Where does data visualization begin and data art begin? Jer Thorp, resident Data Artist at the New York Times (a position he created for himself), believes the distinction is unproductive. Combining his background in genetics with his digital art practice, his award-winning, internationally exhibited works are not easily categorized. In this interview we discussed the innovation of Cascade, a data visualization tool he helped develop that reveals sharing systems within social networks as well as his work developing the algorithm for the 9/11 memorial, “All The Names.” Thorp also shares his take on what makes data art unsuccessful, how he would like to see the field of data science evolve and exercises his creative with a five word poem on his favorite food.
As Data Artist in Residence at The New York Times you were instrumental in developing the Cascade visualization tool. Can you share how Cascade works?
Cascade is a collaborative project. While it was initiated by Mark Hansen and I, and we continue to work on the project, it is and has been supported by a huge group of super talented folks at the R&D department. So, while I can take some of the credit for the design and the underlying ideas, it’s definitely a group effort.
Cascade works by showing us something that we’ve never seen before – the underlying “architecture” of sharing systems within social networks. Not only can we see that a piece of content has been shared from person A to person B, we can see, in really granular detail, all of the activity that happened in between.
You’ve said that Cascade can be applied to any virtual sharing system. Can you give us a use-case example of how a company (other than a news agency) might effectively harness Cascade?
We have a number of clients who are using Cascade right now. Because the underlying mechanisms aren’t New York Times-specific, we can set up the system to show how any specific set of content is being shared, and we can visualize the content in near real-time. It can give any company or organization a view into activity on Twitter in a much, much more detailed level than your typical social media “dashboard.” Not only can you see the who and when of the networks, but you can get insight into the specific character of sharing that is tied to your users and to your organization.
I was really moved by your contribution to the 9/11 Memorial in Manhattan. Can you explain the connections you took into account when developing the name arrangement algorithm and software tool for the project?
These connections are called “meaningful adjacencies,” and they were provided by next-of-kin to the memorial foundation. Collecting these requests was a really in-depth process that the foundation undertook, and it took a number of years. There ended up being about 1,400 adjacency requests, all of which where satisfied in the final layout for the memorial. It’s really worth noting that what we (myself & Local Projects) did was to produce an algorithm and a software tool that could assist the architects in their production of the final layout. It’s very much an example of computer-assisted design, rather than one of computer-generated design.
What makes a work of data art successful or not?
The biggest failure of data art, in my opinion, is in neglecting to address the individual character of a data set. We have all kinds of quite beautiful methods to visualize datasets of a specific sort – say network data sets – but they tend to be general use algorithms and tecnhiques. As a result, visualizations of varied data sets tend to look the same. While this can be a good thing, and it can communicate similarity between data sets, I think it’s very often lazy.
Almost any data set you find has some specific character that could and should be addressed in a vizualization – and certainly in a data art project.
There are many organizations and initiatives aiming to link data science and social good. What role do you think data artists can play in effecting positive social change?
First, I think artists can push limits that designers or scientists might have trouble justifying. Since our primary concern is often not clarity or communication or any of the other typical data vizualization goals, we’re free to try out methods that might be discarded in more conservative projects. In this way artists can be a kind of R&D lab for non-standard technique.
More importantly, I think artists can add a critical voice to the discussion around data and society. I remember being at a conference a couple of years ago, where someone proudly stated that data was the “new oil.” I was shocked that everyone in the audience seemed to think that this was a good thing. I think we’ll see data artists making more projects that provoke and disrupt public thinking around things like data ownership, data-driven surveillance, and other issues connected to data.
Where does data art begins and data visualization end? Do you think the distinction between the two is useful?
We always end this segment with: How would you like to see the field of data science evolve over the next few years?
Any quickly evolving field will have stretches of wild innovation, followed by somewhat more boring, but often longer periods of refinement. I think we’re at the tail end of a refinement stage right now, with lots of people trying out near-exact copies of other peoples’ ideas in an effort to strike it rich on idea terrain that has already proven to be productive. Once these overcrowded veins start to run dry, people will be forced to explore and innovate again – I’m looking forward to that.
I also hope and dream that data science will be linked in dialogue with the data humanities, so that the general discussion is a more balanced one than it is right now.
DATA ART SYNTHESIZER
What’s the funniest piece of data you’ve coming across in reviewing streams of information with Cascade?
My favorite thing that I’ve found using the tool is something I call ‘The Rabbi Cascade’. It’s a back-and forth discussion about an article published in the times last year about religious workers not taking very much time off of work, and the conversants are all Rabbis. It’s great to be able to see these beautiful, small conversational structures along side the giant cascades that accompany stories about the iPad, etc.
What’s the best advice you’ve been giving on finding creative inspiration?
Get off your computer. Get out of the house. Read fiction.
Can you share a 5-word poem about your favorite food?
If all else fails: cheese.