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Space/Order

CS-533C Reading Presentation. Space/Order. Quanzhen Geng (Master of Software Systems Program) January 27, 2003. Space/Order Encodings. Definition:

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Space/Order

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  1. CS-533C Reading Presentation Space/Order Quanzhen Geng (Master of Software Systems Program) January 27, 2003

  2. Space/Order Encodings Definition: Space/order encodings transform data in information space into a spatial representation (size and order) in display space that preserves informational characteristics of the dataset and facilitates our visual perception and understanding of the data. Importance: Finding a good spatial representation of the information at hand is one of the most difficult and also the most important tasks in information visualization.

  3. Two challenges ofSpatial Encodings (1) Visualizing large information space (Large Maps, Tables, Documents etc.) through a relatively small window screen. Lack of screen space (2) Visualizing multi-dimensional data (n>3) in 2D space How to effectively present more than 3 dimensions of information in a visual display with 2 (to 3) dimensions? How to display 1,000,000 rows of table on screen? What does 10-D space look like?

  4. Solving the Problems inSpatial Encodings Two important spatial representation techniques: • Spatial distortions solve the lack of screen space problem • Parallel coordinates Non-projective mapping between N-D and 2-D

  5. Distortions Problems: – Large Computer-Based Information Systems – Small Window as Single Access-Point – Difficult to Interpret Single Information Items when Viewing it Outside of its Context Definition: Distortion is a visual transformation that modifies a Visual Structure to create focus+context views. Want to achieve: • Focus: to see detail of immediate interest • Context: to see the overall picture Want to solve: The problem of displaying a large information space through a relatively small window, i.e., lack of screen space problem.

  6. Principles of distortions Transformation function Magnification function

  7. Distortions • Methods of distortions (focus+context views): --Bifocal Display --Perspective wall --Document lens --Fisheye views --Table lens • Major differences of these methods: --Transformation function --Magnification function

  8. Bifocal Display • First suggested by Spence and Apperley (1980?). • Combination of a detailed view and two distorted sideview. • One-dimensional form.

  9. Bifocal Display Fold Project www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/PDF-Files/InfoVis-6.pdf

  10. What is the Bifocal Display Doing? • Transform the information space to the display space with Visual transformation functions www.comp.leeds.ac.uk/kwb/VIS/v02_16.ppt

  11. Early implementation of Bifocal Display (1980) www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/PDF-Files/InfoVis-6.pdf

  12. Perspective Wall • A technique for viewing and navigating large, linearly-structured information (for instance, chronological / alphabetical data), allowing the viewer to focus on a particular area while still maintaining some degree of location or context. • Extension or descendant of Bifocal Display. • 3D aspect decreases cognitive load.

  13. Perspective Wall vs. Bifocal Display Perspective Wall: • 3D view • Center panel to view detail • Perspective panels to view context Bifocal Display Perspective Wall 2D view 3D view www.sims.berkeley.edu/courses/is247/s02/lectures/ZoomingFocusContextDistortion.ppt

  14. Perspective Wall [Mackinlay et al.c 1991]

  15. In terms of transformation function, the situation is closer to the bifocal display. Perspective gives smoother transition from focus to context. Perspective Wall

  16. Perspective WallExample 1 – project schedule Map work charts onto diagram. x-axis is time, y-axis is project. (Mackinlay, Robertson, Card ’91)

  17. Perspective WallExample 2 – file navigation • Typical example use is file navigation • Shown by date, type • However few files can be displayed at once

  18. Perspective WallExample 3 – file navigation

  19. Features of Perspective Wall • Folding is used to distort a 2-D layout into a 3-D visualization,using hardware support for 3-D interactive animation. • Perspective panels are shaded to enhance the effect of 3-D. • Vertical dimension can be used to visualize layering information. Disadvantage: • Wastes the corner areas of the screen.

  20. Document Lens Why: -Text too small to read but yet needed to perceive patterns. -Perspective wall wastes corner areas of screen What: General visualization technique based on a common strategy for understanding paper documents when their structure is not known. How:3D Visualization Tool For Large Rectangular Presentations

  21. Document Lens Features • Lens – rectangular – interested in text that is mostly rectangular • Sides are elastic and pull the surrounding parts towards the lens creating a pyramid

  22. Document Lens Document lens, 3-D effect, no waste of corner space

  23. Comparison with other approaches Bifocal Display Perspective Wall Document Lens

  24. Fisheye View (Distortion) • When people think about focus+context views, they typically think of the Fisheye View (Distortion) • First introduced by George Furnas in his 1981 report • “Provide[s] detailed views (focus) and overviews (context) without obscuring anything…The focus area (or areas) is magnified to show detail, while preserving the context, all in a single display.” -(Shneiderman, DTUI, 1998) www.cc.gatech.edu/classes/AY2002/cs7450_spring/ Talks/10-focuscontext.ppt

