VISUALIZING UNCERTAINTY Fall 2017 Mac Hill VISUALIZING UNCERTAINTY - - PowerPoint PPT Presentation

VISUALIZING UNCERTAINTY

Thesis Proposal

Fall 2017 Mac Hill

VISUALIZING UNCERTAINTY

How can the design of information visualizations commonly found 
 in news media coverage incorporate representations of uncertainty in situations that depict predictive data to facilitate non-expert decision making about current events?

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DEVELOPING A VISUAL VOCABULARY FOR UNCERTAINTY

VISUALIZING UNCERTAINTY 3

Incomplete or imperfect knowledge arising from a variety of factors including: measurement precision, completeness, inferences, disagreement, and credibility.

DEFINING UNCERTAINTY

Skeels, Meredith, et al. “Revealing Uncertainty for Information Visualization.”

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TYPES OF UNCERTAINTY

Skeels, Meredith, et al. “Revealing Uncertainty for Information Visualization.”

Inference Precision Completeness Disagreement

Uncertainty can be introduced during the collection, analysis, 


• r presentation of information. Multiple types of uncertainty can 


be present in a single information visualization.

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“Uncertainty is a fact of information; 
 all information contains uncertainty”

• A. M. MacEachren, et al. "Visual Semiotics & Uncertainty Visualization: An Empirical Study."

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The presentation of data has an impact on its meaning and its usefulness for decision making. Leaving out uncertainty provides a skewed and incomplete picture of the information being visualized.

WHY INCLUDE UNCERTAINTY?

Edward Tufte. Envisioning Information

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MISSING UNCERTAINTY

Visualizations like this one leave

• ut the uncertainty inherent in

predictive and polling data. In this case, the margin of error is larger than the lead one candidate has over another.

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Information visualizations in mass media often leave out representations of uncertainty. Instead they rely on percentages to convey doubt.

UNCERTAINTY IN MASS MEDIA

FiveThirtyEight.com, “Who Will Win the Presidency?”

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The raw data from the previous visualization looked more like this, with 1,106 polls combined together and several showing Trump winning.

FiveThirtyEight.com, “Who Will Win the Presidency?”

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“there are real shortcomings in how American politics are covered, including...
 a failure to appreciate uncertainty”

Silver, Nate. “The Real Story of 2016”. FiveThirtyEight.com

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STATISTICAL METHODS FOR VISUALIZING UNCERTAINTY

Error Bars Box Plots Violin Plot Confidence Intervals Blur

Statistics has some methods for visualizing uncertainty. These methods, however, require some level of statistical expertise to interpret, or like blur, are difficult to quantify.

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How can representations of uncertainty give non-experts a greater appreciation of the possible outcomes associated with a data set? How can interactive representations of uncertainty push non-experts 
 to perform more like experts? How can representations of uncertainty help non-experts quantify uncertainty?

RESEARCH QUESTIONS

How can the design of information visualizations commonly found in news media coverage incorporate representations of uncertainty in situations that depict predictive data to facilitate non-expert decision making about current events (specifically economic, political, and weather issues)?

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Mini Visual Studies Track III Observational Study Editorial Studies Scenarios

RESEARCH METHODS

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MINI VISUAL STUDIES: DATA SETS

Hurricane Path 
 Projections 2016 Presidential 
 Polls Presidential Approval Ratings Unemployment Rates TBD Type of Uncertainty Inference Disagreement Precision Disagreement Completeness Inference Completeness Inference Disagreement Inference Insight Type Location Trends Comparison Trends Distribution Trends Comparisons

Skeels, Meredith, et al. “Revealing Uncertainty for Information Visualization.” Börner, Katy. Atlas of Knowledge: Anyone Can Map.

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• f projections.

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Disagreement Uncertainty Trends Analysis

UNEMPLOYMENT PROJECTIONS BY 78 MAJOR US FIRMS

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MGD TRACK III OBSERVATIONAL STUDY

Track III studies looked at 2D, interactive, and animated methods for visualizing uncertainty.

