Last week we drew to a close our second year of Urban Media Archaeology, a graduate studio in which my 15 students; my Technical Associate, the ever capable Rory Solomon; and I work together to map historic media networks. Last fall, in the inaugural section of the class, our students mapped everything from the history of walking tours, to newspaper company headquarters, to Daily News delivery infrastructure, to the social lives of East Village zines, to key sites in carrier pigeon history. This semester the projects were no less innovative; we mapped “media actors” in the debate over the Atlantic Yards development; data-driven systems of graffiti removal; the spatial history of the Young Filmmakers Foundation (intended to seed a larger map of youth media organizations in New York); the evolution of street signs in Manhattan since the late 19th century; the old West Side Cowboys of Chelsea (this project, one of my favorites, involved “ontography“; see below); the changing landscape of independent bookstores in Manhattan and Brooklyn; the social networks of the Soho Fluxus community; 100 years’ history of theaters around Union Square; key individuals and places in the history of subway graffiti; the spatial history of the Bell Telephone system; the forgotten histories of official memorials and murals in East Harlem; surveillance networks in Corona, Queens; locations in Woody Allen’s films; and historic jazz performance venues.
We learned this year, as we did last year, about media archaeology, about maps as media, about the spatial- and digital humanities, about archival research, and about design methods and prototyping strategies. And this year we added a new lesson on “spatial data modeling” to help students translate their conceptual models into “database language.”
We also learned quite a few things that could never be spelled out in the obligatory “learning goals” section of a syllabus. I’ll try to describe a few of those hard-to-articulate lessons here:
Learning Doesn’t Happen in 15-Week Chunks. Many students commented that they had a hard time knowing when to stop researching. They had a tough time gauging when they had enough archival images, enough data to discern a spatial pattern of some sort, enough contextual information for each of the records they plotted on the map. Many of my students spent weeks sorting through official data sets or in various archives, either frustrated that they hadn’t yet tracked down the “magic data set” or the “magic box” of archival treasure, or thrilled to have found much great material — and in many cases, eventually overwhelmed by the volume of material they gathered. Whatever their individual experiences, they almost invariably felt incomplete at the end of the semester. “If I had another week, I would’ve….”
We had to come to terms with the fact that learning — the most natural, meaningful kind — doesn’t stop at the end of the semester. The most exciting projects, with the most potential for future development, will inevitably remain undone — much to the benefit of those who come after us, who’ll take inspiration from our work and build upon the foundations we’ve laid. DH projects in particular require that we recalibrate our internal self-critics to take into account that fact that our work is often only one small part of a larger, longer-term endeavor. At the same time, this “recalibration” doesn’t diminish our sense of personal accountability; knowing that others — our contemporary and future collaborators — are counting on us, and knowing that our audience is larger than our professors and ourselves, we appreciate that there’s a lot more at stake than an end-of-semester grade.
Learning Can Be Deeper, and More Rewarding, When It Pushes Us Out of Our Comfort Zone. Some students commented that venturing into new research venues and employing new research skills; having to gather the pieces to construct a “multimodal,” spatial argument; and realizing that they needed to have something to show for all their work, resulted in an unprecedentedly deep level of engagement. “I’ve never been this invested in, or learned this much from, a research project before.” I suggested in our last class that most folks can BS their way through a 20-page seminar paper, but when you have to show stuff to back up your claims — when you have to plot records to support a spatial argument — your research will require getting your hands dirty.
Some students also learned not to fear the error message. We created our own mapping system, and asked students to construct their own data models, so they could see what’s behind the social media systems that they regularly use — systems that have been naturalized and seamlessly integrated into their everyday lives. Opening the black box, if you’ll pardon the cliche, requires that we test its limits, that we often push the system until it breaks. And when we do break something — when we encounter one of those ugly “TemplateSyntaxError” messages — rather than panic or give up, we can actually learn to hear what the system is telling us, and work with others in class — most likely those with a different set of technical skills than our own — to fix the problem. These small defeats and victories tell us a lot about how a system works. And ultimately we learn more from these error-pitted processes, uncomfortable though they might be, than from those that proceed perfectly smoothly.
