cs5984: Information Visualization Chris North Trees 2 cs5984: Information Visualization Chris North

2 Approaches Connection (node & link) Containment (node in node) 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 B C today A B C

3D

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

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)

Squared Treemaps Wattenberg Van Wijk

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

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

Dynamic Queries http://www.cs.umd.edu/hcil/treemap3/

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

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

Sunburst Stasko, GaTech Radial layout Animated zooming

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

CHEOPS Beaudoin, “Cheops” Jon, Mudita http://www.crim.ca/hci/cheops/index1.html

Summary Hyperbolic ConeTree TreeMap Sunburst Cheops

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) f x

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

Next Week I will be away Book chapter 6 Tues: Dr. McCrickard Healey, “Preattentive Processing” parool, priya Somervell, “InfoVis in the Periphery” ali, vikrant Thurs: Virtual Environments Go directly to Torg 3050 Dr. Bowman, Alex Kalita

Next Next Week Tues: Networks Thurs: Networks Eick, “SeeNet” kuljit, anil Munzner, “H3: 3D hyperbolic graphs” dananjan, samil Thurs: Networks Korn, “Multi-Digraphs” ying, kuljit Storey, “SHriMP” qing, quoc Tues Oct 30: Project status report due Thurs Nov 1: Homework #3 due Purvi: HiNote info session, Fri 4pm, McB 655 Change!