Better Data Visualizations

Not only is the book well designed from fonts to white space, it's also well written and enjoyable to read.

Auteur
Jonathan Schwabish is an economist and writer, teacher, and creator of policy-relevant data visualizations. He helps nonprofits, research institutions, and governments at all levels improve how they communicate their work and findings to their colleagues, partners, clients, and constituents. He is the author of Better Presentations: A Guide for Scholars, Researchers, and Wonks (Columbia, 2016).

Texte du rabat

This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts.

Résumé
Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually.

This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.

Contenu
Introduction
Part I: Principles of Data Visualization

1. Visual Processing and Perceptual Rankings
2. Five Guidelines for Better Data Visualizations
3. Form and Function
   Part II: Chart Types
4. Comparing Categories
5. Time
6. Distribution
7. Geospatial
8. Relationship
9. Part-to-Whole
10. Qualitative
11. Tables
    Part III: Designing and Redesigning Your Visual
12. Developing a Data Visualization Style Guide
13. Redesigns
    Conclusion
    Appendix 1. Data Visualization Tools
    Appendix 2. Further Reading and Resources
    Acknowledgments
    References
    Index