1 / 37

1D & 2D Spaces for Representing Data

1D & 2D Spaces for Representing Data. Mao Lin Huang. 1-D Representation of Data. 1-D Textual Data. Keyhole Problem. No context Lost, disoriented Where am I? Where can I go? Where do I want to go? How do I get there?. Visual Overview. Map, organization (spatial layout of concepts)

gary
Download Presentation

1D & 2D Spaces for Representing Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 1D & 2D Spaces for Representing Data Mao Lin Huang

  2. 1-D Representation of Data

  3. 1-D Textual Data

  4. Keyhole Problem • No context • Lost, disoriented • Where am I? • Where can I go? • Where do I want to go? • How do I get there?

  5. Visual Overview • Map, organization (spatial layout of concepts) • What information is (not) available? • Adds context info, relationships • Enables direct access • Encourages exploration • HCI metrics: • Improves user performance, learning time, error rates, retention, satisfaction

  6. Navigation Approaches • Detail Only • Zooming • Overview+Detail • Focus+Context (Distortion, fisheye)

  7. 1D Visual Representation • Plaisant, “Lifelines”, pp 285 See personal history • Mackinlay, “Perspective Wall”, web

  8. 1D Visual Representation • Eick, “SeeSoft”, p 419 • Analyze 50,000 lines of code simultaneously by mapping each line of code into a thin row. • Eick, “Data Visualization Sliders”, p 251 (2 pages)

  9. Navigation Strategies • Detail Only • Zooming • Overview+Detail • Focus+Context (Distortion, fisheye)

  10. Fisheye Menus • http://www.cs.umd.edu/hcil/fisheyemenu/ • Very Fast • due to mouse mechanics, no clicking, mostly vertical sliding • Alphabet overview helpful • Fisheye context not useful in this case? • Might be more useful in SeeSoft where miniature representation gives important information • Limits # of readable items to ~10 • Wasted space at top- and bottom- right • Distortion problematic? • Alphabet overview distorted at A and Z • Scale limited? • Possible improvement: • Same alphabet overview (without end distortion) • Remove fisheye, maximize readable items like scrolling version • Same fast mouse mechanics, scroll fast on left, no scroll on right

  11. Music Animation Machine • Good for visualizing music during serial playback, relate audio to visual structure • Visualizing entire compositions: increase information density • See patterns of phrases, instruments, etc.

  12. 2-D Representation of Data

  13. 2-D • Image browsing • Maps

  14. Today • Bederson, “Pad++”, p 530 • a zooming graphic interface to replace icon-based window interface • Furnas, “Space-Scale Diagrams”, web

  15. Space-Scale

  16. Pad++ on edge Info surface • Like ray-tracing zoom window

  17. Semantic Zooming • Zooming in, red object turns to blue

  18. Multiple Views • Zoom factor ~ 20

  19. Multiple levels = large scale • Zoom factor = 20 * 20 * 20 = 8000

  20. Multiple Foci

  21. Multiple Overviews • Can have different information types at each level

  22. 2-D + Attributes • Dynamaps: dynamic queries on maps

  23. 2-D: Focus+Context Representation of Data

  24. 2-D • Robertson, “Document Lens”, p 562 • Spence, “Bifocal Lens”, p 331,333

  25. Focus+Context • Details within overview • “Distortion-oriented display” • “Fisheye” • Leung, Apperley, “Taxonomy of distortion-oriented presentations”, book pg 350

  26. Visual Transfer Functions Display surface Information surface Identity function = normal flat overview Bifocal

  27. Magnification Functions 1st Derivative

  28. Bifocal Display • Spence, Apperley

  29. Bifocal Display Disadvantage: 1 dimensional stretching on the 4 sides

  30. Perspective Wall / Document Lens

  31. NonLinear Magnification • http://www.cs.indiana.edu/hyplan/tkeahey/research/nlm/nlm.html • http://www.cs.indiana.edu/hyplan/tkeahey/research/papers/infovis.98.html

  32. “Bubble” Disadvantage: local context highly de-magnified

  33. “Fisheye”, “wide-angle lens” Disadvantage: no flat area

  34. Quiz: TableLens • Bifocal!

  35. Fisheye Menus • Non-linear: combination of Bubble + fisheye

  36. Why not magnifying glass? • Hides local context

  37. F+C vs. O+D • + Scales up to larger data (zoom factor and chaining) • + Multi foci easier • + multiple overviews possible • + Easy to implement, Less math! • Fast system performance • - >=2 places to look (cross-eyed!) • Tracking field-of-view box hard • Hand-eye coordination problem • - detail and overview disconnected • - Windows/space management • - replicates detail data in overview • + Space efficient • + Detail connected to context • Smooth transition • + matches human vision/processing? • - Distortion • - Longer learning time • - no flat overview - Need a way to turn off focus • - Content moves differently than mouse • - hard to tell zoom factor

More Related