1. Mao Lin Huang University of Technology, Sydney, Visual Representations of Data and Knowledge

2. 2 Rendering Effective Route Maps

3. 3 General Idea Automatically generate a route map that has the same properties as a hand drawn map. Hand drawn maps: Exaggerated Lengths (non-constant scale factor) No irrelevant information

4. 4 More Specifically Constant scale factor Road lengths on a conventional map vary in several orders of magnitude => small roads and neighborhoods are hard to navigate with large maps Information irrelevant to navigation Names of locations, places, cities, etc. that are all far away from the route Takes up space that would be otherwise useful for showing crossroads and relevant landmarks

5. 5 Generalization Techniques Generalize Length Use more space for short roads, less for longer ones. Distribute based on importance, not physical length Generalize Angle Align roads or make room for others Generalize Shape Navigator doesn’t need to know roads shape. Simpler roads are easier to differentiate on a map.

6. 6 Demo at mapblast.com

7. 7 Simple Visualization Model Data View Port Visual Mapping

8. 8 Film Data Table Example: Attributes

9. 9 Visual Mapping Define a Space Map: data  marks Map: data attributes  graphical mark attributes Year  X Length  Y Popularity  size Subject  color Award?  shape

10. 10 Example: FilmFinder 38

11. 11 Example: FilmFinder 39

12. 12 Use of graphical time scales as an approach to visualize histories. [Time Scale + History = Intuitive]

13. 13

14. 14

15. 15 Patient Records

16. 16 Galaxies Projection of clustering algorithms into 2D Galaxies are clusters of related data Proximity of galaxies is relevant Designed to add temporal patterns to clustering

17. 17 Galaxies

18. 3D Visualization & VR Techniques

19. 19 3D Cone Tree 16

20. 20 3D Cone Trees research.microsoft.com/~ggr/gi97.ppt 17

21. 21 Perspective Wall research.microsoft.com/~ggr/gi97.ppt 18

22. 22 Example: 3D-Room (The Exploratory) Robertson, Card, and Mackinlay (1989) 20

23. 23 3D Navigation Task (Hallway) research.microsoft.com/~ggr/gi97.ppt 21

24. 24 3D GUI for Web Browsing 22

25. 25 3D GUI for Web Browsing http://research.microsoft.com/ui/TaskGallery/index.htm 23

26. 26 Web Forager http://research.microsoft.com/ui/TaskGallery/index.htm 24

27. 27 WebBook research.microsoft.com/~ggr/gi97.ppt 25

28. 28 3D GUI for Desktop http://research.microsoft.com/ui/TaskGallery/index.htm 26

29. 29

30. 30 ThemeScape Abstract 3D landscape of information Reduce cognitive load using terrain Elevation, colour encode theme strength redundantly Landscape metaphor translates well Peaks are easy to recognize Interesting characteristics include ridges and valleys

31. 31 ThemeScape

32. 32 ThemeScape

33. 33 Calendar Based Visualization Using 3 dimensions X-axis: Time of day Y-axis: Days of data period Z-axis: Univariate data samples

34. 34 Calendar Based Visualization

35. 35 Calendar Based Visualization

36. 36 Graph-Driven Visualization of Relational Data An example of visualizing relational data. This is the visualization of a family tree (graph). Here each image node represents a person and the edges represent relationships among these people in a large family. Graph Visualization

37. 37 Classical Graph Layouts Link-node diagrams Layout algorithms (graph drawing) Geometric positioning of nodes & edges Small amount of nodes Avoid node overlaps Reduce edge crossings hierarchical force-directed orthogonal symmetric radial layout

38. 38 Using a very large virtual page The virtual page technique predefines the drawing of the whole graph, and then provides a small window and scroll bar to allow the user to navigate through it (by changing the viewing area).

39. 39 Fish-eye views The fish-eye technique can keep a detailed picture of a part of a graph as well as the global context of the graph. It changes the zoomed focus point.

40. 40 3D Graph Drawing SGI fsn file-system viewer Image from: http://www.sgi.com/fun/images/fs n.map2.jpg

41. Trees

42. 42 2 Approaches Connection (node & link) Enclosure (node in node) Structure vs. attributes Attributes only (multi-dimensional viz) Structure only (1 attribute, e.g. name) Structure + attributes A CB A BC

43. 43 Containment Approach

44. 44 Treemaps (Shneiderman) Slice and Dice Alternate horizontal and vertical cuts for levels Node area  node attribute Zoom onto nodes Space-Filling Structure + 3 attributes Area, color, label

45. 45 Treemaps

46. 46 Balanced trees

47. 47 Treemaps ~ 1000 nodes Quantitative attributes Good combination of structure + attributes For unbalanced trees, structure more difficult Learning time: 20 min Evaluation: major performance boost over outliner Bad aspect ratios: long narrow rectangles Large scale or deep causes solid black

48. 48 Treemap Algorithm Calculate sizes: Recurse to children My size = sum children sizes Draw Treemap (node, space, direction) Draw node rectangle in space Alternate direction For each child: Calculate child space as % of node space using size and direction Draw Treemap (child, child space, direction)

49. 49 Cushion Treemaps

50. 50 Squared Treemaps

51. 51 Treemaps on the Web Map of the Market: http://www.smartmoney.com/marketmap/http://www.smartmoney.com/marketmap/ People Map: http://www.truepeers.com/http://www.truepeers.com/ Coffee Map: http://www.peets.com/tast/11/coffee_selector.asphttp://www.peets.com/tast/11/coffee_selector.asp

52. 52 DiskMapper http://www.miclog.com/dmdesc.htm

53. 53 2D Tree Drawing (web sitemap) MosiacG System Zyers and Stasko Image from: http://www.w3j.com/1/ayers.270/pap er/270.html

54. 54 PDQ Trees Overview+Detail of 2D layout Dynamic Queries on each level for pruning

55. 55 Space-Optimized Tree Layout A large data set of approximately 50 000 nodes My Unix root with approx. 3700 directories and files

56. 56 Hyperbolic tree The hyperbolic browser technique performs fish-eye viewing with animated transitions to preserve the user’s mental map. It changes both the viewing area and the zoomed focus point.

57. 57 H3 Image from: http://graphics.stanford.edu/papers/h3/fig/nab0.gif