Understand Users’ Comprehension and Preference for ComposingInformation Visualization

Huahai Yang, Yunyao Li & Michelle ZhouIBM Almaden Research Center

Data Visualization for Decision Making

Motivation

Image from dynamifitness.com

Composing Visualizations

Motivation

Crossed Bar

ChooseWhich 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 Identification

Visual Insights

Motivation

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

Research Questions

1. How do people comprehend visualization to derive visual insights?

2. Which composite visualization is preferred for deriving an insight?

Goal

Ask People to Describe Visualization

Study 1 Method

Image from goanimate.com

Crowd-sourcing Descriptions

514 turkers50 turkers per composition

10 compositions30 charts in total

1542 acceptable descriptionsaverage 47 words

Study 1 Method

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

Research Questions

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

Study 1 Results

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.

Four Basic Insights• Extrema Identification (Ex)

“Women exercising three to six times a week have the lowest overweight ratio”

Study 1 Results - Insights

Four Basic Insights• Distribution Characterization (Di)

“Women’s overweight ratio goes down three times, then goes back up once.”

Study 1 Results - Insights

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

Study 1 Results - Insights

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

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

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 LawValue Comparison

Extrema

Correlation

Study 1 Results – Insights Distribution

Rank insight patterns for all 30 charts by frequency

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 Barfor 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 overweight in general

Which Composition David Wants?

Motivation

• To compare between males and females the effect of exercise frequency on overweight

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

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.