Looking back from this final week of the semester, I have to admit that this class was both one of my most rewarding experiences at MSU and one of my most humbling. Every week I felt like I was learning something new, whether that be a new way of thinking about data and historical inquiry or learning a new skill that I before considered alien and out-of-reach. But with that came many blunders. After most visualizations I made (or attempted to make), there were often more methodological mistakes and technical errors for me to reflect on than there were celebrations of having achieved nuanced insights by successfully grappling with complex data. But with that said, in my opportunity to revisit and improve upon an earlier activity for this final reflection, what I wanted a do-over for felt rather obvious: data mapping. When I originally did it, I chose to focus on the distribution of people by race and gender within 1850 Alabama. I wanted to know if the history of sexual exploitation showed in how the population distributed across counties, but this very question was based off a extremely flawed assumption that married white men would be less inclined to abuse enslaved women as sexual objects than unmarried white men. And methodologically, the visualization was a disaster; not only was it visually unappealing, I also was not realizing the proper potential of data mapping. Space is relative and people’s “lived space” is not always delineated within the boundaries of precise counties, so to make visualizations useful, mapping is sometimes better at observing broader “spatial trends” than the kind of county-by-county comparisons I was attempting. Mapping had so much more potential as a historical tool than I originally achieved, I just needed to find the right data, the right question, and the right way to represent it. Thus, I went to the IPUMS National Historical Geographic Information System (NHGIS) to see what census data they had available related to slavery, through which I discovered something rather interesting: the US census of 1840 enumerated the population labelled as “Insane and Idiot Persons” by race.
This census question came at an interesting time of “science” being used as a tool of enslavement. By the 1850s, principles of scientific racism had become one of the central tenets of pro-slavery voices, with characters like Dr. Samuel Cartwright claiming that, with enslavement and subservience being the “natural” condition of Black people, “rascal” slaves were actually suffering from a condition of Dysaesthesia Aethiopica that needed to be treated with a regimen of hard labor and that slaves were running away because they had gone mad with Drapetomania, rather than due to poor treatment under a brutal regime of bondage. But of course, enslavers had to build these ideas over time. Hence, I wanted to examine if this data revealed any patterns of enslavers using mental health as an avenue for social control (i.e. how inclined were slave areas to mark people as insane compared to non-slave areas?), leading to the following series of visualizations in Flourish detailing who the census recorded as “public charges” and “private charges”:
At even a glance, there is a clear pattern of the data being clustered around the North as the center of “insane persons” regardless of race, until you get to “Black Public Charges,” at which point the data shifts southward. But to understand the significance of this, we must first understand the data itself.
For the sake of transparency, the above data comes from the IPUMS NHGIS dataset (available here), with the exact figures retrieved from the “Insane and Idiot Persons by Race by Public/Private Charge” data they have transcribed from the 1840 census. All data is as received from NHGIS (which generally does a great job of making data that is both human- and machine-readable), with the exception of the “Total” figures, which I derived (signified by an asterisk in the pop-up metadata) by adding all the demographics of race and “public charge” vs. “private charge” together. Additionally, data for each map is binned into seven equal-sized categories to optimize the readability of demographic differences across counties while minimizing the degree of human manipulation of the data. Each map has its own color scheme to emphasize the fact that they contain separate data with their own unique scales in their legends, which vary widely due to the different scales of mental hospitals and racial demographics.
The data is also (perhaps unsurprisingly) even more problematic than its language already suggests, as historian Albert Deutsch revealed all the way back in 1944 in his discussion of this 1840 “Census of the Insane”. When the results of the 1840 census came out, pro-slavery advocates leaned on the rising rationale of scientific racism as they touted that, of the Black people in the slaveholding South, only one out of 1558 were “insane or idiotic”, whilst the ratio was one out of every 144.5 in the free North. Former Vice President John C. Calhoun announced before Congress after the census “Here is proof of the necessity of slavery. The African is incapable of self-care and sinks into lunacy under the burden of freedom. It is a mercy to him to give him the guardianship and protection from mental death.” But the census figures turned out to be rather questionable, with even critics at the time citing how some towns in the North would be listed as having “insane” Black people, but no Black inhabitants—the most extreme example of which being the “hospital for the insane” in Worcester county listed as being home to 133 Black “private charges”, when in reality, all those residents were white people mismarked (by accident or intention) as Black (hence why there is a separate map of “Black Private Charges” that excludes Worcester).
