Moving forward from the topic of narrative mapping to data mapping, this week we looked at the geospatial projects “The Spread of Slavery in the US” (https://lincolnmullen.com/projects/slavery/) and “Visualizing Emancipation” (http://dsl.richmond.edu/emancipation/). Much like how Ayers and Nisbet pointed out in this week’s readings, projects such as these bring the issue of “scale” to the forefront in our understanding of history. In writing on the history of slavery, for example, people will write on anything from individual slave narratives, to the networks of trade and finance the institution was built upon, to state and national policies. Yet events at all these different scales play into one another; the “macro” has no meaning without a plurality of the “micro” to constitute it, whilst the boundaries within which the “micro” takes place are shaped by the “macro” context. The “Visualizing Emancipation” map embodies this point quite well: the project plots in a time series the locations of major Union troop positions and over 3000 documented “emancipation events.” On a large scale, this shows how the emancipation of slaves in the Civil War was correlated with the expansion of Union troops into the south. Yet also for each “emancipation event” represented by a red dot on the map, one can click on it to view the details of how such a broad-sweeping observation actually played out at the level of people’s lives on the ground (i.e. did people at a particular space and time self-emancipate by fleeing toward Union lines? Did Union troops tear a town apart and conscript the slaves there? Or something else?). Meanwhile, “The Spread of Slavery in the US” project uses census data to represent the proportion of free and enslaved populations at the level of territories and state counties across the US. Through this, we can observe trends like how enslaved populations were growing in the run up to the Civil War or how transport routes like the Mississippi River tended to have high concentrations of enslaved compared to free people (indicating a lot of large plantations). We can potentially achieve a greater depth and nuance to our understanding through such geospatial data visualizations.
At the same time, data mapping can open new questions based on the various levels of scale contained within a map. When one looks at the “Visualizing Emancipation” project, its depictions of national scale can illuminate the significance of microcosms within it. For example, well before Union troops expanded into the deep south, we see clusters of emancipation events around the coast of South Carolina, which prompts further investigation as to what was so distinct about emancipation in South Carolina. In the same sense, we can use these visualizations to greater illuminate investigations into more “localized” histories. Were one to research the history of slavery in Charleston county, would it not be important to know from these visualizations that Charleston had more slaves than any other US county since 1800? We can sharpen our research and the questions we ask by seeing how the “micro” relates with and compares to the “macro” in these visualizations.
Of course, we must remember that these visualizations are not infallible, and depending upon how they are used, have the potential to skew our image of the past. What is inherent in any visualization is that it is a simplification of whatever it represents. For example, in the emancipation map, “re-enslavement of African-Americans by Confederates” and “African-Americans helping the Union” are both listed as “emancipation events.” In this sense, the sea of red dots we witness on the map can be very misleading, with two very different types of experiences lumped together as the same thing. And sometimes, the very data one uses to create a visualization can be flawed. According to the census data, Michilimackinac and Detroit (Wayne, Northwest Territory) had no slaves in 1800, yet we know from Tiya Miles’s work that this was far from the case. Thus, we must maintain our critical eye toward “what is the source?” when examining geospatial data just like when we examine any other history.