Making Sense Visual

Preface
Who Is This Book For?
Who Are We?
Overview of Chapters
Acknowledgments
O’Reilly Safari
How to Contact Us

1. Getting to an Effective Visualization
Getting to Insight
Hotmap: Making Decisions with Data
Where Visualization Is Useful
Further Reading

2. From Questions to Tasks
Example: Identifying Good Movie Directors
Making a Question Concrete
A Concrete Movie Question
Breaking Down a Task
When Tasks Lead to New Questions
Returning to the Example: Exploring Different Definitions
How Specific Does the Process Get?
Making Use of Results
Conclusion: A Well-Operationalized Task
Further Reading

3. Data Counseling, Exploration, and Prototyping
Technique 1: Data Counseling
Identifying Stakeholders
Conducting Interviews
Conducting Contextual Interviews
Technique 2: Exploring the Data
Technique 3: Rapid Prototyping for Design
The Range of Prototypes
Eliciting Feedback
And, Repeat
Conclusion

4. Components of a Visualization
Dimensions and Measures
Example: International Towing & Ice Cream
Dimensions
Types of Data
Transforming Between Dimension Types
Dimensionality Reduction and Clustering
Examining Actions

5. Single Views
Overall Perceptual Concerns
Question: How Is a Measure Distributed?
Histogram (Categorical)
Histogram (Quantitative)
Smoothed Histogram
Box Plot
Density Plot for Two Dimensions
Question: How Do Groups Differ from Each Other?
Bar Chart
Pie Chart
Heatmap
Question: Do Individual Items Fall into Groups? Is There a Relationship Between Attributes of Items?
Scatterplot
Question: How Does an Attribute Vary Continuously?
Line and Area Charts
Question: How Are Objects Related to Each Other in a Network or Hierarchy?
Node-Link View
Adjacency Matrix
Tree View
Treemap and Sunburst
Question: Where Are Objects Located?
Geographical Map
Question: What Is in This Text?
Word Cloud
Conclusion
Further Reading
Relevant Articles
Datasets

6. Multiple and Coordinated Views
Small Multiples
Scatterplot Matrices
Overview+Detail
Multiform Views and Dashboards
Overlays
Axis Alignment and Scale Consistency
Interacting with Multiple Linked Views
MLVs and the Operationalization Process
Conclusion
Further Reading
Datasets

7. Case Study 1: Visualizing Telemetry to Improve Software
Introduction
Project Background
The Data
Determining How to Compare Builds
Comparing Distributions to Understand “Better”
Multiple Scenarios
Sketching Dashboards
Turning Back to the Data
Final UI for High-Level Goals
Additional Visualizations
Conclusion
Acknowledgments
Further Reading

8. Case Study 2: Visualizing Biological Data
Background
Setting the Context
Zooming in a Level
Improving the Existing Approach
Similarity, Not Outliers
A Final Version
Conclusion
Further Reading

9. Conclusions

Index