Uncertainty Visualization InfoVis ~ Winter 2008 ~ Torre Zuk

What Uncertainty?• Uncertainty in measurements or data from

precision– E.g. 50 kg +/- 0.5kg

• Uncertainty in measurements or data from temporal changes– E.g. Shares of RIMM 120.12

• Uncertainty in confidence– E.g. What should I have for lunch?

• …

Uncertainty Typology

source independenceInterrelatednessamount of judgment includedSubjectivityassessment of info sourceCredibilitytemporal gaps from info collectionCurrency/Timingconduit through which info passedLineageextent to which info components agreeConsistencyextent to which info is comprehensiveCompletenessexactness of measurementPrecisiondifference between observation & realityAccuracy/Error

Thomson et al. A typology for visualizing uncertainty. In Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 5669: Visualization and Data Analysis 2005, pages 146–157, 2005

Uncertainty Visualizations

• Visualizing Data Uncertainty

Uncertainty Visualizations

• Visualizing Uncertainty for Tasks/Decisions

National Oceanic and Atmospheric Administration National Weather Service

Visualizing Uncertainty

• Uncertainty is relevant to comprehension and decision-making but may be left out

• Interpretation may be difficult enough without extra cognitive load

What are the best ways to integrate uncertainty into visualizations?

Uncertainty as Metadata

• Example A • Example B

• same base data• variation in metadata

Metadata and Representations

• Example A [Nirvana] • Example B [Paul Anka]

• Variables [Krygier 1994]: loudness, pitch, register, timbre, duration, rate of change, order, attack/decay

• audience• …

Uncertainty Visualization Pipeline

A. T. Pang, C. M. Wittenbrink, and S. K. Lodha, “Approaches to uncertainty visualization,” The Visual Computer 13(8), pp. 370–390, 1997

Analysis of Uncertainty Visualization

• Any visual cue may be used to represent uncertainty, but which are best?

– Taxonomic approach – Semiotic: icon, index, symbol– Perceptual & cognitive theory (InfoVis)– Human factors and task constraints

Analysis for Order

• Any visual cue may be used to represent uncertainty, but which are best?

– Taxonomic approach– Semiotic: icon, index, symbol– Perceptual & cognitive theory (InfoVis)– Human factors and task constraints

Approaches to uncertainty visualization [Pang et al. 1996]

Categorizations• datum values

– scalar, vector, tensor, multivariate• location of the datum

– 0D, 1D, 2D, 3D, time, …• visualization axes mapping

– experiential or abstract• extent of both location and value

– discrete or continuous

Approaches to uncertainty visualization [Pang et al. 1996]

• Uncertainty encoding– add glyphs– add geometry– modify geometry– modify attributes – animation – sonification– psychovisual

Representations for Uncertainty

• Vector fields

C. M. Wittenbrink, A. T. Pang, and S. K. Lodha, “Glyphs for visualizing uncertainty in vector fields,”IEEE TVCG 2(3), pp. 266–279, 1996.

Representations for Uncertainty

Zuk et al., Visualizing temporal uncertainty in 3D virtual reconstructions. VAST 2005.

• Temporal uncertainty in Archaeology

Analysis for Fun & Profit

• Any visual cue may be used to represent uncertainty, but which are best?

– Taxonomic– Semiotic– Perceptual & Cognitive Theory (InfoVis)– Human factors and task constraints

Representations for Uncertainty• MacEachren’s Focus Variables [1992]

– edge crispness (blur)– fill crispness (blur)– resolution– fog (transparency)

• MacEachren’s Clarity Variables (revision) [1995]– crispness– resolution– transparency

Representations for Uncertainty

• MacEachren’s Clarity Variables [1995]– crispness– resolution– transparency

Representations for Uncertainty

• MacEachren’s Clarity Variables [1995]– crispness– resolution– transparency

Representations for Uncertainty

• MacEachren’s Clarity Variables [1995]– crispness– resolution– transparency

Representations for Uncertainty

• MacEachren’s Clarity Variables [1995]– crispness– resolution– transparency

Representations for Uncertainty

• MacEachren’s Clarity Variables [1995]– crispness– resolution– transparency

• Why these variables?– Icon, index, symbol

Representations for Uncertainty

• Virtual reconstructions

T. Strothotte, M. Puhle, M. Masuch, B. Freudenberg, S. Kreiker, and B. Ludowici, “Visualizing Uncertainty in Virtual Reconstructions,” in Proceedings of Electronic Imaging & the Visual Arts, EVA Europe ’99, VASARI, GFaI, (Berlin), 1999.

