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Dresden, 21.01.2015 The effectiveness of uncertainty visualization in a Coordinated Multiple View" environment using a temporal dataset (case study) Lisa Clemens Fakultät Umweltwissenschaften Fachrichtung Geowissenschaften Institut für Kartographie

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Page 1: The effectiveness of uncertainty visualization in a ... · 5 von 17 2 State of the Art 2.1 Uncertainty Visualization Definition uncertainty: • “The difference between a real geographic

Dresden, 21.01.2015

The effectiveness of uncertainty visualization in a

“Coordinated Multiple View" environment using a

temporal dataset (case study)

Lisa Clemens

Fakultät Umweltwissenschaften Fachrichtung Geowissenschaften Institut für Kartographie

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Structure

1. Motivation

2. State of the Art

1. CMV

2. Uncertainty Visualization

3. Implementation

4. Evaluation

1. Experiment

2. Results

5. Discussion

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1 Motivation

• Big amounts of (complex) data, bigger proportions of uncertain data

• Coordinated Multiple Views (CMV) are the perfect exploration environment for complex data

Research objective:

• Analysis of methods to effectively, efficiently and satisfying visualize uncertainty in CMV

• Integrated uncertainty vs. additional uncertainty view

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2 State of the Art2.1 CMV

Definition:

• A CMV is a “specific exploratory visualization technique that enables the user to explore their data” (Roberts, J. C., 2007)

− To visualize complex data

− Different visualization forms show different perspectives of the data

− Interaction allows the user to adopt the visualization

� Example

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2 State of the Art2.1 Uncertainty Visualization

Definition uncertainty:

• “The difference between a real geographic phenomenon and the user’s understanding of the geographic phenomenon” (Longley, P. A. et al. 2005).

− Often also denoted as „data quality“

• Originates from data acquisition, transformation or visualization process

Definition uncertainty visualization:

• „The process to display data together with assisting uncertainty information in order to aid the user in data analysis and decision making“ (Pang, A. T., 1996).

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2 State of the Art2.1 Uncertainty Visualization

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[1]

[2]

[3]

[4]

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2 State of the Art2.1 Uncertainty Visualization

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[1]

[2]

[3]

[4]

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2 State of the Art2.1 Uncertainty Visualization

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[1]

[2]

[3]

[4]

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2 State of the Art2.1 Uncertainty Visualization

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[1]

[2]

[3]

[4]

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3 Implementation

Case study:

• Napoleon‘s Campaign in Russia 1812

• Battle at the Berezina 26.-29.11.1812

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Dataset:

• number of soldiers from 4 different sources

• time of crossing

• position of the troops

[5]

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3 Implementation

Case study:

• Napoleon‘s Campaign in Russia 1812

• Battle at the Berezina 26.-29.11.1812

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Dataset:

• number of soldiers from 4 different sources

• time of crossing

• position of the troops

[5]

Battle at the Berezina

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3 Implementation

Uncertainty integrated (3 views)

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3 Implementation

Additional Uncertainty view (4 views)

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4 Evaluation4.1 Experiment

Hypothesis:

• 3 views (integrated uncertainty) better

− Less cognitive load

− Different visualizations of uncertainty

• But:

− More complex

− Takes more time to understand in the beginning

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4 Evaluation4.1 Experiment

Experiment settings

• Think-aloud protocol

• Video recording

• Screen logging

Test users

• 2 equal control groups

• each 8 test users (16 in total)

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4 Evaluation4.1 Experiment

Test procedure

• Introduction

− Introduction script with information

− 3 test questions to pratice think-aloud

• Test

− 7 main questions

− 2 sub-questions

• Feedback:

− Questionnaire about user satisfaction

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Wrong answers: 10,7% 8,9%

4 Evaluation4.2 Results

Wrong answers: 10,7% 8,9%

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Effectiveness

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Wrong answers: 10,7% 8,9%

4 Evaluation4.2 Results

Wrong answers: 10,7% 8,9%

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Effectiveness

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Wrong answers: 10,7% 8,9%

4 Evaluation4.2 Results

Wrong answers: 10,7% 8,9%

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Effectiveness

3,0%

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4 Evaluation4.2 Results

3 views: Ø 332,5s

4 views: Ø 281,5s

� Hypothesis refuted, 4 views more effective and efficient

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Efficiency

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4 Evaluation4.2 Results

Mouse Interaction Density Map

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a) uncertainty integrated b) extra uncertainty view

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5 Discussion

Summary

• Additional uncertainty view more effective and efficient

• Integrated uncertainty confuses user

Further research

• Higher number of test persons

• Different case study dataset(s)

• Different uncertainty visualization methods

• 3 views vs. 4 views

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Sources

• J. C. Roberts. (2007). ‘State of the Art: Coordinated & Multiple Views in Exploratory Visualization’. In ‘Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007)’, pp. 61-71, IEEE Computer Society Washington, DC, USA, [ISBN:0-7695-2903-8].

• P. A. Longley, M. F. Goodchild, D. J. Maguire, D. W. Rhind. (2005). ‘Geographic Information Systems and Science’, Wiley, Chichester. [ISBN 0470870001].

• A. T. Pang, C. M. Wittenbrink, S. K. Lodha. (1996). ‘Approaches to Uncertainty Visualization’. The Visual Computer, Volume 13, Issue 8, pp. 370-390.

• [1] A. M. MacEachren. (1992). ‘VISUALIZING UNCERTAIN INFORMATION’. Cartographic Perspective, Volume 13, pp. 10-19. (modifyed)

• [2] http://understandinggraphics.com/wp-content/uploads/ 2010/01/ retinal-variables.png (modifyed)

• [3] http://slvg.soe.ucsc.edu/images.uncertainty/SurfLines.gif

• [4] http://support2.dundas.com/OnlineDocumentation/winchart2005/ChartType_Images/ErrorBarsChart.png

• [5] http://www.historyhome.co.uk/pict2/Moscow.jpg

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