But with all this said, the value of this data represented spatially comes to the fore when put into discussion with what past historians have already said. As Deutsch points out in trying to describe why this data was so flawed, the determination of someone as “insane” was often the lay judgment of census-takers and family members. “The so-called insane were recognized only when they were too dangerous to be at large or too helpless or troublesome to be tolerated in normal society.” Hence, the most “visibly” insane were disproportionately in hospitals and asylums (public or private), whilst the designation of someone as a “private charge” outside of an asylum was dependent upon what family members said. Plus, more importantly, enslavers had economic incentives to keep their slaves out of hospitals and asylums. Enslavers would often need to pay for the upkeep of their human property if hospitalized, and would not be able to expropriate value from their labor if they took an enslaved person out of commission by labeling them “insane.” An enslaver “could tolerate a great deal of queer behavior if his chattel slave could still be used for labor.” However, Deutsch might be missing an even deeper point: how “queer” was the behavior of the enslaved marked as mentally ill, truly? Considering the maps, mass-scale asylums were evidently concentrated in the North, but this raises the question: if there is little difference between white “public charges” and “private charges” being concentrated in the North, why is there a difference in how Black “public charges” and “private charges” are distributed? The answer is pretty much right in front of us. The scale of Black people being insane in the 1840 census (as reflected in the map legends) is small, but this is unsurprising: it would require some special circumstances for an enslaver to mark their property as mentally incapable of bringing them profit. The South’s relative embrace of Black people as “public charges” (at least compared to the North’s love of private asylums) would thus indicate a set of priorities in white southern society, in which “dangerous negros” needed to be kept under lock-and-key as a “public good” in state-sponsored hospitals. Mental health very much might have been a mechanism for control in America’s slave society.
But of course, this evidence is still mostly circumstantial, and further exploration through primary source research would probably be necessary to more thoroughly explore it. But that is what is remarkable about what this class has taught me: data and digital tools are not replacements for historical methodology, but a part of it, adding to the historian’s traditional repertoire of approaches. In the same sense you could not read one single document or one single article and call that “end of story,” data visualizations are not a “be all, end all” in a conversation. To derive value from the above data, I had to put it into conversation with what historians have already said. But at the same time, considering I was not able to find anyone since 1944 who had written directly on the relationship between the 1840 “insanity census” and slavery, this visualization is an example of how digital methods in history can reinvigorate old questions and open new avenues of inquiry. In the same sense that I was able to notice nuances Albert Deutsch overlooked by plugging the “public charge” and “private charge” data into a map (a situation very reminiscent of why geographers critique that historians “write history as if it took place on the head of a pin”), we as historians can do far more than put old wine in new bottles through the usage of digital methods. But perhaps most importantly, this course drove home the importance of being (and an awareness of how to be) a critical consumer and transparent producer of knowledge. Before, I would not have thought to scrutinize statistics or data visualizations too deeply. Yet every presentation of data is created by humans in the same sense that a historical narrative is; both have an endless number of judgment calls that go into their construction, and I as a history student and person in the digital age need to always be prepared to interrogate/critique those judgement calls and ask “what is the source of this information?” Thus, when it is the other way around, and I am the producer of the knowledge, I have to take the responsibility of being transparent on where my data comes from, how I constructed the data, and why I constructed it that way. If I was not clear in pointing to how I got the census data from NHGIS or explaining my binning scales for each map, my visualizations would be worthy of suspicion. Additionally, discussing the problematic histories behind the data I visualized will hopefully prompt people to consume this information with even greater due scrutiny. Historical data frequently walks an ethical line, and we need to be critical and honest to maintain that line.
All said, I as a Junior-year History student—something of a late-bloomer when it came to technology—was intimidated going into this class, but now look forward to the world of digital tools and data that lies before me. While I have much to learn, I look forward to learning more. Not only have I signed up for a GIS class next Fall, but I will also continue to work in LEADR and am happy to say I am finishing a project where I attempt to present text analysis as mappable data. As I move forward, this is something I look forward to learning more on how to do: how we retrieve or even create new data outside of the most traditional sources, like the census. Being a history student does not mean I cannot strive to be on the cutting-edge.
 Calvin Schermerhorn, Unrequited Toil: A History of United States Slavery (Cambridge University Press, 2018), 162-163.
 Albert Deutsch, “The First U.S. Census of the Insane (1840) and Its Use as Pro-Slavery Propaganda”, Bulletin of the History of Medicine (1944): 472.
 Deutsch, 473.
 Deutsch, 475.
 Deutsch, 479.
 Deutsch, 479.
 Richard White, “What Is Spatial History?”, The Spatial History Project, February 1, 2010. http://web.stanford.edu/group/spatialhistory/cgi-bin/site/pub.php?id=29