• Molecular positional uncertainty

P. Rheingans and S. Joshi. Visualization of molecules with positional uncertainty. Data Visualization ’99, pages 299–306. Springer-Verlag Wien, 1999.

Representations for Uncertainty

Analysis for Design

• Any visual cue may be used to represent uncertainty, but which are best?

– Taxonomic– Semiotic– Perceptual & cognitive theory (InfoVis)– Human factors and task constraints

Heuristic Evaluation• Evaluated 8 existing uncertainty visualizations • Amalgamated infovis theory to develop 12 general

heuristics

Zuk and Carpendale, Theoretical Analysis of Uncertainty Visualizations. SPIE VDA 2005.

Local contrast affects color & gray perception

Color perception varies with size of colored item

BertinQuantitative assessment requires position or size variation

Bertin & WarePreattentive benefits increase with field of viewWareConsider people with color blindnessWareLocal contrast affects color & gray perceptionWare & BertinColor perception varies with size of colored itemBertin & WareDon’t expect a reading order from colorTufte & WareIntegrate text wherever relevantWareConsider Gestalt LawsTufteRemove the extraneous (ink)Tufte & WareProvide multiple levels of detailTuftePut the most data in the least spaceTufte & BertinPreserve data to graphic dimensionalityBertin & WareEnsure visual variable has sufficient lengthSourceHeuristic

Heuristic Evaluation

Representations for Uncertainty

• Medical segmented surfaces

G. Grigoryan and P. Rheingans, “Point-based probabilistic surfaces to show surface uncertainty,” IEEE TVCG 10(5), pp. 564–573, 2004..

Representations for Uncertainty

• Procedural rendering

Andrej Cedilnik and Penny Rheingans. Procedural rendering of uncertainty information.In T. Ertl, B. Hamann, and A. Varshney, editors, Proceedings Visualization 2000..

Analysis for Cognitive Support

• Any visual cue may be used to represent uncertainty, but which are best?

– Taxonomic– Semiotic– Perceptual & cognitive theory (InfoVis)– Human factors and task constraints

• Experiment – Reasoning under uncertainty

Cognition and Uncertainty

y = 0.07x + 0.21R2 = 0.6662

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outliers

• Experiment I – Observations by Person I

y = 0.07x + 0.2101R2 = 0.6667

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oversampling

• Experiment I – Observations by Person II

• From only the options below person II (data above) is most likely: HCI student InfoVis student non-technical InfoVis student Graphics student

y = 0.07x + 0.2101R2 = 0.6667

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B C

Judgment Question

Types of Heuristics and Biases

• Associations– affect, availability, recency bias, …

• Ignorance of Rules– representativeness, statistics, ...

• Application of Rules– automation bias, adjustment and anchoring, …

Visualizing Reasoning under Uncertainty• People have reasoning heuristics and biases

[Tversky and Kahneman]

• Supporting the decision may be as important as seeing the data uncertainty

Zuk and Carpendale, Vis. Uncertainty in Reasoning, Smart Graphics 2007.

Task & Cognitive Constraints:Supporting Evidence-based Diagnosis

© 2007 W21C

Collaboration with W. Ghali and B. Baylis, UofC Medicine

• Uncertainty in test data, test implications, strategies, diagnosis, …

Supporting Evidence-based Diagnosis

Bayes Theorem

A Bab

( | ) ( )( | )( | ) ( ) ( | ) ( )

P B A P AP A BP B A P A P B A P A

=+ ¬ ¬

Supporting Evidence-based DiagnosisObservational Study

• Understand the problem domain• Assess existing support at W21C

© 2007 C. Tang

Supporting Evidence-based DiagnosisAnalysis & Design

• Task model with uncertainty

• Design recommendations for new support• Participatory design

Supporting Evidence-based DiagnosisTest Results: multiple representations

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DPDTPDPDTPDPDTPTDP

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GeneralizationsDirectives for Uncertainty Visualization

1. Provide support for cognitive task simplification.2. Support emphasis and de-emphasis of uncertainty

information.3. Support viewing of uncertainty as metadata and as

separate data.4. Allow the user to select realizations of interest.5. Mitigate cognitive heuristics and biases with reasoning

support.6. Provide interaction to assist knowledge creation.7. Assess the implications of incorrectly interpreting the

uncertainty.

Zuk, Visualizing Uncertainty, PhD Thesis. 2008.

Acknowledgements