Comp/Phys/APSc 715 Information Visualization and Tufte 4/8/2014 - - PDF document

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4/7/2014 Comp/Phys/APSc 715 Information Visualization and Tufte 4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor 1 Preview Videos Vis 2012: ttg2012122061s Crack propagation Vis2012: ttg2012122355s Transfer function

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SLIDE 1

4/7/2014 1

Information Visualization and Tufte

Comp/Phys/APSc 715

4/8/2014 Infovis, Genomics Tufte 1 Comp/Phys/APSc 715 Taylor

Preview Videos

  • Vis 2012: ttg2012122061s

– Crack propagation

  • Vis2012: ttg2012122355s

– Transfer function design

  • Vis 2004: theisel.avi

– Flow topology for time-varying 2D flow fields

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Administrative

  • Thursday: Meet in Sitterson 264
  • Two weeks: Your presentations!

– Final write-up due on day of final

  • Have you completed your goals?
  • Have you iterated your designs?

– Better than best HW design?

4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor 3

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SLIDE 2

4/7/2014 2 Information Display and Spatial Embeddings

  • The data we have looked at so far has existed in some natural

spatial embedding

  • Subset of the possible kinds of information that exist
  • Key challenges in information visualization

– How to embed in 2D or 3D? – How do you reduce high-dimensional data? – How do you convey relationships among entities?

  • Choice of embedding often depends on goal
  • Often, spatial embedding changes

– Level-of-detail – Focus + context techniques magnify portions of data

4/8/2014 Infovis, Genomics Tufte 4 Comp/Phys/APSc 715 Taylor

Focus + Context

  • May only want to show part of data in detail
  • Knowing how the detailed data relates to the

rest of the data set is still important

  • Focus + context techniques attempt to do this

– More screen space dedicated to showing one or more parts of the data – Rest of the data shown in less resolution

4/8/2014 Infovis, Genomics Tufte 5 Comp/Phys/APSc 715 Taylor

Interactive Data Analysis

  • Tie data analysis into visualization interface
  • Show results of queries/algorithms
  • Allows pattern detection in subsets of the data
  • Enable finding answers to different questions

at different points in data exploration

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SLIDE 3

4/7/2014 3

Techniques for small data sets…

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Flow Maps

  • Phan et al., IEEE Vis 2005
  • Quantifies movement of items between

nodes in a graph

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Cartograms 1

  • Heilmann et al., InfoVis 2004
  • Distort maps so area reflects population

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SLIDE 4

4/7/2014 4 Cartograms 2

  • Dorling, Barford,

Newman, IEEE TVCG, 12(5), 2006

  • Distorts geographic

map based on scalar data values

  • Net emigration
  • ver last 50 years

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Tracking Avian Flu Outbreaks

  • Proulx et al., IEEE VAST, 2006

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Tracking Avian Flu Outbreaks

  • Proulx et al., IEEE VAST, 2006

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SLIDE 5

4/7/2014 5

Multi-Dimensional Scaling

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Multi-Dimensional Scaling

  • Family of algorithms to reduce high-dimensional data

down to 2D or 3D

– So we can visualize it – Reduce complexity of higher-dimensional relationships

  • Aims to preserve distances in higher dimensional

space after the dimension reduction

– Close things in high-dimension space are close in 2D or 3D – Far things remain far apart

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H-BLOBs

  • Sprenger, Brunella, Gross; Vis 2000
  • Splatting in 3D

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SLIDE 6

4/7/2014 6

ThemeView

  • Card, SIGGRAPH ‘96

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Tree Visualization

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Tree Visualization

  • Trees model hierarchical information very well
  • Structure found in many areas

– Corporation management hierarchy – Governments – Taxonomy – Data structures

  • Many visualization techniques

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SLIDE 7

4/7/2014 7

Node-link Diagrams

Tree Graph

Nodes Link Leaf nodes

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Visual Grammar of Node-Link Diagrams

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ConeTrees

  • Robertson et al., Comm. ACM 36(4), pp. 57-71

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SLIDE 8

4/7/2014 8

Tree “Extruded” in 3D

  • Wattenberg and Fisher,

IEEE InfoVis 2003

  • Tree structure showing

salient features of text

  • Tree extruded into 3D

with varying levels of detail in text

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Nature-Inspired Graph Layout

