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Make-up Activity 8: Critiquing Narrative Maps

Going back to the week of March 12-14’s lesson introducing us to geospatial analysis and narrative mapping (something I am embarrassed to admit I had forgotten to reflect on earlier), I find that one of the greatest takeaways came in the word’s of Richard White in his article, “What is Spatial History?”:

visualization and spatial history are not about producing illustrations or maps to communicate things that you have discovered by other means. It is a means of doing research; it generates questions that might otherwise go unasked, it reveals historical relations that might otherwise go unnoticed, and it undermines, or substantiates, stories upon which we build our own versions of the past.[1]

In this sense, Anne Kelly Knowles’s re-examination of the Battle of Gettysburg is exemplary: by putting traditional historical research on the battle into conversation with digitally-mapped representations of troop locations and 3D models created through cartographic surveying, Knowles and her associates are able to shed light on how General Lee’s lacking visibility of Union lines contributed to the ultimate failure of the Confederate army at Gettysburg.

In many respects, this project is a prime example of a well-made narrative map/spatial history. The list of contributors, with expertise ranging from history to 3D animation, reflects an embrace of collaboration that such projects require compared to more “traditional” historical narrative projects. Additionally, the map also embraces the interactive potential of its web format. Not only is the user free to scroll and examine any point of the map, the key “turning points” of generals making judgement calls based on what they could see—the moments that construct Knowles’s argument—are made accessible in the form of 3D “viewsheds”, while zooming in on the map changes the very scale of the analysis (rather than just the scale of the viewing window) from troop lines to more finite troop, cavalry, and artillery locations.

Of course, this narrative map begins to show flaws upon closer inspection. The dynamic zoom feature is not always as useful as it would first appear, with changes in the map legend and the significance behind those differences of scale not always being intuitive from a user standpoint. And perhaps more importantly, the way the narrative map engages with its sources is not as transparent as it could be. The sources for this project are presented as a long list of things that influenced the makers’ thinking, rather than the makers pointing to the specific sources they drew from and engaging in discussion with the various sources’ arguments. This is a problem that becomes especially apparent in the moments it marks as “turning points”; the makers present those instances’ status as such as unquestionable “fact”—something that simply is—rather than explaining their thinking in why they established those moments as “turning points” which warranted deeper exploration through mapping and 3D modeling. The methods of narrative mapping enable a viewer friendliness and angle of engagement with history that other forms of presenting information simply do not allow. However, this does not change how mapped history is still history, and thus arguably must not forget to practice the same level of engagement and knowledge transparency that we expect from historical writing

[1] Richard White, “What is Spatial History?” The Spatial History Project, Feb 1, 2010. https://www.smithsonianmag.com/history/A-Cutting-Edge-Second-Look-at-the-Battle-of-Gettysburg-1-180947921/?no-ist

Final Portfolio

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.[1] 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”:

https://public.flourish.studio/story/39236/

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.[2] 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.”[3] 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).[4]

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.”[5] 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.”[6] 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”),[7] 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.

[1] Calvin Schermerhorn, Unrequited Toil: A History of United States Slavery (Cambridge University Press, 2018), 162-163.

[2] 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.

[3] Deutsch, 473.

[4] Deutsch, 475.

[5] Deutsch, 479.

[6] Deutsch, 479.

[7] 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

Activity 14: Constructing Data Visualizations

https://public.flourish.studio/visualisation/323702/

https://public.flourish.studio/visualisation/323498/

Partially born out of a frustrated effort to work through the technical issues that can arise from working with a new form of data representation, I produced the above two chord diagrams for this week’s exercise in data visualization. Both diagrams were made using data acquired from the Trans-Atlantic Slave Trade Database (the download page is available here).

After discovering Flourish offered a chord diagram feature–a type of visualization I had not heard of before–I realized this might have a lot of potential for representing slave trade networks, and I wanted to see if I could make that happen given the readily available access to data on the Trans Atlantic Slave Trade via the Slave Voyages website. The TAST Database is very thorough, consisting of data derived from the records of tens-of-thousands of individual enslaving voyages, encoding available details for each ship such as the number of people it carried as cargo or where it traveled to and from. That said, both of the above visualizations are constructed with three variables: the region in Africa that is “the imputed principal place of slave purchase”, the broad region in the world that is considered “the imputed principal place of slave disembarkation”, and the “total slaves on board at departure from last slaving port” (while this does not give an accurate representation of those who survived the journey across the ocean, I ultimately judged that it was better in such limited visualizations like these to skew towards the scale of people taken from Africa in the cargo holds of ships, rather than risk erasing them by using only the numbers of those who made it to their destination). The differences between the two visualizations is a matter of what voyages I drew from. My “Slave Trade from Different Regions of Africa” diagram is intended to highlight the diversity of areas people were taken from in the slave trade, and was made using the first 1500 voyages listed in the TAST Database (albeit, the “randomness” of this data sample has a high risk of being skewed by whether or not the voyages in the database were added in a relatively random order). Out of fascination for a region that is not often discussed in the history of the slave trade, my visualization of “Slave Voyages from Southeast Africa” consists strictly of the over 900 voyages that the database lists “Southeast Africa and the Indian Ocean Islands” as the principle source of human cargo. If nothing else, this draws attention to the range of places around the Atlantic these peoples from the Indian Ocean were brought to as strangers in chains in strange lands.

