My new book Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks, hit bookstores last week, almost a year to the day from last year’s book, Elevate the Debate. I thought I’d give you an overview of the entire book, which at more than 450 pages, 70,000 words, and 500 images should give you a lot of data visualization content to get into.
Part 1: Principles of Data Visualization
The book consists of three parts. The first part covers the overarching theories and principles for creating effective data visualizations. I talk about rules of perception and how our visual processing network facilitates understanding by discussing gestalt principles and preattentive processing. I also discuss five main rules that I have used time and again for creating better visualizations. Those rules are
- Show the Data
- Reduce the Clutter
- Integrate Graphics and Text
- Small Multiples (or, as I call it in the book, Avoid the Spaghetti Chart)
- Start with Gray.
These rules guide my own visualizations. I think this last guideline can be especially helpful—make everything gray so that you are forced to make purposeful decisions about your data visualizations.
There’s also a section here on using data ethically and responsibly with references to work by Richard Rothstein and Safiya Noble. And, because the final proofs were delivered in the spring, there’s a box on data visualization during the COVID pandemic.
I round out this first part with a discussion of the different types of data visualizations. While the book primarily focuses on static visualizations that are used to help make a point or argument, I would be remiss not to at least discuss interactivity and exploratory visualizations. I also talk about different platforms (e.g., mobile use) and the evolution of how we interact with data. The chapter is largely based on this blog post and this article I wrote for the Journal of Economic Perspectives in 2014.
Part 2: Chart Types
The second part of the book is what my editor and I have called the “meat chapters” of the book. Here, I discuss each of more than eighty different graphs, charts, and diagrams split across six different data categories: comparing categories, time, distribution, geospatial, relationship, and part-to-whole. There is also a chapter on visualizing qualitative data, which is something that seems to have escaped many data visualization books to date. Finally, I’ve included a chapter on good table design, because it’s such an important part of communicating data (and which informed my recent article in the Journal for Benefit Cost Analysis).
The organization of the book mimics my Graphic Continuum projects that serve as reference tools for people to create different types of graph types. In this book, I describe each graph in detail, things to consider, and specific examples. Those examples are either ones that I created myself using real data or are historical examples—I’ve included the John Snow Voronoi Diagram (no, it’s not really a map), Florence Nightingale’s Rose Diagram, and more. You can also get a flavor for these chapters of the book by watching the One Chart at a Time video series, which features more than fifty short videos on individual graph types presented by different experts in the field.
Part 3: Designing and Redesigning Your Visual
The final part of the book is a collection of tool kits and sections that might prove useful as you develop your own data visualization strategy and processes. In Chapter 12, I talk about how to build a data visualization style guide, including considerations around color, font, and layout so your visualizations are consistently branded. I based much of this section on my an ongoing collection of existing style guides. A style guide is helpful for individuals and organizations of all sizes because it can make the creation process easier, faster, and more consistent. I also write about creating high-resolution images and being sure to take an accessible, equitable view in presenting your data.
The final content chapter consists of nine examples of redesigned and reimagined data visualizations. These are drawn from government agencies or individuals who gave me permission to include the redesign in the book. My goal is to demonstrate how to pull the lessons from the book—different graph types, better integration of text and graphs, and better design—into real-world examples. As you might note, the entire book is rooted in actual data and actual examples—I didn’t make up any data or any visualizations, which also helps show how sometimes data and graphs are messy and aren’t always exactly the way you’d like them to go.
The book concludes with a short chapter on how the charts, graphs, and diagrams included in the book don’t constitute the end-all-and-be-all of the field. The visualization space is infinite—people are constantly creating new graph types and playing with different forms and functions. Two appendices at the end list major data visualization tools, libraries, books, and other resources.
I hope you’ll enjoy the book and find it a useful guide for your data visualization work. Whether you are new to the field or an experienced data visualization specialist, I hope you’ll find value in it. You may also find areas in which you disagree with me. But that’s why data visualization is a mix of art and science, and why the field continues to evolve.