I try to maintain a comprehensive library of books and articles about data visualization, so when I recently read that Springer published a new book entitled Handbook of Data Visualization, filled with chapters from respected experts in the field, I set out to get a copy. Ordinarily, I purchase books for my data visualization library—that is, I pay for them, rather than requesting complimentary desk copies, which are often offered to educators. This book’s $319 price tag, however, discouraged my normal practice. I decided this was an exceptional situation, so I tried to take advantage of my faculty position at the University of California, Berkeley. In response, I was told that I could get a copy to examine, but would have to return it if I decided against using it as a textbook for one of my courses. Once my hands touch a book, they don’t let go, so I tried a different approach. I offered to review the book in my blog, which earned me a copy, and today I am fulfilling my promise.
Last week, I spent many hours in airports and on planes (I was a victim of American Airlines’ inspections, which grounded thousands of flights), which gave me time to peruse all 920 pages of this book and read several chapters in detail. This book is indeed filled with data visualization expertise, but it isn’t clear for whom it was written. Contrary to the title, this is not what I would call a handbook. This is a collection of sophisticated academic articles that cover broad territory, but do not provide an overview and introduction to data visualization that the term handbook suggests. Unlike Readings in Information Visualization: Using Vision to Think by Card, Mackinlay, and Shneiderman (1999), Handbook of Data Visualization lacks informative and digestible introductions to the topics that it addresses. Despite my expertise in quantitative data visualization, I couldn’t follow much of the content, for it assumes an advanced level of mathematics and statistics well beyond my own. The fact that I couldn’t understand much of it certainly doesn’t make this a bad book; it simply suggests that non-statisticians should probably avoid it. Which brings me back to my earlier question: for whom was this book written? The obvious answer—for statisticians with expertise in data visualization—becomes less obvious in light of the price. I don’t know many statisticians who are able, or if able, are willing to plunk down $319 for a collection of articles, no matter how good they are.
I couldn’t help but wonder what could have possibly caused Springer to set the price so high. I assumed that its three editors—Chen, Hardle, and Unwin—would receive royalties from Springer for their work, but I doubted that contributors of individual chapters would be paid for their efforts. My curiosity led me to ask a friend who contributed a chapter along with three of his colleagues if he was compensated. He replied that his compensation was a single copy of the book, which he and his three co-writers were obliged to share. He was frustrated that the book’s high price would keep his work from being purchased, except by university libraries. Besides royalties to the writers, the only other significant expense a publisher usually faces is the cost of printing and binding, for they rarely spend much money to promote books. This book is hardbound, which costs more to produce than a paperback, but I estimate based on my own experience that printing and binding should cost less than $10 a copy, even if printed in relatively small quantities.
For $319, one would expect a book about data visualization to feature beautifully rendered color figures throughout, but it exhibits only one-color printing (black and shades of gray), except for a small insert of multi-colored pages in the middle. I believe that every book about data visualization should be printed in color, yet I’ve seen many examples of an author’s fine work that were undermined by a publisher’s decision to save money on production costs by going with cheap paper and a one-color printing process.
Another aspect of this book’s design that I found annoying and completely out of character with the concerns of data visualization was the placement of figures. In the chapter “Good Graphics?” by Antony Unwin, he wisely recommends:
Keeping graphics and text on the same page or on facing pages is valuable for practical reasons. It is inconvenient to have to turn pages back and forth because graphics and the text relating to them are on different pages.
Amen to that. Unfortunately, even the very page on which this statement appears and almost every other page in the chapter ignores this advice. I’m confident that Unwin cannot be faulted for this flaw in design, and that fault lies with the publisher, which took the easy, inexpensive path, despite the inconvenience to readers. I have had to fight hard to control the design of my books and articles, sometimes to the annoyance of publishers, in an effort to avoid problems like this. This shouldn’t be necessary. Publishers should be experts in these matters and respect their customers enough to do what’s required to make books work, even when it takes more time and costs a bit more.
I cannot recommend this book to most of my readers, who usually favor advice that is accessible to non-statisticians and can be more broadly applied. I am confident, however, that this book would be useful to statisticians who already know quite a lot about data visualization, if they could only afford to buy it. The failures of this book rarely stem from its authors, but instead from Springer’s near-sighted and dysfunctional publishing model.