What is Asynchronous Collaborative Information Visualization? That’s a good question.
A group out of Berkeley’s visualization lab (Heer et al. 2007; website here) argue that despite the great advances in made in computer-aided data visualization in the past half century, designers and academics in general have ignored the potential for social visual analysis. They propose a sort of Wikipedia-style moderated group annotation of data visualization in order to provide a method of navigating that data more easily. Think of it as crowdsourcing visual analysis.
Their prototype application for social visual data analysis used census data over about 150 years. It provided a way for collaborative bookmarking by graphical annotations as well as comments.
Luckily, they have actually implemented their design on a larger, public scale at CommentSpace. Using their original design, they have comments like a transparent layer in photoshop over the visualization, This makes it easy for people to engage in a discussion over the validity of annotations through a Wikipedia-esque moderation system.
Above is their original design. Below is what it looks like now.
What I particularly liked was that they not only implemented and tested the system, but spent a considerable time collecting data on how it was used and ways in which it could be improved. As the authors note, “[o]verall, we believe these results show the value of focusing on the social aspects of visual analysis. Our user studies indicate that combining conversation and visual data analysis can help people explore a data set both broadly and deeply.” This conclusion definitely shows in the good work that CommentSpace is producing:
I hope to see more of this sort of work in the future because it provides a convenient and useful bridge between academic projects and an increasingly data-aware public. This has been shown as a viable method by projects like Galaxy Zoo, OpenStreetMap, and, of course, Wikipedia. I think that social scientists working on complex projects should look at not just crowdsourcing projects like Amazon’s Mechanical Turk, but think of the implications for data visualization provided by a society both constantly connected to the internet and also used to seeing data visualizations in their everyday lives.
See the original research paper here.
Gergle D., Kraut R.E., Fussell S.R. 2004. Language efficiency and visual technology: Minimizing collaborative effort with visual information. Journal of Language & Social Psychology, 23(4): 491–517.
Heer, Jeffrey, Fernanda B. Viégas, and Martin Wattenberg. 2007. Voyagers and Voyeurs: Supporting Asynchronous Collaborative Information Visualization. In CHI 2007, April 28–May 3, 2007, San Jose, California, USA.
Surowiecki J. 2004. The Wisdom of Crowds. Random House.
Wright W., Schroh D., Proulx P., Skaburskis A., Cort B. 2006. The sandbox for analysis: concepts and evaluation. In Proc. ACM CHI 2006.
Wu F., Huberman B.A., Adamic L.A., Tyler J.R. 2000. Information flow in social groups. Physica A: Statistical and Theoretical Physics, 337(1–2): 327.
- Visualize Big Data with Google Public Data Explorer (kinlane.com)
- What is Data Visualization? [Infographic] (readwriteweb.com)
- Data Visualization References (dashboardspy.wordpress.com)