  25. 1D Fisheye 2D Fisheye Principles of Fisheye View • Continuous Magnification Functions • Can distort boundaries because applied radially rather than x y http://davis.wpi.edu/~matt/courses/distortion/#fisheye

  26. Fisheye-view vs. Bifocal display Bifocal Display Fisheye-view http://davis.wpi.edu/~matt/courses/distortion/#fisheye

  27. Fisheye View Application 1 –Map of Washington D.C. web.mit.edu/16.399/www/course_notes/context_and_detail1.pdf

  28. Fisheye ViewApplication 2 –viewing network nodes

  29. Fisheye ViewApplication 3 – fisheye menu Dynamically change the size of a menu item to provide a focus area around the mouse pointer, while allowing all menu items to remain on screen • All elements are visible but items near cursor are full-size, further away are smaller • “bubble” of readable items move with cursor www.comp.leeds.ac.uk/kwb/VIS/v02_16.ppt

  30. Fisheye View Application 4 – fisheye table

  31. Table Lens The Table Lens: Merges Graphical and Symbolic Representations in an Interactive Focus + Context Visualization for Tabular Information. (Ramana Rao and Stuart K. Card)

  32. Table Lens Features • Focus + context for large datasets while retaining access to all data • Works best for case / variable data & flexible, suitable for many domains • Cell contents coded by color (nominal) or bar length (interval) • Tools: zoom, adjust, slide • Search / browse (spotlighting) • Create groups by dragging columns

  33. Row Focal Area Column Focal Area Non-Focal Area Focal Area Table Lens • Distortion in each dim. is independent • Multiple focal areas • Degree of Interest (DOI) • Interactive Focus Manipulation

  34. DOI (Degree of Interest) • Maps from an item to a value that indicates the level of interest in the item.

  35. DOI DOI DOI Slide Zoom Adjust Table Lens Focus Manipulation Zoom, adjust and slide provides interactive focus manipulation

  36. Table Lens

  37. Parallel Coordinates Issues: • How to effectively present more than 3 dimensions of information in a visual display with 2 (to 3) dimensions? • How to effectively visualize very large, often complex data sets? www.sims.berkeley.edu/courses/is247/s02/lectures/MultidimensionalDataAnalysis.ppt

  38. Parallel Coordinates -Goals We want to: Visualize multi-dimensional data • Without loss of information • With: • Minimal complexity • Any number of dimensions • Variables treated uniformly • Objects remain recognizable across transformations • Easy / intuitive conveyance of information • Mathematically / algorithmically rigorous (Adapted from Inselberg) www.sims.berkeley.edu/courses/is247/s02/lectures/MultidimensionalDataAnalysis.ppt

  39. Parallel Coordinates:Visualizing N variables on one chart • Create N equidistant vertical axes, each corresponding to a variable • Each axis scaled to [min, max] range of the variable • Each observation corresponds to a line drawn through point on each axis corresponding to value of the variable www.comp.leeds.ac.uk/kwb/VIS/v02_14.ppt

  40. Parallel Coordinates -- Correlations may start to appear as the observations are plotted on the chart -- Here there appears to be negative correlation between values of A and B for example -- This has been used for applications with thousands of data items www.comp.leeds.ac.uk/kwb/VIS/v02_14.ppt

  41. Dataset in a Cartesian coordinate Same dataset in parallel coordinates Cartesian vs. Parallel Coordinates infovis.cs.vt.edu/cs5984/students/parcoord.ppt

  42. Parallel Coordinates Example 1: Correlations Detroit homicide data 7 variables 13 observations

  43. Parallel Coordinates -Example 2: Air traffic control Cartesian Coordinates Parallel Coordinates http://www.caip.rutgers.edu/~peskin/epriRpt/ParallelCoords.html

  44. Parallel Coordinates: Advantages • Multi-dimensional data can be visualized in • two dimensions with low complexity. • Each variable is treated uniformly. • Relations within multi-dimensional data can • be discovered (“data mining”). • Because of its visual cues, can serve as a • preprocessor to other methods.

  45. Parallel Coordinates: Disadvantages • Close axes as dimensions increase. • Clutter can reduce information perceived. • Varying axes scale, although indicating • relationships, may cause confusion. • Connecting the data points can be misleading.

  46. Disadvantage: Level of Clutter Taken from: “Hierarchical Parallel Coordinates” Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward 16,384 records in 5 dimensions causes over-plotting.

  47. Improvement: SummarizationTaken from: “Hierarchical Parallel Coordinates”Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward. 

  48. Improvement: Level-Of-Detail (LOD)Taken from: “Hierarchical Parallel Coordinates”Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward. 

  49. Improvement: BrushingTaken from: “Hierarchical Parallel Coordinates”Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward. 

  50. Summary • Spatial encoding the most important encoding • The good and bad of spatial distortion • The advantages and disadvantages of parallel coordinates

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