Fernanda Viégas and Martin Wattenberg opened Columbia University’s Talk Series: Artists using Data in late March.
Messiness, Clutter and Revelation
As pioneers in data visualisation, analytics and data art, Viégas and Wattenberg have paved new pathways for users to understand and explore data.
As technologists we ask: Can visualization help people think collectively and move us beyond numbers into the realm of words and images and never-before-told stories? As artists we seek the joy of revelation.
While most of their current work is geared towards AI as part of Google Brain, Viégas and Wattenberg began by walking their audience through one of their earliest projects using Google called ‘Web Seer’ that allows users to compare Google Suggest completions.
This is also a really interesting window into public psyche. Because, this is what people are coming to google for. It visualizes exactly the same data but by adding a couple of dimensions. So, we get a sense of which ones are more popular. We can see the completions that are different for each one of the cases. But, we can also see what they have in common.
You see a richness in these kind of data sets. It also starts to show how vulnerable some people are when they come to google for answers.Viégas
Tying into the idea of data created by the masses, the artists unfolded processes that went into their notable ‘History Flow Tool’ which, visualized the behind-the-scenes dynamics of publicly edited Wikipedia pages in 2004, when the online encyclopedia was a relatively new and mysterious place on the web. Viégas prospected commonly overlooked occurrences on Wikipedia like vandalism, watch-listing, edit wars and disambiguation that go unnoticed due to the sheer Web 2.0 speed at which the giant encyclopedia gets edited.
Wikipedia fosters a knowledge community built upon trust. An interesting feature that the artists discovered in their process were Watch lists. Something that we commonly seem to be unaware of. Watch lists on article topics help active Wikipedia contributors take notice of vandalism. Every time an article of their interest is edited, contributors receive notifications. A notification from a new IP address or a user they haven’t seen before would be cause for alarm, wherein the community would check to make sure that it’s not a vandal. A real-time visualisation within the History Flow tool would show no discontinuities.
An article on Cat tends to be longer than a lot of other articles such as ‘Design’, as more people edit ‘Cat’. The visualisation of the history of the ‘Abortion’ page would have distinct discontinuities, reflective of the polarized opinions around that topic. The artists also colored the text based on its age instead of the authors to determine parts of an article that could be posited as qualitatively more stable.
We were really interested in how people were negotiating in this sphere, how were they deciding what fits and what doesn’t fit. Questions like these, were out first exploration into how these collaboration dynamics work.
History Flow became a part of MoMA’s collection in 2003.
An attempt to extract structure from longer pieces of music, Wattenberg’s ‘The Shape of Song’ from 2011 looked at notes of repetitions from classical music and folk songs to Jazz and Led Zeppelin’s Stairway to Heaven.
Jazz is actually quite interesting. You get something that’s relatively simple at the beginning, which then explodes into complexity towards the end. This, to me, is actually capturing something visually that you can otherwise only hear.
In 2009, a Boston-based print magazine ‘The Positive Things’ commissioned Viégas and Wattenberg’s piece Flickr Flow with a brief to visualize Boston. The artists turned to the photo-sharing website Flickr for a year’s worth of creative-commons images of Boston Common, a central public park in downtown Boston, Massachusetts – with the intent to capture Boston’s visual dimension through its seasonality. The images were organized by months and parsed through for different kinds of reds, greens and so on, counting pixels for each image, which became raw data for drawing the ribbons.
This is a very “dirty” data set if you will, because these were not all going to be beautiful pictures. There would be pictures of benches, for example and other things that have nothing to do with flowers or foliage. But, we decided to work with the messiness and see if we can get somewhere.
Even with all the messiness in the data, there was still some signal that there is change. In fact, this looks very fluid. But if we break it down into the height of each season, you can see that the color distribution is dramatically different between winter, fall, summer and spring.
Art of Reproduction
Playing with the idea of visual half-truths, Watternberg’s Art of Reproduction was a collection of fragmented collages of famous artworks representing dramatic differences across the reproduced images.
Not all of these images are really the correct image at all. For one thing, they are different sizes. But, more deeply, the colors are different. And if you keep looking, you realize just how broad the variation is. We all know that reproductions are not the same as the original on some level. But, seeing the breadth of these different things is impressive.
The Wind Map
In 2012, the artists pioneered a distinctive way of visualizing the wind – something that has virtually no visual form. Working with government data of the United States, they initially began by conceiving of wind as ‘particles that we see as a pattern’. Eventually, they settled on the idea of particles that would leave behind little trails, which allowed for communicating subtler forms information such as change in direction.
When Hurricane Isaac made landfall in August 2012, the artists began receiving emails from people affected by the natural disaster.
It was a very strong experience to have something on the web that is real-time that people were looking at for very different reasons and that we had people in these very specific situations talking to you about the data that you’re visualizing.
When working with data such as this, designers tend to aggregate in turn obfuscating a lot of the detail. Viégas and Wattenberg, instead emphasize the texture and richness of the data relying on the viewer’s visual system and intuitive understanding of the difference between ‘broad patterns of wind versus delicate things’. This particular map came to be used professionally by farmers, and scientists who observed bird migrations and butterfly migrations, and teachers and school children to learn forecasting. Cameron Beccario, a software engineer adapted this tool to scale it to the entire earth at different levels going up to the stratosphere, creating greater accessibility to the data for purposes such as aerial navigation.
There were a lot of decisions we made in this visualisation – design decisions. We’re not using color, for instance. We’re not showing pressure or temperature. We’re not drawing (geopolitical) boundaries on the map. We wanted this to be as unobtrusive as possible. We wanted you to see the shape because that’s what we wanted to see and then, people started using it in really unexpected ways.
It speaks to the power of just making complex data easily accessible. How can you make anyone digest and interact with complex data. This is one of the aspects of data visualisation that’s near and dear to us.
The Wind Map became a part of MoMA’s collection in 2012.