introduction to information visualization - computer...

ECS289H, Winter 2005

Introduction to

Information Visualization

Kwan-Liu Ma


Outline

• Visualization definition

• Visualization process

• Scientific visualization vs. informationvisualization

• Visualization samples

• Information visualization: challenges anddirections

• Readings


Visualization

• To understand something is called “seeing it”.

• Visualizations

– Convey information using graphics means

– Amplify cognition

• The purpose of visualization is insight

– Communicate a known idea, or

– Create or discover new ones

• Graphical aids for thinking is not new!


Visualization

• The advent of computer makes possible morepowerful participating exploration and discoveryexperience

• Visualization began as a field since 1987– Visualization in Scientific Computing, an NSF report

• Visualization now and then– Interactive, semi-automated, 3D and beyond

– Still, hand-crafted, 1D and 2D


Visualization and Medicine

Leonardo da Vinci


Visualization and Meteorology

Leonardo da Vinci, 1515

East Asia coastal cyclone, NCAR

Edmond Halley, 1696


Visualization and Engineering

Ibrahim Yavuz and Andrei Smirnov using Ensight

Eric Lum


Visualization Process

Filtering

Mapping

Rendering

Viewing

Data Source

User

Inte

rfaces

user

Cognitive

Processing


SciVis vs. InfoVis

• Classifications

– Spatialization given versus chosen—T. Munzner

– Physical versus abstract

– Applications

– Algorithms—M. Tory and T. Möller, InfoVis 2004

– Objectives

• Do we need a distinction?


Scientific Visualization

NCSA



http://www.smartmoney.com/marketmap Size=market cap Color=price performance



Chandra X-ray Observatory Simulation by TSI and visualization by UCD



Visualization study of the NSFNET, NCSA



SCIRun, Utah Internal defibrillation simulation



Microarray data analysis, NIH

Hierarchical clustering explorer, HCI Lab, Univ. of Maryland



Virtual Wind Tunnel, NASA Ames Research Center




Information VisualizationStructure of Music

Bach, Goldberg Variations

Chopin, Mazurka in F# Minor

Martin Wattenberg http://www.turbulence.org/Works/song/method/method.html


What are the challenges?

• Large

• Multivariate

• Multidimensional

• Multimodalities

• Time-varying

• Complex and/or evolving geometry

• Noisy

• Incomplete samples

• Usability


ECS289H: Information Visualization

• In this class, we study how to effectively conveyinformation using computer graphics means

• We consider all types of data including text, documents,maps, relations, computer networks, social networks,inventories, business transactions, web, processes,images, …

• Beyond interactive diagrams

• HCI

• Fundamental topics:– Space

– Color

– Interaction

– Perception and cognition


Readings


Papers and PresentationsJanuary 24

Data, Taxonomy, Design SpacesReadings:

1. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, B. Shneiderman,

Symposium on Visual Languages 1996. [at CiteSeer]

2. The Structure of the Information Visualization Design Space, S. Card and J. Mackinlay, InfoVis '97.[pdf]

3. A Knowledge Task-Based Framework for Design and Evaluation of Information Visualization, R. Amarand J. Stasko, InfoVis 2004.[pdf]

January 26

PerceptionReadings:

1. Internal vs. External Information in Visual Perception, R. Rensink, 2nd Int. Symposium on SmartGraphics, pp. 63-70, 2002.[pdf]

2. Perceptual and Interpretative Properties of Motion for Information Visualization, L. Bartram, Proc. 1997Workshop on New Paradigms in Information Visualization and Manipulation, 1997, pp. 3-7.

3. Face-based Luminance Matching for Perceptual Colormap Generation, G. Kindlmann, E. Reinhard, andS. Creem, Proceedings of Visualization 2002. [pdf and software]

4. Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization, C. Healey and J.

Enns, IEEE Transactions on Visualization and Computer Graphics 5(2):145-167, 1999.[pdf]

January 31

Multivarite/Multidimensional Data Visualization IReadings:

1. Multidimensional Detective, A. Inselberg, InfoVis '97.

2. Visualizing Multivariate Functions, Data, and Distributions, Mihalisin et al., IEEE CG&A, May 1991.3. Worlds Within Worlds: Metaphors for Explorating n-Dimensional Virtual Worlds, S. Feiner and C.

Beshers, ACM UIST ‘90. [at CiteSeer]

4. HyperSlice: Visualization of Scalar Functions of Many Variables, J. van Wijk and R. van Liere,Visualization '93.


Papers and Presentations

February 2

Multivariate/Multidimensional Data Visualization IIReadings:

• Fast Multidimensional Scaling through Sampling, Springs and Interpolation, A. Morrison, G. Ross, M.Chalmers, Information Visualization 2(1), March 2003.

• Hierarchical Parallel Coordindates for Visulizing Large Multivariate Data Sets, Y.-H. Fua, M. Ward, and E.Rundensteiner, Visualization '99.

• Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration of High DimensionalDatasets, J. Yang, W. Peng, M. Ward, and E. Rundensteiner, InfoVis 2003. [at CiteSeer]

February 7

Distortion, Focus+Context MethodsReadings:

1. The FISHEYE View: A New Look at Structured Files, G. Furnas, Readings in Information Visualization,1999.[at CiteSeer]

2. A Review and Taxonomy of Distortion-Oriented Presentation Techniques, Y. Leung and M. Apperley,ACM Transaction on CHI, 1(2), June 1994.

