When we last left off, I was leading the class on a charting expedition. My intention was to do this on paper, under the assumption that, if we used a medium with which everyone is familiar, we could avoid getting hung up on the implementation details (namely, programming syntax). The class decided that this wasn’t the best use of their time, though—and I admit that charting with pens and using calculators to interpolate values on scales would have been tedious, but instructive nonetheless—so last week we took a look at Many Eyes, a project by the fine folks at the IBM Visual Communication Lab.
Many Eyes’ goal, according to its creators, is “to ‘democratize’ visualization and to enable a new social kind of data analysis,” the idea being that both the use of social visualization tools and the public release of the underlying data can lead to new insights. To test this theory, I played with a few of the data sets already uploaded to the site and sought out a few of my own to contribute. In just a few hours, I had:
- attempted to correlate vehicle miles traveled and rates of traffic fatality in US states;
- created a small multiples map of hate crimes by type and state;
- visualized the high proportion of fuel used by light-duty passenger vehicles,
- tweaked the color contrast on a treemap of CO2 emissions by transportation mode to better show proportional differences;
- shown the rise of light and heavy trucks in highway transportation energy consumption from 1970 to 2003;
- modified a matrix chart to more clearly communicate gender bias by political party represented on The BBC’s Question Time;
- discovered a data set that’s particularly interesting when viewed in Many Eyes’ matrix chart;
- clarified a map illustrating the shifting of Ireland’s population by using circles rather than colored areas;
- and created a line chart comparing annual bicycle and car production worldwide.
The day’s experiment was successful, so last week I had the class use Many Eyes as a tool for visualizing their own data sets. There were some issues, particularly:
- While it was great that everyone already had their tabular data (the first week’s homework was to gather data sets in comma-separated value format), Many Eyes requires you to upload data by pasting it into a text field as tab-separated values. This requires opening the CSV file in a spreadsheet application (such as Microsoft Excel, Apple Numbers, or Google Docs) and copying cells by hand.
- “Null” values (which appeared as “N/A” or other non-numeric strings in some people’s data) sometimes led Many Eyes to interpret columnar fields as qualitative text rather than quantitative numeric values. This resulted in some of the automated visualizations failing to assign fields to particular axes, and often required students to re-upload their data sets.
The biggest issue, however, appeared to be that students quickly ran into the limitations of Many Eyes visualizations. They wanted to change the colors, filter the data interactively, or cross-refernce multiple data sets. We learned in this process that, while Many Eyes is a great tool for creating an initial picture of a data set, it doesn’t provide “all” of the tools one would need to really explore their data. There are many other sites and paid products which claim to do just that, but it should be obvious to anyone who’s used them that no generalized system (yet, anyway) is capable of adjusting itself to suit the needs of every possible data set.
As a remedy for this, I suggested that the students use Many Eyes (or another service) to do the heavy lifting of deriving scales and interpolating values, then use its visual output as input for a more manual, bespoke visualization process. Once you’ve fed your data set into a bar or bubble chart, you can use the calculated relative sizes of each mark as the basis for a new representation. E.g.:
- Take a screenshot of Many Eyes and adjust the colors in Photoshop.
- Trace over the shapes in Illustrator and create your own arrangement.
- Mix and match your data sets by incorporating elements from two or more visualizations into your own canvas.
- Measure the sizes of elements in pixels and recreate them in HTML and CSS.
Don’t assume that you can’t make something interesting without programming skills. Wield the tools that you already know how to use and do whatever it takes to bang the data—be they the original numbers or the normalized output sizes—into a form that you can work with.
And when you bump up against those limits, then you can consider taking up programming and using visualization libraries like protovis (with JavaScript) or Flare (with Flash). And if you get into visualizing large amounts of data you’ll quickly discover that neither Flash nor SVG are capable of moving more than a couple of thousands of points around on the screen at once, at which point you’ll have to either resort to working with aggregations of data and creating interactive interfaces to filter those points into manageable subsets; you’ll decide to employ non-interactive tools to generate static representations of data; or you’ll discover hardware acceleration in more “serious”—and, unfortunately, less “web-friendly”—programming languages like Java and C.
My point isn’t that this stuff is particularly “hard”, but rather that it’s only worth really figuring out if it’s applicable to your goals and you have the time to learn it. Just as visualization can be seen as a process for educating yourself about data, visualization is also a useful programming exercise. Many of the Processing examples could be easily adapted to incorporate real data rather than generating a series of random shapes. Conversely, you may wish to use visualization libraries to generate artistic renditions of random data. I spend a lot of time trying to automate or prematurely optimize processes that would probably have taken less time to do manually. Whatever process helps you do what you need to do quickly and effectively, by all means use it.
Coming back to Many Eyes, though, let’s consider the advantages of its visualizations existing within an open structure on the web. The name itself is meaningful: The more eyes on this stuff the better. That’s a belief that we espouse at Stamen too, because the whole point of our work is to make data more relevant, accessible, and desirable. And how can you do that without showing it to lots of people? Above all else, your choices of medium and technology should be driven by your ability to make something with them that you can share. A series of static graphics on the web is more interesting and useful than the half-baked interactive interface that you started but never finished. And incremental improvements made in the public eye both help other people learn about your process and invite valuable input along the way. There’s nothing much worse than spending a lot of time on something in private, only to have it picked apart once you finally release it to the world.
Use the tools with which you’re comfortable, and experiment with new stuff if you’ve got the time. In the words of my esteemed peer Matt Jones, though, and above all else, just:
