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. Please download and view in PowerPoint to see the animation correctly.

1. Understand Users’ Comprehension
and Preference for Composing
Information Visualization
Huahai Yang, Yunyao Li & Michelle Zhou
IBM Almaden Research Center

2. Data Visualization for Decision Making
Motivation
Image from dynamifitness.com

3. Composing Visualizations
Motivation
Crossed Bar
Choose
Which One?

4. Visual Insights
Motivation
Choice of Composition

5. Visual Insights
Motivation

6. Visual Insights
Women exercising three to six times a week have the lowest
overweight ratio.
Motivation
Extrema Identification

7. Visual Insights
Motivation

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. Research Questions
1. How do people comprehend visualization to derive
visual insights?
2. Which composite visualization is preferred for
deriving an insight?
Goal

10. Ask People to Describe Visualization
Study 1 Method
Image from goanimate.com

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. 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. Research Questions
1. How do people comprehend visualization to derive
insights?
a. What kinds of insights are derived?
Study 1 Results

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. Four Basic Insights
• Extrema Identification (Ex)
“Women exercising three to six times a week have the lowest overweight
ratio”
Study 1 Results - Insights

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. Four Basic Insights
• Correlation (Co)
“The more men exercise, the lower is their overweight ratio.”
Study 1 Results - Insights

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. 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. Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution

21. Insights Distribution for One Chart
All Participants
Study 1 Results – Insights Distribution

22. Insights Distribution for One Chart
Study 1 Results – Insights Distribution
All Participants

23. Insights Patterns
Study 1 Results – Insights Distribution
15 patterns

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. Research Questions
2. Which composite visualization is preferred for
deriving an insight?
Study 2 Results

26. Preference Rankings for Basic Insights
Study 2 Results

27. Preference Rankings for
Comparative Insights
Study 2 Results

28. Often Most Preferred: Crossed Bar
Study 2 Results

29. Often Most Preferred: Crossed Bar
for 6 out of 8 Insights
Study 2 Results

30. Comparing Correlations: Crossed Line Best
Study 2 Results

31. Which Composition David Wants?
Motivation
• To know the most effective exercise
frequency for female clients

32. Which Composition David Wants?
Motivation
• To know the most effective exercise
frequency for female clients

33. Which Composition David Wants?
Motivation
• To compare between males and females
the effect of exercise frequency on
overweight in general

34. Which Composition David Wants?
Motivation
• To compare between males and females
the effect of exercise frequency on
overweight

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. 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.