3. Nonlinear Magnification Fields, A. Keahey and E. Robertson, InfoVis '97.[pdf]

4. Extending Distortion Viewing Techniques from 2D to 3D Data, M. Sheelagh, T. Carpendale, D.

Cowperthwaite, and D. Fracchia, IEEE CG&A, July 1997.[pdf]

5. The Table Lengs: Merging Graphical and Symbolic Representations in an Interactive Focus+ContextVisualization for Tabular Information, R. Rao and S. Card, CHI '94, pp. 318-322. [pdf]

6. The Hyperbolic Browser: A Focus+Context Technique for Visualizing Large Hierarchies, J. Lamping andR. Rao, Journal of Visual Languages and Computing, 7(1), 1996. [at CiteSeer]


Papers and PresentationsFebruary 9

Interactive NavigationReadings:

1. Space-Scale Diagrams, G. Furnas and B. Bederson, CHI '95. [pdf]

2. Pad++: A Zooming Graphical Interface for Exploring Alternate Interface Physics, B. Bederson and J. Hollan,UIST '94. [pdf]

3. Smooth and Efficient Zooming and Panning, J. van Wijk and W. Nuij, InfoVis 2003. [pdf]

4. Design Guidelines for Landmarks to Support Navigation in Virtual Environments, G. Norman, CHI '99. [pdf]

February 14

Tree and Hierarchy VisualizationReadings:

1. Treemaps, B. Johnson and B. Shneiderman, Visualization '91.

2. Cone Tree, G. Robertson, J. Mackinlay, and S. Card, CHI '91. [at CiteSeer]

3. Space Tree, C. Plaisant, J. Grosjean, and B. Bederson, InfoVis 2002. [Project page]

4. Navigating Large Network with Hierarchies, S. Eick and G. Wills, Visualization 93. [at CiteSeer]

February 16

Graph VisualizationReadings:

1. Graph Visualization and Navigation in Information Visualization, I. Herman, g. Melancon, M. Marshall,

TVCG, January-March 2000. [at CiteSeer]

2. Topological Fisheye Views for Visualizing Large Graphs, E. Gansner, Y. Koren, and S. North, InfoVis 2004.[pdf]

3. Animated Exploration of Graphs with Radial Layout, K.-P. Yee, D. Fisher, R. Dhamija, and M. Hearst,InfoVis 2001. [pdf]

4. H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space, T. Munzner, InfoVis '97. [Project page]


Papers and PresentationsFebruary 21

Dynamic Queries and Interactive AnalysisReadings:

1. Dynamic Queries for Visual Information Seeking, B. Shneiderman, IEEE Software, 11(6), 1994. [at CiteSeer]

2. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays, C. Ahlberg and B.Shneiderman, CHI '94. [at CiteSeer]

3. SDM: Selective Dynamic Manipulation of Visualizations, M. Chuah, S. Roth, J. Mattis, and J. Kolojejchick,UIST '95. [at CiteSeer]

4. VisDB: Database Exploration using Multidimensional Visualization, D. Keim and H.-P. Kriegel, IEEE CG&A,

1994.[pdf]

February 23

EvaluationReadings:

1. The Perceptual Evaluation of Visualization Techniques and Systems, Appendix C, Information Visualization,C. Ware, Morgan Kaufmann, 2004.

2. Snap-together Visualization, C. North and B. Shneiderman, Intl. Journal of Human-Computer Studies,Academic Press, 53(5), November 2000. [AVI 2000 version at CiteSeer]

3. An Evaluation of Microarray Visualization Tools for Biological Insight, P. Saraiya, C. North, and K. Duca,InfoVis 2004. 4. The Challenge of Information Visualization Evaluation, C. Plaisant, AVI 2004.

February 28

Document visualizationReadings:1. Visualization for the Document Space, X. Lin, Visualization '922. Galaxy of News, E. Rennison, UIST '94. [pdf]

3. Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents(Themescapes), J. Wise et al., InfoVis '95.

4. WebBook and Web Forager, S. Card and G. Robertson, CHI '96. [pdf]


Papers and Presentations

March 2

Software VisualizationReadings:• SeeSoft: A Tool for Visualizing Line Oriented Software Statistics, S. Eick et al., IEEE Transaction on

Software Engineering, 18(11), 1992, pp. 957-968.• Space-Filling Software Visualization, M. Baker and S. Eick, Journal of Visual Language and

Computing, 6(2), 1995• Managing Software with New Visual Representations, M. Chuah, S. Eick, InfoVis '97. [at CiteSeer]

• Execution Patterns in Object-Oriented Visualization, W. Pauw et al., COOTS '98. [pdf]

March 7, March 9, March 14 (March 11, Friday)

Final project presentations


Homework 1

• Visualization critiques

• An HTML file is due 5pm, January 16

• In-class presentation on January 17

Business 2.0, June 2004


Class Home Page

http://www.cs.ucdavis.edu/~ma/ECS289H