Understand Users’ Comprehension
and Preference for Composing
Information Visualization
Huahai Yang, Yunyao Li & Michelle Z...
Data Visualization for Decision Making
Motivation
Image from dynamifitness.com
Composing Visualizations
Motivation
Crossed Bar
Choose
Which One?
Visual Insights
Motivation
Choice of Composition
Visual Insights
Motivation
Visual Insights
Women exercising three to six times a week have the lowest
overweight ratio.
Motivation
Extrema Identifica...
Visual Insights
Motivation
Visual Insights
For men, increased frequency of exercise is associated with reduced
overweight ratio; for women, the same ...
Research Questions
1. How do people comprehend visualization to derive
visual insights?
2. Which composite visualization i...
Ask People to Describe Visualization
Study 1 Method
Image from goanimate.com
Crowd-sourcing Descriptions
514 turkers
50 turkers per composition
10 compositions
30 charts in total
1542 acceptable desc...
Content Analysis on Descriptions
Study 1 Method
• Three coders
• Three months of coding
• Iterative process
• Cognitive se...
Research Questions
1. How do people comprehend visualization to derive
insights?
a. What kinds of insights are derived?
St...
Four Basic Insights
• Value Read (Va)
“The average ratio over the desirable weight of male is 1.14
approx. ”
Study 1 Resul...
Four Basic Insights
• Extrema Identification (Ex)
“Women exercising three to six times a week have the lowest overweight
r...
Four Basic Insights
• Distribution Characterization (Di)
“Women’s overweight ratio goes down three times, then goes back u...
Four Basic Insights
• Correlation (Co)
“The more men exercise, the lower is their overweight ratio.”
Study 1 Results - Ins...
Four Comparative Insights
Basis of comparison: all four basic insights
• Value Comparison (VC)
• Extrema Comparison (EC)
•...
Research Questions
1. How do people comprehend visualization to derive
insights?
a. What kind of insights are derived?
b. ...
Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution
Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution
Insights Distribution for One Chart
Study 1 Results – Insights Distribution
All Participants
Insights Patterns
Study 1 Results – Insights Distribution
15 patterns
Overall Insight Distribution: Zipf’s Law
Value Comparison
Extrema
Correlation
Study 1 Results – Insights Distribution
Rank...
Research Questions
2. Which composite visualization is preferred for
deriving an insight?
Study 2 Results
Preference Rankings for Basic Insights
Study 2 Results
Preference Rankings for
Comparative Insights
Study 2 Results
Often Most Preferred: Crossed Bar
Study 2 Results
Often Most Preferred: Crossed Bar
for 6 out of 8 Insights
Study 2 Results
Comparing Correlations: Crossed Line Best
Study 2 Results
Which Composition David Wants?
Motivation
• To know the most effective exercise
frequency for female clients
Which Composition David Wants?
Motivation
• To know the most effective exercise
frequency for female clients
Which Composition David Wants?
Motivation
• To compare between males and females
the effect of exercise frequency on
overw...
Which Composition David Wants?
Motivation
• To compare between males and females
the effect of exercise frequency on
overw...
Contributions
• Methodology
• Large scale crowding-sourcing visualization description
• Content analysis via cognitive sem...
Thank you! Questions?
For more information: hyang@us.ibm.com
Yang, H., Li, Y., & Zhou, M. X. (2014). Understand users’ com...
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Understand Users’ Comprehension and Preference for Composing Information Visualization

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Presented the following TOCHI paper in CHI2014 on April 29 in Toronto.

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.

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Understand Users’ Comprehension and Preference for Composing Information Visualization

  1. 1. Understand Users’ Comprehension and Preference for Composing Information Visualization Huahai Yang, Yunyao Li & Michelle Zhou IBM Almaden Research Center
  2. 2. Data Visualization for Decision Making Motivation Image from dynamifitness.com
  3. 3. Composing Visualizations Motivation Crossed Bar Choose Which One?
  4. 4. Visual Insights Motivation Choice of Composition
  5. 5. Visual Insights Motivation
  6. 6. Visual Insights Women exercising three to six times a week have the lowest overweight ratio. Motivation Extrema Identification
  7. 7. Visual Insights Motivation
  8. 8. 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
  9. 9. Research Questions 1. How do people comprehend visualization to derive visual insights? 2. Which composite visualization is preferred for deriving an insight? Goal
  10. 10. Ask People to Describe Visualization Study 1 Method Image from goanimate.com
  11. 11. Crowd-sourcing Descriptions 514 turkers 50 turkers per composition 10 compositions 30 charts in total 1542 acceptable descriptions average 47 words Study 1 Method
  12. 12. 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
  13. 13. Research Questions 1. How do people comprehend visualization to derive insights? a. What kinds of insights are derived? Study 1 Results
  14. 14. 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.
  15. 15. Four Basic Insights • Extrema Identification (Ex) “Women exercising three to six times a week have the lowest overweight ratio” Study 1 Results - Insights
  16. 16. Four Basic Insights • Distribution Characterization (Di) “Women’s overweight ratio goes down three times, then goes back up once.” Study 1 Results - Insights
  17. 17. Four Basic Insights • Correlation (Co) “The more men exercise, the lower is their overweight ratio.” Study 1 Results - Insights
  18. 18. 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
  19. 19. 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
  20. 20. Insights Distribution for One Chart All Participants Study 1 Results – Insights Distribution
  21. 21. Insights Distribution for One Chart All Participants Study 1 Results – Insights Distribution
  22. 22. Insights Distribution for One Chart Study 1 Results – Insights Distribution All Participants
  23. 23. Insights Patterns Study 1 Results – Insights Distribution 15 patterns
  24. 24. Overall Insight Distribution: Zipf’s Law Value Comparison Extrema Correlation Study 1 Results – Insights Distribution Rank insight patterns for all 30 charts by frequency
  25. 25. Research Questions 2. Which composite visualization is preferred for deriving an insight? Study 2 Results
  26. 26. Preference Rankings for Basic Insights Study 2 Results
  27. 27. Preference Rankings for Comparative Insights Study 2 Results
  28. 28. Often Most Preferred: Crossed Bar Study 2 Results
  29. 29. Often Most Preferred: Crossed Bar for 6 out of 8 Insights Study 2 Results
  30. 30. Comparing Correlations: Crossed Line Best Study 2 Results
  31. 31. Which Composition David Wants? Motivation • To know the most effective exercise frequency for female clients
  32. 32. Which Composition David Wants? Motivation • To know the most effective exercise frequency for female clients
  33. 33. Which Composition David Wants? Motivation • To compare between males and females the effect of exercise frequency on overweight in general
  34. 34. Which Composition David Wants? Motivation • To compare between males and females the effect of exercise frequency on overweight
  35. 35. 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
  36. 36. Thank you! Questions? For more information: hyang@us.ibm.com 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.

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