Spurred by my enthusiasm for data visualization (thanks, Michelle McSweeney!) and an awareness of my gross underutilization of the vast resources of the New York Public Library, I opted to attend the Information Visualization Open House at the NYPL’s Center for Research in the Humanities. The event offered a lineup of speakers sharing ways that the Center’s holdings have contributed to DH pursuits. And, while I had expected to leave with ideas for ways to leverage their collection for both my graduate studies and my own classroom, I came away with something more important: an astute appreciation for the GC’s motto of “public education for the public good.” The presenters’ work really fell across a spectrum ranging from practical service to perhaps a little academic navel gazing.

Clearly benefitting a wide swath of users was a project presented by a representative of the Science Industry Business Library: a tool called Simply Analytics (SA) whose tagline is, “Analytics for Everyone.” SA provides a range of easy-to-manipulate parameters to help non-DH humans visualize data in truly pragmatic ways. The presenter demonstrated how easy it is to determine, say, where a prospective liquor store owner might find an untapped Brooklyn space ripe for business. In just a matter of clicks, he had narrowed his search to two spots. (For the academic among us, he showed how to access metadata on the data sources to boot.) The demonstration was made all the more powerful in that the example came from real life. SCORE, a group of retired and self-professed tech-nervous business people who offer free advice to small business owners, had used SA to help a client research exactly that question. So, while we may argue about whether increasing the number of liquor stores boosts the public good, the tool certainly allows greater access to the kind of data visualization tools often reserved for the tech savvy or for those with money to purchase proprietary software or hire someone to do the work. The only drawback is that it seems you have to be in one of the SA-licensed NYPL buildings, like the main branch, the Schomburg Center, or the Science Industry Business Library itself, to get the free access.

On the other side of the spectrum was the Photographers Identities Catalog (PIC), a labor of love by an NYPL photography specialist. The project is a searchable database that visualizes on a dynamic map where prominent photographers were born, did their work, and, if applicable, died. I was particularly interested in the project because my students are currently investigating figures from the Harlem Renaissance, among which are photographers James Van Der Zee and James Latimer Allen. The PIC is complicated and not terribly intuitive, though exploring certainly sparks interesting questions about the arc of a photographer’s life or about demographic or historic trends (since you can filter by, among other categories, gender, nationality, and date). My very surface exploration was ultimately unrewarding, as the data varies wildly by photographer without a particular pattern. At first, I thought there was a predilection for white, well-known photographers. For example, Dorothea Lange has 491 marked locations, while Raghu Rai and Dewoud Bey each have only 1. But, famed Ansel Adams has only 2, and Edward Weston just 1, while my students’ lesser-known research subjects James Van Der Zee and James Latimer Allen have 4 and 5 respectively. Chester Burger, whose collection rests solely at the NYPL, has 1261 locations, yet his work isn’t visible online in a quick Google search, while Mary Ellen Mark has only 2 locations, and neither is connected to the Miami image that is part of the NYPL collection. So, representation seems haphazard. (The site displays no photographs by the photographers, so copyright restriction is not likely to be the issue either.) Further, while the site does a lovely home-page job of encouraging exploration and inviting non-academics in, the interface isn’t built for the lay tech user. The PIC may have potential for helping scholars in the field of photography, but it seems in stark contrast to Simply Analytics which appears to be built with a wide range of the public in mind to allow them to dive in for pragmatic needs.

Other presentations fell somewhere in between, from how the NYPL’s historic visualizations can spark good design thinking to combining maps and pie graphs to analyze spatially the 1880 census data about Greenwich Village. All delightful. But the full array really got me thinking about the work we do at the GC. Particularly, I hope to ask myself of future projects questions such as, “How does this work directly benefit or interface with the public?”, “What does this tool or project assume about that public, its abilities, and its needs?”, and “How can we communicate the limitations of our data to avoid misleading users?”

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