Matt Norton

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EDITORIAL STUDIES

Editorial studies will focus on pictorial and metaphorical ways

• f incorporating uncertainty,

similar to this visualization from The New York Times.

“How to Reduce Shootings”, The New York Times

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Data Journalism Science Communication Visualizing Uncertainty Visualization Literacy Data Visualization Perception Conceptual Framework

Cairo, Alberto. “Ethical Infographics.” Dixon, Graham N., et al. “The Power of a Picture: Overcoming Scientifjc Misinformation by Communicating Weight-of- Evidence Information with Visual Exemplars.” Journal of Communication Nguyen, An, and Jairo Lugo-

• Ocando. “The State of Data

and Statistics in Journalism and Journalism Education: Issues and Debates.” Journalism Schrager, Allison. “The Problem with Data Journalism — Quartz.” Quartz Silver, Nate. The Real Story Of 2016 | FiveThirtyEight Solop, Frederic I., and Nancy

• A. Wonders. “Data Journalism

Versus Traditional Journalism in Election Reporting.” Electronic News Boggs, S. W. “Cartohypnosis.” The Scientifjc Monthly Bond, C. E., et al. “Knowledge Transfer in a Digital World: Field Data Acquisition, Uncertainty, Visualization, and Data Management.” Geosphere Grainger, Sam, et al. “Environmental Data Visualization for Non- Scientifjc Contexts: Literature Review and Design Framework.” Environmental Modelling & Software Johnson, Chris R., and Allen R. Sanderson. “A Next Step: Visualizing Errors and Uncertainty.” IEEE Computer Graphics and Applications McInerny, Greg J., et al. “Information Visualization for Science and Policy: Engaging Users and Avoiding Bias.” Ecology & Evolution Spiegelhalter, David, et al. “Visualizing Uncertainty About the Future.” Science Johnson, Chris R., and Allen R. Sanderson. “A Next Step: Visualizing Errors and Uncertainty.” IEEE Computer Graphics and Applications MacEachren, Alan M., et

• al. “Visual Semiotics &

Uncertainty Visualization: An Empirical Study.” IEEE Transactions on Visualizations and Computer Graphics Skeels, Meredith, et al. “Revealing Uncertainty for Information Visualization.” Information Visualization Klir, G.J. Uncertainty and Information: Foundations

• f Generalized Information

Theory Pang A.T et al. “Approaches to Uncertainty Visualization” The Visual Computer Börner, Katy, et al. “Investigating Aspects of Data Visualization Literacy Using 20 Information Visualizations and 273 Science Museum Visitors.” Information Visualization Maltese, Adam V., et

• al. “Data Visualization

Literacy: Investigating Data Interpretation Along the Novice-Expert Continuum.” Journal of College Science Teaching Börner, Katy, and David E.

• Polley. Visual Insights: A

Practical Guide to Making Sense of Data. Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication Mollerup, Per. Data Design: Visualising Quantities, Locations, Connections Rodrigues, José Fernando, et al. “The Spatial-Perceptual Design Space: A New Comprehension for Data Visualization.” Information Visualization Tufte, Edward R. Envisioning Information Fox, P . and Hendler, J. “Changing the equation

• n the scientifjc data

visualization.” Science McCandless, David. Knowledge is Beautiful Wilkinson, Leland. The Grammar of Graphics Kress, Gunther, and Theo van

• Leeuwen. Reading Images:

The Grammar of Visual Design Spoehr, Kathryn T., and Stephen W. Lehmkuhle. “Organization and Visual Processing.” Visual Information Processing, Drucker, Johanna. Graphesis : Visual Forms of Knowledge Production Zusne, Leonard. Visual Perception of Form Roth, William and Kenneth

• Tobin. “Cascades of

inscriptions and the re presentation of nature” Latour, Bruno. Science in Action: How to Follow Scientists and Engineers Through Society Norman, Donald. Things That Make Us Smart

LITERATURE

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Questions?