As I re-read Lima’s Visual Complexity this week, I couldn’t help but think about Tufte and the relationship between data and aesthetics. And thought about this site (and the book it supports): Information is Beautiful. Then, I open up my Sunday New York Times and find this substantial and smart introduction to Big Data. In the Gray Lady.
I remember trying to decide between Lima and McCandless’ texts, and I erred on the side of Big Data and rhetoric. I don’t think that’s much of a stretch for us. McCandless is more Tufte-like in his insistence on the aesthetic, whereas I read Lima as much more rhetorical. And Lima’s visualizations are, I think, more powerful and less pretty precisely because they wrestle with issues of information articulation. Like Horn* before him (see Horn, Visual Language, 1999), there is power especially in seeing visualizations as a language—see Lima’s chapter 5 titled “Syntax of a New Language”. Lyotard’s différend allows us to be a bit more precise and assert that visualizations allow us a means of transcending the current limits of language. See Liu’s Auto- différend page if you aren’t familiar. Or perhaps visualizations provide a route, a heuristic, from inability to articulate through to our drafted and preliminary articulations, from ineffability and a sense there is “something” there, to language, to explanation, and on to persuasion. So for me, for us, following Lima, we are grasping at a “Rhetoric of a New Language” as Lima’s chapter articulates its syntax.
So in asking you to work with datasets, I am not asking you to come up with fully formed projects or ideas for Tuesday, but to search through these links I’ve provided and set out on your own to explore data with these new visualization tools&em;rhetorical, technical, and aesthetic—and to think about problems or questions for which you think there may be an answer that does not yet have an idiom (in Lyotard’s terms)—that we do not yet have a language to express. How can (how might) we use these visualizations tools to offer a “new” or emergent means of expression and persuasion not previously open to us? So Data Visualization is in this way a means of rhetorical invention.
With any luck, this line of argumentation leaves anyone who was uneasy about exploring Big Data, datasets, and visualizations a little less uneasy and a bit more confident that my obsession with visuals remains visual rhetoric. The challenge persists: what arguments are (our and others’) visuals making possible that are not otherwise possible?
Periodic Table of Visualization Techniques (And why use a scientific metaphor?)
I’ll kick things off with some geographic/mapping examples, unless you’re all so excited you push my stuff to the side. (I can hope.) IBM’s eBook (linked below) offers a solid introduction to Big Data. My recommendation is to spend lots of time with Lima, especially Chapter 5, and flip through these links and find ones that speak to you, your interests, and your current obsessions towards understanding various assertions of dataviz and visual rhetoric as invention. Once you’re okay with that, I’m curious about the kinds of arguments you want to make, and the data you need to do it. That’s what I mean about finding datasets. Pracjus et al remind us of the importance of whitespace – Helvetica. Scott comes from advertising and argues for a theory of visual rhetoric, which connects it to my interest in Lima as well as to Handa. And if you run Ray and Charles Eames in the background while reading Lima, all the better: Charles & Ray Eames: The Architect and the Painter . For those who may have missed it, Kristen Moore offered this link as a powerful dataset for our consideration: The Adjunct Project collecting adjunct-related data from the instructors themselves: crowdsourcing and user-centered…and prosumer. Let’s see how we can rise to her challenge.
Define Big Data at Wikipedia.
McKinsey Report on Big Data
O’Reilly Defines BD
ZDNet is next,
then Information Week
Us Census Data is Big Data,
that IBM and
EMC want to play with.
IBM has dedicated itself to Big Data,
releasing an IBM eBook
and they market with Big Data.
IBM has link to visualization tools
as well as a DataViz community portal called ManyEyes.
And business journalists are paying attention.
TED has committed itself to BigData:
TEDxUofM – Jameson Toole – Big Data for Tomorrow
big DATA, BIG stories (TEDx event at University of Vermont) see esp. TEDxUVM 2011 – Peter Dodds – Big Data and the Science of Complexity
Hans Rosling is a DataViz node of his own. We watched parts of the 2007 piece on poverty. He supports Gapminder and while he kept a blog, the last post was 2007. More recent posts are gapminder news.
Worldmapper and Worldometer are two favorites.