Like many people, I tend to respond positively to visualizations of ideas, concepts, and topics. For me, history is a very visual enterprise, and I have never been able to imagine producing history without some accompanying visual representations to enhance the prose and the ideas I set forth. For the books I’ve published on World War II topics, photographs and maps have always been my visual mediums of choice. In fact, I still get my hackles up thinking about the “battle royals” I had with my publishers over how many photographs and maps I could include in my books. They always low-balled me from the outset, so I had to scrape for every additional photograph or map. Alas, publishing is capitalism at its finest. The more visual representations in the book, the more these additions affected the publisher’s bottom line. The scars I suffered in these battles are still with me, and I’ve never felt that my works were as complete as they could have been without the full complement of photographs and maps that I intended to use. But now, with the emergence of digital history, and new ways of providing digitized visualizations beyond simply maps and photographs, those “battle royals” may be a thing of the past. Hard copies may still be limited in what graphics and photographs they can include due to cost considerations, but electronic Kindle editions and the use of Web sites to supplement published hard-copy works certainly offer a more feasible and cost-effective approach to extensive visual portrayals of the past.
Although maps and photographs have been my traditional “visualizations of choice,” I have always been drawn to histograms, tables, and other graphs as possible ways to portray selected bits of information visually, particularly when depicting significant changes over time or for quantifying particular assertions. Yet developing such quantitative representations have always proved daunting for me, principally because I believed that they failed to capture the ‘fuzziness’ of some interpretations appropriately. For that long-standing reason, Johanna’s Drucker’s article about the subjective, interpretive nature of certain data — which she labels as “capta” — made perfect sense to me. My past experiences with attempting to employ otherwise quantitative tools to portray subjective data, or capta, have always come up flat. I worried that graphic portrayals that could not account for the ambiguity inherent in interpreted data might suggest an attempt by me to engage in “quantitative manipulation” or some other form of fallacious reasoning to make my point. Frankly, I’ve always been skeptical of statistics and other data, since they are prone to such easy manipulation. And, for that reason, complicated graphs and tables have been something that I’ve simply “jumped over” in my readings of historical monographs. Most of the time, figuring out what those visualizations were trying to say proved too aggravating and time-consuming. Few of them met John Theibault’s standards of being “transparent, accurate, and information rich.”
But the examples provided by Drucker in two of her article’s figures, Figures 2 and 4, really worked for me. They showed quite readily, as she intended, an alternative way of portraying data (or capta) that was, in her words, “taken” and not “given”; they depicted the “fuzziness” inherent in the capta – the external and internal factors that shaped and re-shaped that information without scuttling the larger point being made. Clearly, Drucker has made both a philosophical and practical point of the highest order; historians must — and I mean “must” — find visual techniques to present capta in a way that represents the humanistic methods that generated it, methods that, as Drucker claims, “are counter to the idea of reliably repeatable experiments or standard metrics that assume observer independent phenomena” (Drucker, numbered paragraph 13). In fact, Lauren F. Klein, in analyzing Jefferson’s correspondence for “breadcrumbs” about James Hemings, cites her research efforts as proof of Drucker’s broader assertion that graphic techniques applicable to the empirical sciences can mask the subjective biases of historically interpreted capta. I agree, and I can’t wait to experiment with visualization techniques that will subordinate the quantitative to the qualitative.
Many of the sample visualizations that John Thiebault included in his article stirred my imagination and opened up many possibilities for effective data-visualization techniques. Density maps in particular demonstrated both transparency and meaning; they communicated to me, through colorized graphics overlayed on actual geographical representations (like the entire United States), the ability to track specific events over space and time. This approach intrigued me most because of the possibilities of using similar maps (albeit with animation) to portray the movement of specific units engaged in, say, a World War II battle. In fact, the idea of recreating maps from my two books in such a format fascinated me; by posting them on the Web as supplements to my hard-copy books, readers could follow troop movements and engagements in real time over actual maps of the terrain, creating a visual narrative that would not only complement but perhaps enhance significantly a reader’s understanding of a battle’s flow and the inherent friction in war. As Thiebault rightly opined, “Animation increases [the visualization's] interpretive force dramatically.” Oddly enough, back in the late 1990s, the U.S. Army tried to use a similar visualization tool with computers to track combat forces in real time over actual terrain as a battle was unfolding. The tool did not work exactly as planned but instead morphed into something better that the Army uses today. Theibault’s visual examples remind me of those early battle-mapping graphics and how quickly such things can develop into other, more effective tools over time.
The visualizations that are least effective for me are the ones with nodes, “edges,” and abstract visualizations — basically network graphs. I prefer to see visual information grounded contextually in something familiar, like a map or a basic graph. The graph that Elena M. Friot generated with the Gephi program simply does not work for me. And I’m inclined to agree with Scott Weingart’s assertion that “network structures are deceitful,” primarily because they rely so heavily on adhering to specific input rules. If you don’t enter your values (or whatever) properly, God only knows what will come out at the other end. Probably the Frankenstein’s monster of all graphic portrayals — a hodgepodge of complexity and confusion. But visualizations are a good thing — a very good thing – and I want to engage in more opportunities to use them with my work.