Musician Frank Zappa famously coined the phrase “talking about music is like fishing about architecture” – a statement that surely applies just as well to visual design. Luckily, datavis is of a more analytical, logic-and-language bent, so we can start to dig beneath the surface of what makes a visualisation effective, compelling, even original or beautiful. In compiling a reading list for the MRes ASAV course, I’ve had the chance to sample a number of books which tackle the subject from different perspectives – whether general priniciples of datavis design, like Edward Tufte, specifics on implementing datavis in Processing (my particular weapon of choice) by Ben Fry, or how datavis and the more general field of data journalism can be used to create not just visuals, but stories, out of data (like The Data Journalism Handbook). I’ve compiled a few thoughts on these, both for current students about to start on their visualisation module this term, and people just generally interested in dipping their toe into datavis.
You can get all of these books from your favourite online retailer, but I try where I can to order them from an independent bookshop and support businesses which are local and pay tax; mine is Bookseller Crow in Crystal Palace. Some of these books were given to me as inspection copies, some I bought, and some I borrowed.
Starting with the toe-dippiest…
Information is Beautiful by David McCandless
David McCandless is a bit like the J K Rowling of data vis- he’s tremendously popular and is the person who Newsnight go to when they’ve got an item on datavis. He also runs competitions to recognise good datavis, and regularly posts material on his website http://www.informationisbeautiful.net/. His visualisation are generally more at what I would call the infographic end of the spectrum – simple pieces of information illustrated with vibrant and often novel pieces of graphic design, and Information is Beautiful compiles a number of these. His forays into mapping are limited, and it’s relatively unusual for him to represent complex datasets, but this book has example after example of how approaching a problem laterally can yield imaginative and accessible new ways of visualising data.
The Visual Display of Quantitative Information by Edward R Tufte
Tufte is one of the forerunners of design for data visualisation, and The Visual Display of Quantitative Information is his best-known work. He highlights some great historical visualisations, the most noteworthy of which is probably Menard’s map of Napoleon’s march on Moscow. Across the amorphous and probably imaginary line that separates mapping for navigation and mapping for conveying data, Menard’s map exists very firmly in the latter camp. Tufte deconstructs this and other visualisations from a design perspective, laying out his ideas for good datavis. He’s also pretty good at skewering crappy visualisation, but more importantly, highlighting the principles he feels have been violated and making the visualisation bad.
This book is quite old, and doesn’t deal explicitly with a lot of computer vis techniques that we tend to use. And if I’m honest, I find plenty to disagree with in Tufte – for example, his reimagining of the graph’s axes doesn’t seem to have caught on and seems needlessly complex. But it’s all food for thought in a field that’s capable of being very conservative as much as it’s known for its whizzbang superficiality.
His later Visual Explanations has more reference to geographical visualisations and unpicks some new examples. It’s a very rich book, and deals with everything from Feynmann’s O-ring explanation for the Challenger Disaster to creating charts for recognising fish underwater with the associated magnification error. Tufte’s written a series of books on this subject, and none of them are cheap, so be judicious unless you have money to burn.
Visualise This by Nathan Yau
Nathan Yau’s website, http://flowingdata.com/, is like David McCandless': an active resource of new visualisations. Much closer to a “how-to” guide, Yau’s 2011 book acts as a good primer for different data tools available (such as ManyEyes, Google Spreadsheets, Python and Processing) before delving deeper into the magic of R. People who know me will know I’m not a massive fan of R (for various reasons becoming increasingly outdated and irrelevant), but there’s no doubt that’s it’s a powerful tool for creating beautiful static visualisations and maps, as James Cheshire’s and Ollie O’Brien’s various blogs demonstrate (e.g. http://spatialanalysis.co.uk/).
Visualising Data by Ben Fry
Excellent introduction to visualising data in Processing, covering reading in data through data workflow and some nice, innovative visualisation techniques. Like Nathan Yau’s book, it acts as a quite detailed how-to guide, but using Processing rather than R. The main criticism would be that Processing 2.0 has shifted focus and this 2008 book needs updates to reflect this.
Beautiful Visualisation edited by Julie Steele and Noah Illinsky
Fascinating portfolio of data visualisation work. Less a technical how-to than a series of examples of different approaches and datasets, with commentary from the designers outlining their strategies, challenges and outputs from the data. From a teaching and learning perspective, the lack of technical detail means more examples could be fitted into the book, but it’s harder to recreate them. However, as an overview of vis. techniques and how they fit into a broader research methodology, this is highly illuminating. It’s the sort of book where, if you kind of know what you’re doing already, you can probably replicate the writers’ outputs, and you also get a sense of their workflow, thinking process, and the development of their ideas.
The Data Journalism Handbook edited by Jonathan Gray, Liliana Bounegru and Lucy Chambers
Seeing the journalistic angle on using data to uncover “news”, and to find and tell a story, is fascinating from the perspective of scientists more used to using datavis and analysis as a research tool. The book provides a series of case studies, and illustrates how data science, analysis and communication all have impacts beyond academics and policymakers. The book is structured loosely does not have a strong “centralised” message – but provides a series of case studies which uncover both datavis techniques and journalistic approaches to the subject matter. It’s not a how-to, like some of these others, put provides a broader perspective on using visualisation to communicate – and literally, create stories.