  • Carpendale and Agarawala, Info Vis 2004

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Showing Hierarchical Clusters

  • Pretorius and van

Wijk, TVCG 12(5), 2006

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SLIDE 9

4/7/2014 9

Hyperbolic Trees

  • Lamping and Rao,

UIST 1994

  • Focus+context

scheme for viewing large hierarchies

  • Smooth animated

changes between different foci

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TreeMaps

Herman et. al. IEEE Trans on Vis & CG, 6(1), 2000

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Tree Maps

  • www.smartmoney.com

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SLIDE 10

4/7/2014 10

Cushion Maps

  • Van Wijk and van de Wetering, InfoVis 1999
  • Multiple levels of

lighting group subtrees

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Voronoi Tree Maps

  • Balzer and Deussen, IEEE Vis 2005

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SLIDE 11

4/7/2014 11

GraphSplatting

  • van Liere and de Leeuw, IEEE TVCG 9(2), 2003
  • Add up 2D Gaussians centered at nodes

– Density field – All 2D scalar techniques available

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Rainbow

Visual Bibliography of Tree Vis (1) by Hans-Jorg Schulz

  • http://www.informatik.uni-rostock.de/~hs162/treeposter/poster.html

4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor 32

Visual Bibliography of Tree Vis (2) by Hans-Jorg Schulz

  • http://www.informatik.uni-rostock.de/~hs162/treeposter/poster.html

4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor 33

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SLIDE 12

4/7/2014 12

Graph Visualization

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Graph Visualization

  • Goals:

– Show all nodes clearly – Show all edges and avoid edge overlap

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Airline Flight Network

  • Czechoslovakia

Air Transport Company flight network

  • Edward R. Tufte,

Envisioning Information

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SLIDE 13

4/7/2014 13

Facebook Friend Wheel

  • Fletcher 2008

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Facebook Friend Wheel

  • Fletcher 2008

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Facebook Friend Wheel

  • Fletcher 2008

Russ Taylor

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SLIDE 14

4/7/2014 14

Radial Focus+Context Graphs

  • Jankun-Kelly and Ma, IEEE InfoVis 2003
  • For showing visual nodes (images, web

pages)

  • Level highlighting

– More space allocated to level of interest

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Social Network Identity Resolution

  • Bilgic et al., IEEE VAST, 2006

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Social Networks in Time

  • Card et al., VAST 2006
  • Expand tree in regions
  • f interest
  • Highlight nodes

matching search criteria

  • Slider widget to

change time

  • Animation to show

changes in time

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SLIDE 15

4/7/2014 15

Social Networking: Vizster

  • Heer and Boyd, InfoVis 2005

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Social Network Vis with Matrices and Graphs

  • Henry and

Fekete, IEEE TVCG 12(5), 2006

  • Reordered

matrices better for some tasks

  • Node-link

diagrams better for others

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Semantic Relationships

  • Wong, et al., IEEE VAST 2006

4/8/2014 Infovis, Genomics Tufte 45 Comp/Phys/APSc 715 Taylor

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SLIDE 16

4/7/2014 16

Graph Edge Reduction

  • Perer and

Shneiderman, IEEE TVCG 12(5), 2006

  • Grouping by

country

  • Interconnection

among groups

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Small World Graphs

  • van Ham and van Wijk, InfoVis 2004

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Rainbow

Viewing Large Graphs

  • Abello, van Ham, Krishnan, IEEE TVCG 12(5), 2006

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Rainbow

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SLIDE 17

4/7/2014 17

Semi-transparent Lines

  • Wong et al., InfoVis 2005

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EdgeLens: Managing Edge Congestion in Graphs

  • Wong, Carpendale, Greenberg,

InfoVis 2003

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Topological Fish-Eye Lens for Large Graph Visualization

  • Ganser, Koren, North, InfoVis 2004
  • Approximates graph structure with less

detail

  • Points of focus are shown in higher detail

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SLIDE 18

4/7/2014 18

Reducing Clutter in Graphs

  • Kumar and

Garland, IEEE TVCG, 12(5) 2006

  • Remove edges

within clusters

  • Replace edges in

cliques with star edge glyphs

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Parallel Coordinates

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Parallel Coordinates

  • http://davis.wpi.edu/~xmdv/vis_parcoord.html

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SLIDE 19

4/7/2014 19 Parallel Coordinates with Color Map

  • Xie et al., IEEE VAST 2006

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Parallel Coordinates with Transfer Functions

