It’s tempting for those who work with numbers and spreadsheets, for those who live by the bottom line and whose minds run along quantitative paths to think that art exists for its own sake. It’s tempting to think of art as something nice for the wall, pleasant to look at, maybe even restorative or inspiring in its impact, but ultimately not essential to the running of the world.
Yet consider the work of University of Maryland Computer Science Prof. Ben Shneiderman. Shneiderman is the inventor of treemaps — those graphics that chart often vast quantities of hierarchical data, such as electronic health records. He’s also famous for the eponymous “Shneiderman’s Mantra” of visual data analysis: look at an overview of the data first, then zoom and filter it, then, on demand, consider the details.
Nowadays Shneiderman is intentionally trying to manipulate massive quantities of data into data visualizations that are beautiful. Shneiderman has recognized that bringing art, aesthetics and design front and center into what is fundamentally a quantitative investigation is not a frivolous exercise. Indeed doing so helps viewers perceive connections and insights they might not have otherwise noticed. People respond to beauty; they stop, look and wonder. And that slow(er) more emotional and instinctual engagement with what on one level appears to be an artwork, can ultimately encourage viewers to better detect and then more fully understand the underlying quantitative content of a visualization.
Shneiderman is a man with art in his genes — his uncle was David Seymour, the legendary war photographer and founder of Magnum better known as CHIM. (The Leica Gallery in Washington, DC is currently hosting an exhibit of CHIM’s photographs.) Perhaps this life-long immersion in art prompted Shneiderman to recognize that many of his data visualizations mimic work of well-known artists. Some of his squarified treemaps, for instance, appear to be algorithmically-generated twins of Piet Mondrian’s paintings, while others look more like the paintings of Paul Klee and still others seem to take the quilts of Gee’s Bend as their inspiration.
Interdisciplinary art appreciation is unusual in the fields of computer science and statistics, even though it has been observed, for instance, that “the best statisticians often set their calculations aside for a while and let their eyes take the lead.” (Stephen Few) Such a lack of attention to art, outside the field of art, is a mistake, however. Viewers of art intuitively recognize patterns and relationships of scale, color and size — and those perceptions can help inform and educate viewers even when a piece of “art” is essentially a beautiful data visualization. Indeed the act of creating and viewing a visualization encourages both the creators and the viewers to look and understand the data in new ways.
It is commonplace to note that we live in a world of non-stop media. It is also becoming commonplace to adapt to this inundation of messages and data by trying to limit what we take in to just those messages from our own professional specializations. What’s left goes into our trash cans and spam tanks.
But when we limit what we see, we also limit the likelihood of our having serendipitous encounters with other fields of information and inquiry. When we cross the boundaries of the STEM fields and the arts, we find new perspectives to look back at our own disciplines. When we bring art into our quantitative measurements of the world, we not only may find our hearts lifting ever so slightly, we may discover new ways of seeing the forests of numbers in front of us.
Professor Ben Shneiderman has created a website for his “Treemap Art Project: Every Algorithm Has Art in It.” Individual works reproduced on the website are additionally being exhibited on the third floor of the University of Maryland Computer Science Instructional Center through the middle of October. The artworks are available for free download and are available as signed numbered prints in exchange for a contribution to the UM Human-Computer Interaction Lab.