Even the “Objective” Calls for Reflexivity. Many students came to realize that the primary materials they were gathering were determined primarily by choices they made — which streets to travel, which times of day to visit, which people to talk to, etc. Even data — either self-generated or pulled from an “open data” bank — aren’t immune to researcher bias or subjectivity. We came to be keenly aware of how data and other research materials come into being, and are discovered by ourselves and other researchers — and many students decided to build themselves, through self-reflexive methodology maps, into their own projects. As David Bodenhamer writes in “The Potential of the Spatial Humanities” (In The Spatial Humanities, Indiana University Press, 2010, “A humanities GIS-facilitated understanding of society and culture may ultimately make its contribution in this way, by embracing a new, reflexive epistemology that integrates the multiple voices, views, and memories…” (29).
Mapping Isn’t Always About Big Data — Or, Mapping ≠ GIS. Several students began their projects looking for the data “motherlode” that would reveal clear temporal and spatial patterns and allow them to make big, profound, earth-shattering claims. “I intend to correlate huge changes in socioeconomic data to movements in these massive infrastructures.” “I plan to develop a comprehensive map of all the people and places involved in this social movement.” When, by mid-semester, they hadn’t experienced the “data epiphanies” they were waiting for, many were either apologetic (for not looking hard enough or in the right places), frustrated, or defeated.
I wondered if perhaps, influenced by the prevalence of GIS and “data fetishization,” and by the way so many of us tend to use the terms “mapping” and “data visualization” interchangeably, my students assumed that their maps had to show large-scale patterns in quantitative data. Many of them had forgotten that the personal and the partial, the subjective and the speculative, are also mappable — and worthy of being mapped. The “GIS mindset” was stifling to some students. As Bodenhamer puts it, GIS can appear “reductionist in its epistemology. It forces data into categories; it defines space in limited and literal ways instead of the metaphorical frames that are equally reflective of human experience” (24).
Eventually coming to terms with the “non-systematicity” of their conclusions, accepting that they wouldn’t be creating a heat map showing conclusive evidence of quantifiable macro-scale changes, they recognized the breadth and flexibility of mapping as a method. We can map the qualitative, the necessarily incomplete and inconclusive, the fuzzy. And we can even infuse a little poetry into our data models (as many of my students did by developing creative many-to-many relationships) to capture the nuance and nebulousness of our subjects.
Our Maps Can Contain an Implicit Critique of Mapping Itself. Despite whatever opportunities we might have to detourn the map and its underlying database, we sometimes run up against the operative or epistemological limitations of these systems. Not all stories are spatial. Not everything can be plotted to a point, line, or area on a map. And not everything can be translated into a data model — at least not without losing something. Many of my students offered amazingly insightful reflections on the values and limitations of mapping as a method and a mode of presentation in their own projects:
I think proximity is a point to be made, but not the whole point, and it might push users to get caught up in spatial observations. (via)
I’ve noticed that all the presentations involved navigation tasks that would seem obscure without the author walking us through them. Why do the maps come so alive when we have a guide walking us through them? (via)
At its most basic, my conceptual point about Atlantic Yards is to look at as much as you can. When you see my map from far enough away, it looks like all of Brooklyn is covered in green circles, but zoom in further and there are gaps begging to be filled in. And I think for now at least, that’s how it’s supposed to look. (via)
They’ve come to accept that some gaps are supposed to be there, that their projects will be defined by holes and incompleteness. In recognizing what maps can and can’t do well, we’ve been able to look at them more critically as media, and at mapping as a method — as only one of myriad media and methods at our disposal.
Bodenhamer advocates for the integration of multiple media formats — “a letter, memoir, photograph, painting, oral account, video” — and types of research material — “oral testimony, anthology, memoir, biography, images, natural history and everything you might ever want to say about a place” — into what he calls “deep maps,” maps that are “visual, time-based, and structurally open” (26-8).
They are genuinely multimedia and multilayered. They do not seek authority or objectivity but involve negotiation between insiders and outsiders, experts and contributors, over what is represented and how. Framed as a conversation and not a statement, deep maps are inherently unstable, continually unfolding and changing in response to new data, new perspectives, and new insights” (26-7).
Whether we regard mapping as the “umbrella” strategy encompassing these other methods and modalities, or mapping as only one component of a “deep” spatially-oriented methodology, it’s important that we think critically about each component of our “toolbox” — that we resist the temptation to fetishize the data or the map, that we appreciate what each of our tools can and can’t do, that we devise a strategy by which these various tools can work in a complementary fashion to do justice to the rich spatial and temporal dimensions of our subjects of inquiry.