout • 2D : SunBurst, Hyperbolic Trees • 3D : H3 & Walrus • Space-Filling Treemaps

  19. Hierarchical Data – Traditional Node-Link Layout Allocate Space proportional to # of Children at Different Levels

  20. Traditional Node-Link Layout  SpaceTree • HCI Lab – University of Marylandhttp://www.cs.umd.edu/hcil/spacetree/ Download Video(… will take a while) or http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/videos/ and right click on “orgchart.avi” and save

  21. Positive Higher Information Density Smooth animation Negative Occlusion Non-trivial to implement Requires horsepower 3D ConeTree • 3D used to increase Information Density • Children laid out in a cylinder “below” parent

  22. Treemaps  Space-Filling Design

  23. Treemaps – “Slice & Dice”

  24. Treemap – Mapping Traditional Layout into Treemap

  25. Nested Tree-Map Treemaps – Nested vs. Non-nested Non-nested Tree-Map

  26. Treemaps – Examples • SmartMoneyhttp://www.smartmoney.com/marketmap/ • The Hive Group • http://www.hivegroup.com/solutions/demos/stocks.html • Newsmap • http://www.marumushi.com/apps/newsmap/newsmap.cfm

  27. Treemaps – Video & Demos • Treemap 4.0 Video • Video: http://www.cs.umd.edu/hcil/treemap/doc4.0/Video/TotalWithBuffer.html • Treemap Demo • Applet: http://www.cs.umd.edu/hcil/treemap/applet/index.shtml • Download: http://www.cs.umd.edu/hcil/treemap/demos/ • Launch Demo • File > NBA Statistics • “Main” Tab: choose “Squarified” • Examine “Label” Tab • Task • Find 3 top Players who have played at least 80 games and scored the highest “Points per Game” • History of Treemaps http://www.cs.umd.edu/hcil/treemap-history/index.shtml

  28. Treemaps • Which Problem do Treemaps aim to address? • Visualize hierarchical structure as well as content of (atom) nodes • What are Treemaps’ main design goals? • Space–filling(High Data / Ink Ratio) • “Structure” is represented using Enclosure / Containment • “Content” is represented using Area • Pre–attentive, Early Visual Processes Used? • Position, Size = Area, Color and Containment

  29. Non-nested Nested Treemap Data = Hierarchy Perceptual Coding Interaction

  30. Which has bigger area? Non-nested Nested Questions – Treemaps • What are the strength’s of Treemaps?What are the issues / weaknesses of Treemaps? What are the visual properties that make them easier or harder to use? • Easy to identify “Largest” because of size = area coding • Easy to identify “Type” of atom node because of color coding • Structure can be difficult infer: borders help, but consumes space • “Long-Thin Aspect Ratio” issue and “Area” can be difficult to estimate • When is a nested display more effective than a non-nested display? • To make structure easier to see

  31. Squarified Unordered best aspect ratios medium stability Slice-and-dice Ordered, very bad aspect ratios stable Treemaps – Other Layout Algorithms • Hard to Improve Aspect Ratio and Preserve Ordering

  32. Treemaps – 1,000,000 items http://www.cs.umd.edu/hcil/VisuMillion/

  33. Treemaps – Shading Visualization Group - Technical University of Eindhovenhttp://www.win.tue.nl/vis/ • Borderless Treemap  difficult to see structure of hierarchy • CushionTreemap SequoiaView

  34. Treemaps – Shading

  35. Treemaps – PhotoMesa • Quantum Treemaps / Bubblemaps for a Zoomable Image Browserby B. B. Bedersonhttp://www.cs.umd.edu/hcil/photomesa/ Download Video(… will take a while) or http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/videos/ and right click on “photoMesa.mpeg” and save

  36. Hierarchical Data – Radial Space-Filling  SunBurst http://www.cc.gatech.edu/gvu/ii/sunburst/

  37. Botanical Visualization of Huge Hierarchies Visualization Group - Technical University of Eindhovenhttp://www.win.tue.nl/vis/

  38. Botanical Visualization of Huge Hierarchies

  39. Botanical Visualization of Huge Hierarchies

  40. Traditional Treemap Botanical SunTree ConeTree Hierarchical Information – Recap

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