To Draw a Tree
Pat Hanrahan
Computer Science Department Stanford University
Motivation
Hierarchies File systems and web sites Organization charts Categorical classifications Similiarity and clustering Branching processes Genealogy and lineages Phylogenetic trees Decision processes Indices or search trees Decision trees Tournaments
Page 1 Tree Drawing
Simple Tree Drawing
Preorder or inorder traversal
Page 2 Rheingold-Tilford Algorithm
Information Visualization
Page 3 Tree Representations
Most Common …
Page 4 Tournaments!
Page 5 Second Most Common …
Lineages
Page 6 http://www.royal.gov.uk/history/trees.htm
Page 7 Demonstration
Saito-Sederberg Genealogy Viewer
C. Elegans Cell Lineage
[Sulston]
Page 8 Page 9 Page 10 Page 11 Evolutionary Trees
[Haeckel]
Page 12 Page 13 [Agassiz, 1883]
1989
Page 14 Chapple and Garofolo, In Tufte
[Furbringer]
Page 15 [Simpson]]
[Gould]
Page 16 Tree of Life
[Haeckel]
[Tufte]
Page 17 Janvier, 1812
“Graphical Excellence is nearly always multivariate”
Edward Tufte
Page 18 Phenograms to Cladograms
GeneBase
Page 19 http://www.gwu.edu/~clade/spiders/peet.htm
Page 20 Page 21 The Shape of Trees
Page 22 Patterns of Evolution
Page 23 Hierachical Databases
Stolte and Hanrahan, Polaris, InfoVis 2000
Page 24 Generalization
• Aggregation • Simplification • Filtering
Abstraction Hierarchies
Datacubes
Star and Snowflake Schemes
Page 25 Memory & Code
Cache misses for a procedure for 10 million cycles
White = not run Grey = no misses Red = # misses
y-dimension is source code x-dimension is cycles (time)
Memory & Code
zooming on y zooms from fileprocedurelineassembly code zooming on x increases time resolution down to one cycle per bar
Page 26 Themes
Cognitive Principles for Design
Congruence Principle: The structure and content of the external representation should correspond to the desired structure and content of the internal representation.
Apprehension Principle: The structure and content of the external representation should be readily and accurately perceived and comprehended.
[B. Tversky in press]
Page 27 Metaphors
Survey the space of designs Best metaphors have been refined over time (and have stood the test of time) Need to understand and appreciate what makes metaphors effective It takes both creativity and experience, algorithms and aesthetics, to create new metaphors. Often one element is lacking “Graphical excellence is multivariate,” therefore mix your metaphors
Depiction
How to map information to graphics Central problem in visualization Formal systems needed Automation and composition Need high-level tools to make this easy
Key theorists: Bertin, Cleveland, Mackinlay, MacEachren, Wilkinson
Page 28 Challenge
Best visualizations designed by humans Computer mediated communication is becoming ubiquitous
Therefore: Visualizations are regressing
Challenge: Develop algorithms that produce high-quality, effective designs
Acknowledgements
Tamara Munzner Chris Stolte Diane Tang Maneesh Agrawala Barbara Tversky
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