1. Tree Structures (Hierarchical Information) cs5764: Information Visualization Chris North

2. Where are we? Multi-D 1D 2D Trees Graphs 3D Document collections Design Principles Empirical Evaluation Visual Overviews

3. Trees (Hierarchies) What is a tree? DAG, one parent per node Items + structure (nodes + associations) In table model? Add parent pointer attribute 1:M

4. Examples File system menus org charts Family tree classification/taxonomy Table of contents data structures …

5. Tasks Multi-D tasks, plus structure-based tasks: Find descendants, ancestors, siblings, cousins Overall structure, height, breadth, dense/sparse areas …

6. Tree Properties Structure vs. attributes Attributes only (multi-dimensional viz) Structure only (1 attribute, e.g. name) Structure + attributes Branching factor Fixed level, categorical

7. Tree Visualization Example: TreeView Why is tree visualization hard? Structure AND items Structure harder, consumes more space Data size grows very quickly (exponential) »#nodes = b height

8. 2 Approaches Connection (node & link) outliner Containment (node in node) Venn diagram A CB A BC

9. Connection (node & link)

10. TreeView Good for directed search tasks subtree filtering (+/-) Not good for learning structure No attributes Apx 50 items visible Lose path to root for deep nodes Scroll bar!

11. Mac Finder Branching factor: Small large

12. Hyperbolic Trees Rao, “Hyperbolic Tree” http://startree.inxight.com/ Xerox PARC Inxight Focus+context

13. Cone Trees Robertson, “ConeTrees” Xerox PARC 3D for focus+context

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

15. PDQ Trees

16. Disk Tree Ed Chi, Xerox PARC Overview: Reduced visual representation

17. WebTOC Website map: TreeView + size attributes http://www.cs.umd.edu/projects/hcil/webtoc/fhcil.html

18. FSN SGI file system navigator Jurassic Park Zooming?

19. Ugh!

20. Containment (node in node)

21. 2 Approaches Connection (node & link) Outliner Containment (node in node) Venn diagram Structure vs. attributes Attributes only (multi-dimensional viz) Structure only (1 attribute, e.g. name) Structure + attributes A CB A BC

22. Pyramids

23. 3D Containment

24. Treemaps Shneiderman, “Treemaps” http://www.cs.umd.edu/hcil/treemap3/ Maryland zooming

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

26. Squarified Treemaps Wattenberg Van Wijk

27. http://www.research.microsoft.com/~masmith/all_map.jpg

28. Cushion Treemaps Van Wijk http://www.win.tue.nl/sequoiaview/

29. Dynamic Query Treemaps http://www.cs.umd.edu/hcil/treemap3/

30. 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

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

32. Sunburst Stasko, GaTech Radial layout Animated zooming

33. Sunburst (vs. Treemap) + Faster learning time: like pie chart + Details outward, instead of inward + Focus+context instead of zooming - Not space filling - More space used by non-leaves - Less scalability? All leaves on 1-D space, perimeter Treemap: 2-D space for leaves

34. Misc.

35. CHEOPS Beaudoin, “Cheops” http://www.crim.ca/hci/cheops/index1.html http://tecfa.unige.ch/~schneide/cheops/lite1.html

36. The Original Fisheye View George Furnas, 1981 (pg 311) Large information space User controlled focus point How to render items? Normal View: just pick items nearby Fisheye View: pick items based on degree of interest Degree of Interest = function of distance from f and a priori importance DOI(x) = -dist(x,f) + imp(x) x f

37. Example: Tree structure Distance = # links between f and x Importance = level of x in tree Distance: I A a i ii b i ii B a i ii b i ii Importance: I A a i ii b i ii B a i ii b i ii DOI: I A a i ii b i ii B a i ii b i ii f

40. Challenges Multiple foci George Robertson, Microsoft Research

41. Polyarchies multiple inter-twined trees Visual pivot George Robertson, Microsoft Research

42. Nifty App of the Day SAS JMP

43. Summary Hyperbolic <1000 TreeMap <3000, attributes, collective Cheops = scale up