  • Johansson et al., InfoVis 2005

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Parallel Coordinates over Time

  • Brodbeck and

Girardin, IEEE InfoVis 2003

  • Fading out

(lowering saturation) shows how data change through time

4/8/2014 Infovis, Genomics Tufte 57 Comp/Phys/APSc 715 Taylor

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SLIDE 20

4/7/2014 20 Showing Clusters in Parallel Coordinates

  • Artero et al., IEEE Info Vis,

2004

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Focus+Context in Parallel Coordinates

  • Brodbeck and Girardin, IEEE InfoVis 2003
  • Increase horizontal resolution in region of

focus

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Curvy PC

  • Graham and

Kennedy IV’03

  • Easier to follow

(good continuity)

  • Cubic splines
  • Separates

intersections

4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor

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SLIDE 21

4/7/2014 21

Curvy PC (2)

  • Graham and

Kennedy IV’03

  • Easier to follow

(good continuity)

  • Cubic splines
  • Separates

intersections

4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor

Curvy PC (3)

  • Moustafa and

Wegman, Graphics of large data sets

  • Zero-derivative, not

just smooth

  • Smooth, but

maintain clustering

not actually their image, but same idea

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Whisker and Star Plots

  • Each angle encodes a different property of the

data

  • Star plots connect the lines

Whisker Plot Star Plot

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SLIDE 22

4/7/2014 22

Star Glyphs + Parallel Coordinates

  • Fanea, Carpendale, Isenberg, InfoVis 2005
  • Star glyphs show how population varies in one

dimension

Where are the clusters?

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RadViz

  • Multiple axes from center to circle perimeter
  • >2 axis, not perpendicular
  • Interactive axis control

– Points follow – Helps reveal clusters

  • Novakova & Stepankova

– SMO’06

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Miscellaneous

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SLIDE 23

4/7/2014 23

Situational Awareness Visualization

  • Livnat et al., InfoVis

2005

  • What, when, where?
  • What - around circle
  • When - which ring
  • Where - indicated by

spatial embedding inside rings

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Showing Time-Varying Data With Flocking Boids

  • Moere, InfoVis 2004
  • Particle motion

controlled by data values

  • Attraction/repulsion

based on similarity

Unpredictable spatial embedding

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Theme River

  • Havre, Hetzler, Nowell; InfoVis 2000

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SLIDE 24

4/7/2014 24

NameVoyager

  • Wattenberg,

InfoVis 2005

  • Shows baby name

popularity over time in a stacked graph

  • Thickness of strip

indicates frequency of name

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Tufte

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Edward R. Tufte

  • Three books on information display

– The Visual Display of Quantitative Information – Envisioning Information – Visual Explanations

  • Beautiful and terrible examples
  • Rules of thumb
  • Design guidelines

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SLIDE 25

4/7/2014 25

Tufte: Visual Display

  • Train Schedule: Good visual organization

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Tufte: Visual Display

  • Multi-layered Graph: Show all the data

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Tufte: Visual Explanations

  • Storm Visualization Redesigned: Chartjunk

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slide-26
SLIDE 26

4/7/2014 26

Aside: How strong a grid?

  • Bartram, Cheung, Stone; TVCG 2011

– Tried a variety of densities – Asked users “set too high” – Asked users “set too low” – 20% opaque within good range

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Tufte: Visual Explanations

  • Medical Illustration Redesigned: Chartjunk

– Finer call-out lines, label directly on diagram

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Tufte: Visual Explanations

  • O-Ring Damage Redesigned

– Order by the important variable

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SLIDE 27

4/7/2014 27

Tufte: Visual Explanations

  • O-Ring Damage Redesigned

– Order by the important variable

4/8/2014 Infovis, Genomics Tufte 79 Comp/Phys/APSc 715 Taylor 4/8/2014 Infovis, Genomics Tufte Comp/Phys/APSc 715 Taylor

Credits

  • Node-link diagram discussion: Information

Visualization by Colin Ware

4/8/2014 Infovis, Genomics Tufte 81 Comp/Phys/APSc 715 Taylor