Book Review: Design For Information

Design For Information: An Introduction To The Histories, Theories, And Best Practices Behind Effective Information Visualizations, by Isabel Meirelles

This is a book that in some ways lives up to its name in providing guidance and instruction on effective visualizations for an audience of graphic designers and data scientists in the sciences and humanities. Whether by design or coincidence, the author chooses a lot of the same visualizations found in books such as Tufte’s Beautiful Evidence [1], but the differences are stark when the two books are viewed side-by-side on the same drawings. Both provide gorgeous visuals, and both profess an aim in providing guidance as to best practices. This book, though, reads like a textbook, full of certainties and a focus on technical matters, while Tufte’s work speaks about the political and rhetorical realities behind visualizations, and does so in a wit that is entirely lacking here.

Indeed, it is that difference in intent that seems to mark this book. It is certainly a very competent book in a narrow focus. It provides technical principles by which the designer of infographics can design for the color-blind, or show sufficient contrast or gradation in color scheme to give graphics that are rich in narrative value. It provides extensive case studies in graphics divided by category, starting from hierarchical drawings to tree relationships, networks to show relational structures, timelines and flows to show temporal structures, maps to show spatial structures, drawings that show spatio-temporal structures that combine space and time, and then a few types of graphics that show word structures. Someone who reads this book well will be able to master the technical details how to make good visual graphics, and there is some criticism made as far as those graphics that fail from a technical standpoint by having dots too large or too small to see the patterns in the data.

That said, what is perhaps most notable about this book is what is missing. The author, in examining a wide variety of historical and contemporary visualizations, gorgeously rendered and examined technically, fails to account for the biggest sort of bias in the creation of visualizations, and that is the corrupt bias of the people and institutions who create such images. The author spends a lot of time talking about the bias and limitations of the readers of data, but tends to work from the assumption that those who present visualizations wish to discuss truth or present facts. Yet many of the infographics shown in the book are the result of government institutions and people who have a very clear agenda in their selection of visual data to display, and the lines and boundaries shown are often done with a clear agenda to present a given view of reality. This larger and systemic bias on the part of the creator of graphical evidence is entirely unexplored and unexamined by the author. The best practices discussed are dry and technical, not moral in nature, and this absence of moral criticism of the visualization of data and its uncritical acceptance by others marks a major missed opportunity that a small book of 200 image-intensive pages could easily have added. It was sorely missed by this reader.

[1] https://edgeinducedcohesion.wordpress.com/2015/06/27/book-review-beautiful-evidence/

About nathanalbright

I'm a person with diverse interests who loves to read. If you want to know something about me, just ask.
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