The explosion of data raises significant challenges to processing, analyzing, and giving meaning to a sustainable flow of information. This book is a great reference in the field of data visualization. The motivation given by the authors to write the book is to cover “theory, details, and tools necessary ... to build visualizations and systems involving the visualization of data.”

The book covers the topic of data visualization from a wide range of domains and applications. The book begins by giving a solid theoretical foundation, covering many topics, including human perception, physiology, and cognition. Next, a long list of visualization techniques is given, each applying the theory seen previously to a particular kind of data (for example, spatial, geospatial, time-oriented, multivariate, trees, graphs, networks, text, and document). A detailed review of the most used visualization techniques is made from a critical and practical point of view.

Subsequently, the book goes into the notion of interaction, and gives tips about making an effective visualization. Some of these tips cover the use of tick marks, the adequate use of colors, and proper data range selection. Mistakes frequently encountered in data visualizations are also discussed.

Chapter 14, “Comparing and Evaluating Visualization Techniques,” gives a generic recipe to rigorously evaluate a visualization technique. It reviews the classical visualization benchmarking procedure. The chapter is a gentle, short, and easy-to-read introduction to the field of experimentation and measurement. It also provides an example. Academics who are not experts in the technique to evaluate visualizations will enjoy this chapter. Note that the chapter does not cover the necessary statistical tools to properly carry out a solid evaluation. However, it is an excellent starting point.

Most of the visualization techniques are presented from both theoretical and practical points of view. Theory is backed up with academic paper references, which give confidence to the reader. The book has more than 500 pages, including 35 pages of bibliographical references. The book also covers many algorithms used in data visualization. While some of these algorithms are well known (for example, building a scatterplot, page 40), some others are not (for example, cycle time arrangement, page 270). Although the book emphasizes the theoretical aspects of the field, the authors are perfectly aware of the available libraries.

I very much enjoyed reading this book. It is very well balanced between theory and applicability. While I like that the visualization techniques are presented from an academic point of view, it is a pity that many of the given references are a bit old. Most of the provided references date before 2000. Only a couple of references refer to academic artifacts published after 2007. However, I do not think this aspect seriously restricts the scope of the book.

The book is not about learning a new library for data visualization. Many excellent books cover libraries for various programming languages. Note that a designer of a new data visualization library will appreciate the algorithms provided in the book, which are presented in pseudocode.

The book is suitable for researchers, academics, and lecturers. It explores new research areas in the field of data visualization. The list of exciting research directions given in chapter 16 will surely be appealing to young researchers.

Those who wish to expand their research field with a component of data visualization will surely find the book useful. It is easy to read, even for people without a background in data visualization. Chapter 13, “Designing Effective Visualizations,” covers some common errors in the field. People without a background in the field will find this chapter valuable, as it highlights the difficulties in mastering data visualization techniques.

Teachers will appreciate the list of exercises and project topics given at the end of most of the chapters. These project topics are often about a concrete application of the theoretical results seen in the chapter. Carrying out these projects typically involves implementing and experimenting. No programming language is preferred, although the code given as an appendix uses Processing. The book is therefore suitable for classroom use.