Understand Users’ Comprehension and Preference for ComposingInformation Visualization
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Understand Users’ Comprehension and Preference for ComposingInformation Visualization
Huahai Yang, Yunyao Li & Michelle ZhouIBM Almaden Research Center
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Data Visualization for Decision Making
Motivation
Image from dynamifitness.com
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Composing Visualizations
Motivation
Crossed Bar
ChooseWhich One?
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Visual Insights
Motivation
Choice of Composition
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Visual Insights
Motivation
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Visual Insights
Women exercising three to six times a week have the lowest overweight ratio.
Motivation
Extrema Identification
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Visual Insights
Motivation
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Visual Insights
For men, increased frequency of exercise is associated with reduced overweight ratio; for women, the same relation remains for a while, then reverses itself.
Motivation
Correlation Comparison
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Research Questions
1. How do people comprehend visualization to derive visual insights?
2. Which composite visualization is preferred for deriving an insight?
Goal
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Ask People to Describe Visualization
Study 1 Method
Image from goanimate.com
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Crowd-sourcing Descriptions
514 turkers50 turkers per composition
10 compositions30 charts in total
1542 acceptable descriptionsaverage 47 words
Study 1 Method
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Content Analysis on Descriptions
Study 1 Method
• Three coders• Three months of coding
• Iterative process• Cognitive semantics (Johnson
1990)
• High level of inter-coder reliability • Krippendorff's alpha > .9
Image from goanimate.com
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Research Questions
1. How do people comprehend visualization to derive insights?a. What kinds of insights are derived?
Study 1 Results
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Four Basic Insights• Value Read (Va)
“The average ratio over the desirable weight of male is 1.14 approx. ”
Study 1 Results - Insights
1.14 approx.
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Four Basic Insights• Extrema Identification (Ex)
“Women exercising three to six times a week have the lowest overweight ratio”
Study 1 Results - Insights
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Four Basic Insights• Distribution Characterization (Di)
“Women’s overweight ratio goes down three times, then goes back up once.”
Study 1 Results - Insights
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Four Basic Insights• Correlation (Co) “The more men exercise, the lower is their overweight ratio.”
Study 1 Results - Insights
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Four Comparative Insights
Basis of comparison: all four basic insights
• Value Comparison (VC)• Extrema Comparison (EC)• Distribution Comparison (DC)• Correlation Comparison (CC)
Study 1 Results - Insights
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Research Questions 1. How do people comprehend visualization to derive
insights?a. What kind of insights are derived? b. How do insights distribute?
Study 1 Results
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Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution
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Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution
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Insights Distribution for One Chart
Study 1 Results – Insights Distribution
All Participants
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Insights Patterns
Study 1 Results – Insights Distribution
15 patterns
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Overall Insight Distribution: Zipf’s LawValue Comparison
Extrema
Correlation
Study 1 Results – Insights Distribution
Rank insight patterns for all 30 charts by frequency
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Research Questions
2. Which composite visualization is preferred for deriving an insight?
Study 2 Results
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Preference Rankings for Basic Insights
Study 2 Results
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Preference Rankings for Comparative Insights
Study 2 Results
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Often Most Preferred: Crossed Bar
Study 2 Results
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Often Most Preferred: Crossed Barfor 6 out of 8 Insights
Study 2 Results
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Comparing Correlations: Crossed Line Best
Study 2 Results
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Which Composition David Wants?
Motivation
• To know the most effective exercise frequency for female clients
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Which Composition David Wants?
Motivation
• To know the most effective exercise frequency for female clients
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Which Composition David Wants?
Motivation
• To compare between males and females the effect of exercise frequency on overweight in general
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Which Composition David Wants?
Motivation
• To compare between males and females the effect of exercise frequency on overweight
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Contributions• Methodology
• Large scale crowding-sourcing visualization description• Content analysis via cognitive semantics
• Taxonomy of visual insights • Empirical foundation for advanced visualization
systems• Auto-composition of visualizations• Natural language driven visualization generation and retrieval
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Thank you! Questions?
For more information: [email protected]
Yang, H., Li, Y., & Zhou, M. X. (2014). Understand users’ comprehension and preferences for composing information visualizations. ACM Transactions on Computer-Human Interaction (TOCHI), 21(1), 6.