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WOLFGANG AiGNER perception and visualization 1

perception andvisualization

Wolfgang Aigneraigner@ifs.tuwien.ac.at

http://ieg.ifs.tuwien.ac.at/~aigner/

wolfgang.aigner@donau-uni.ac.athttp://ike.donau-uni.ac.at/~aigner/

Version 1.05.11.2009

WOLFGANG AiGNER perception and visualization 2

Part Aperception

WOLFGANG AiGNER perception and visualization 3London 1854

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Analytical ReasoningProcess

[Thomas & Cook, 2005]

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Visual Perception

[Goldstein, 2005]

Light reflected from map

Dr. Snow’s eye

Pattern of light and darkis formed on Dr. Snow’s retina

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Human Eye

[Goldstein, 2005]

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Brain Pixel

[Ware, 2008]

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Visual Perception

Optic Nerve

Primary Visual Cortex

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Immediate perceptionImmediate understanding - no learning necessary

Behaviour can‘t be forgottenOptical illusions are seen even when knowing that they are illusions

Sensual immediacyCertain phenomena are perceived very quickly because they are notlearned but „hardwired“ in the brain

Studied by brain research

Effective and stable

Mostly innate and cultural invariant

Phenomena of immediate perception (e.g., color and pattern perception)can be generalized to mankind

But also learned differentiations of the brain: most perceptual processes arebased on a combination of innate and learned mechanisms

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Optical illusions

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Müller-Lyer Illusion

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Conventional representations

Hard to learne.g., script

Easy to forgetBut there are visual representations that can‘t be

forgotten easily (e.g., numbers)

Embedded within cultural context

Powerful form of representatione.g., mathematical symbols

Easy to change

Studied by e.g., psychology, sociology, HCI

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Preattentive Processing

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Preattentive Processing

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1561321203658413076510374627417312752732759273299070974217037077741795279317492709734019743217909370945179279417

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Color

Hue

[Dürsteler, 2006]

Intensity

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Form

[Few, 2004]

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Movement

(Direction of) motion Flicker

[Dürsteler, 2006]

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Gestalt Laws

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Proximity

[Winkler, 2005]

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Similarity

[Winkler, 2005]

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Prägnanz

[Goldstein, 2005]

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Good Continuation

[Winkler, 2005]

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Common Fate

[Pedroza, 2005]

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Familiarity

[Schmidt, 2005]

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Symmetry

[Ware, 2004]

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Enclosure

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Connection

[Ware, 2004]

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Closure

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Change Blindness

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Inattentional Blindness

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Cognition

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Analytical Reasoning Process

[Thomas & Cook, 2005]

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Resources[Thomas and Cook, 2005] J.J. Thomas and K.A. Cook, eds., Illuminating the Path: The Researchand Development Agenda for Visual Analytics, IEEE CS Press, 2005;http://nvac.pnl.gov/agenda.stm.

[Card et al., 1999] Card, S. and Mackinlay, J. and Shneiderman, B., Readings in InformationVisualization: Using Vision to Think, Morgan Kaufmann Publishers, 1999.

[Healey, 2009] Christopher G. Healey, Perception in Visualization, Retrieved at: November 2,2009. http://www.csc.ncsu.edu/faculty/healey/PP/index.html

[Dürsteler, 2005] Juan C. Dürsteler, Processes that pop out, Inf@Vis! (The digital magazine ofInfoVis.net), Created at: Feb. 12, 2006, Retrieved at: Feb. 16, 2006,http://www.infovis.net/printMag.php?num=179&lang=2

[Few, 2004] Stephen Few, Data Presentation: Tapping the Power of Visual Perception,intelligent enterprise, September 2004.http://www.intelligententerprise.com/showArticle.jhtml?articleID=31400009

[Bertin, 1981] Bertin, J. Graphics and Graphic Information Processing. Walter De Gruyter, Inc.,Berlin, 1981.

[Ware, 2008] Ware, C. Visual Thinking for Design, Morgan Kaufmann, Burlington, MA, 2008.

[Ware, 2004] Ware, C., Information Visualization: Perception for Design, Second Edition, MorganKaufmann, San Francisco, 2004.

[Goldstein, 2005] Goldstein, Bruce. Cognitive Psychology, Thomson Wadsworth, 2005.

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Part Bvisualization

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Goal

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Why visualization?

[Few, 2006]

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Why visualization?

[Card et al., 1999]

Increasing cognitive resourcessuch as by using a visual resource to expand human working memory

Reducing searchsuch as by representing a large amount of data in a small space

Enhancing the recognition of patternssuch as when information is organized in space by its time relationships

Supporting the easy perceptual inference of relationshipsthat are otherwise more difficult to induce

Perceptual monitoring of a large number of potential events

Providing a manipulable mediumthat, unlike static diagrams, enables the exploration of a space ofparameter values

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Method

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Data

[garysaid.com]

year

length

popularity

subject

award?

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Visual Mapping

[Spotfire]

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Raw data

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Visual Mapping

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Interactivity / Dynamic Queries

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Visual Variables

[Mackinlay, 1987][Cleveland & McGill, 1984]

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Graphical Excellence

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Tell the truth about thedata

[Tufte, 1983]

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Fuel Economy StandardRedesign

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Lie Factor

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Beer Sales Redesign

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Avoid Chartjunk

[Jansen & Scharfe, 1999]

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Tufte Design Principles

1. Above all else show the data.

2. Maximize the data-ink ratio.

3. Erase non-data-ink.

4. Erase redundant data-ink.

5. Revise and edit.

[Tufte, 1983]

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Example

[Seed, 2006] Edit Staff, State of the Planet - Bigger, Faster, Stronger, More, Seed - seedmagazine.com,

Created at: April 20, 2006, Retrieved at: June 21, 2006. http://www.seedmagazine.com/news/2006/04/state_of_the_planet.php

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Redesign

[Weber et al., 2006] Sonja Weber, Christof Kopfer, Matteo Savio, Nicole Brosch, Category 4-5 Hurricances,

Created at: November 14, 2006, Retrieved at: November 3, 2009. http://www.infovis-wiki.net/index.php?title=Image:Verbessert3.jpg

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Using Color

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Color Context

Rule #1: If you want different objects of the same color in a table or graphto look the same, make sure that the background--the color thatsurrounds them--is consistent.

Rule #2: If you want objects in a table or graph to be easily seen, use abackground color that contrasts sufficiently with the object.

[Few, 2008]

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Color UsageRule #3: Use color only when needed to serve a particular communicationgoal.

Rule #4: Use different colors only when they correspond to differences ofmeaning in the data.

To highlight particular dataTo group itemsTo encode quantitative values

[Few, 2008]

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Color UsageRule #5: Use soft, natural colors to display most information and brightand/or dark colors to highlight information that requires greater attention.

[Few, 2008]

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Palette TypesCategorical

Sequential

Diverging

Rule #6: When using color to encode a sequential range of quantitative values,stick with a single hue (or a small set of closely related hues) and vary intensityfrom pale colors for low values to increasingly darker and brighter colors for highvalues.

[Few, 2008]

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Example

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De-emphasize non-data componentsRule #7: Non-data components of tables and graphs should be displayed justvisibly enough to perform their role, but no more so, for excessive salience couldcause them to distract attention from the data.

[Few, 2008]

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Avoid Red-Green

Rule #8: To guarantee that most people who are colorblind candistinguish groups of data that are color coded, avoid using a combinationof red and green in the same display.

[Few, 2008]

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Avoid visual effects

Rule #9: Avoid using visual effectsin graphs.

[Few, 2008]

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InfoVis Reference Model

[Card et al., 1999]

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Visualization Design Process

Analyze problem

Design solutions

Evaluate solutions

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Resources[Card et al., 1999] Card, S. and Mackinlay, J. and Shneiderman, B., Readings in Information Visualization: UsingVision to Think, Morgan Kaufmann Publishers, 1999.

[Fekete et al., 2008] Fekete. J.. Wijk, J. J., Stasko. J. T.. and North, C. 2008. The Value of InformationVisualization. in Information Visualization: Human-Centered Issues and Perspectives, A. Kerren. J. T. Stasko, JFekete. and C. North. Eds. Lecture Notes In Computer Science, vol. 4950. Springer-Verlag. Berlin, Heidelberg,1-18.

[Few, 2009] Stephen Few, Now You See It: Simple Visualization Techniques for Quantitative Analysis,Analytics Press, 2009.

[Few, 2004] Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press,2004.

[Few, 2008] Stephen Few, Practical Rules for Using Colors in Charts, Visual Business Intelligence Newsletter,February 2008.

[North, 2007] Chris North, Information Visualization Lecture Notes, Virginia Tech,http://people.cs.vt.edu/~north/

[Shneiderman, 1996] Ben Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for InformationVisualizations. In Proceedings of the IEEE Symposium on Visual Languages, pages 336-343, Washington. IEEEComputer Society Press, 1996.

[Thomas and Cook, 2005] J.J. Thomas and K.A. Cook, eds., Illuminating the Path: The Research andDevelopment Agenda for Visual Analytics, IEEE CS Press, 2005; http://nvac.pnl.gov/agenda.stm.

[Tufte, 1983] Edward R. Tufte, The Visual Display of Quantitative Information. 2nd Edition, Graphics Press,Cheshire, Connecticut, 1983.

[Tufte, 1990] Edward R. Tufte, Envisioning Information, Graphics Press, Cheshire, CT 1990.

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Acknowledgements

Thanks to Monika Lanzenberger, Margit Pohl and Markus Resterwhose slides form the basis of this presentation.