Admittedly, neither of these visualizations are ideal; my intent was to create a non-directional chord diagram in which both ends of a “chord” have the same thickness. In visualizing the slave trade and African diaspora, this makes sense for emphasizing the equal importance of peoples’ former homes and the lands they were brought to in bondage. Otherwise, we risk reducing the diversity of enslaved experiences to an amorphous destination of “America” or, even more often, recreating one of slavery’s inherent injustices of reducing peoples of wide ranging origins to an amorphous mass of “Africans.” Nonetheless, my visualizations do not achieve this. After failing to make the “non-directional” flow feature work with two different visualizations, what I am left with is one visualization depicting people funneled out of Southeast Africa into diverse destinations, and another depicting people from across the African continent funneling into a deceivingly singular “American” endpoint. While it can be informative and interesting to contemplate the relative scales of where people came from and ended up over the course of the slave trade (and even suggest that visually that the Southeast African slave trade might have covered a wider proportion of the TAST come the 19th century), the one directional nature of these visualizations makes these seem not enough like chord diagrams and too much like pie charts. More work and technical know-how is needed to make these achieve their potential.

 

Activity 13: Critiquing Data Visualizations

https://www.nytimes.com/interactive/2019/01/26/opinion/sunday/paths-to-congress.html

Before taking this class, I admit I would have been very inclined to take a data visualizations at face value. Data would at times feel like an unquestionable black box, and if I failed to understand a what a visualization was communicating, I took that as a personal fault rather than reason to question whether or not a visualization was good. But now, the questions come much more naturally: What is the argument of a visualization? By what means was the data constructed? What are its sources and variables? How well does the visualization fit its purpose? How intuitively does it convey its information and argument? And perhaps most importantly, what does the given visualization overlook or leave out? Does it actually contribute something to the analysis, or is it merely superficial?

A prime example of this process came in interrogating the above New York Times article. At a glance, this leaves the impression of a visualization that is well made (i.e. strong visual appeal) and fits the creator’s purpose. The author argues that there are certain patterns in the lives of people who become Congressional representatives which separate them from the American public, and we see in the visualization several points where the different representatives’ “paths” converge. But on closer inspection, the creator is trying to make it look like there is more substance than there actually is. The X-axis falsely implies a chronological order, when in reality, why “real estate” is closer to “military” than it is to “private law” on the X-axis is anyone’s guess. The Y-axis also has no clear meaning. Worse still, the visualization lacks data transparency: it offers only a mild disclaimer that items are not in chronological order in small print at the beginning, the methodology is stuck at the end in small print, whilst, despite allowing viewers to single out the “path” of an individual representative, the descriptive details like how long a said person was in a given line of work is left out. As far as this visualization is concerned, someone who worked on Wall Street for one year before quitting to get a Masters of Public Policy is the same as someone who got a Masters of Business Administration and then worked on Wall Street for 10 years. In a word: the author could have been far more honest with a different visualization. People can lie with data, either through convenient omissions or visualizations that obscure their flaws, making the ability to interrogate data all the more important as a historian or general consumer of knowledge.

Activity 12: Received and Derived Data

According to our reading of Hadley Wickham’s article “Tidy Data”, a dataset is “tidy” if (1) each variable forms a column, (2) each observation forms a row, and (3) each type of observational unit forms a table. Otherwise, the data is not structured in such a way so that the analyst can easily derive meaning from it (Wickham 1-3). For historians, this “tidy” structure is useful in part because of the sheer intuitiveness. While I was attempting to transcribe a portion of the 1799 George Washington slave ledger, I was struck upon the realization that I was building my spreadsheet to be “tidy” like this without intentionally designing it as such: designating the first column with the variable “name” and ascribing other variables like “location” or “labor status” to subsequent columns simply came naturally. The way George Washington structured his ledger was only useful to his own purposes, whilst the tidy structure means anyone can look from left to right to observe that the enslaved man Sam Cook was considered property of George Washington, lived in his Mansion House, and was considered too old to work (“passed labor”).

Of course, this process has also highlighted the significance of the contrast between received and derived data. The original intent behind the received data can be obscured or recreated in derived data (and which is “better” is always a contextual matter). For example, when I created the column “labor status,” I chose to take from how George Washington would categorize slaves at different locations under the special subheadings “passed labor” or “child,” and turn that into three “labor status” designations in my table: “working”, “passed labor”, or “child.” In this single decision, there were multiple layers of interpretive judgment calls. On one hand, it assumes that, in how Washington would categorize his slaves in this manner, there was a subtext of thinking of the enslaved as either useful, no longer useful, or eventually useful. But in deciding to use this subtext in the making of my derived data—which in theory is staying true to the intent of the received data George Washington left us—I also had to realize that I was recreating slavery’s injustice and dehumanization in doing so. In most circumstances, “child” would be considered a social status related to age and kinship; listing it as a labor status feels blatantly wrong. Yet on another flipside, if one were to make “child status” a yes-or-no category in the derived data, this would avoid recreating the injustice but at the cost of erasing the injustice contained in the received data. And when you get into deriving categories that simply are not listed in the received data (such as creating data on the gender of enslaved people based off their names or marital status in a ledger), these interpretive risks become an even steeper slope. If nothing else, this highlights why it is so important that we as historians need to be very transparent in saying not just what our final data analysis says, but how we reached that data and why we derived it as such. Data and statistics are powerful tools, and as the practice of deriving data shows, we can pull far more than meets the eye from information we are directly given to answer historical questions, but we can also create lies in doing so. In the same manner, one should not accept contemporary data at face value, but always ask how that data was created.

Activity 11 Creating Data Maps

https://public.flourish.studio/visualisation/287233/

For this week’s exercise in creating data maps, I ultimately chose to narrow in on using the census data to map white and enslaved populations by gender in Alabama in 1850. Admittedly, I came to this after experimenting with a few different scales and questions. I first thought to map how the locations of the free Black populations changed over time, but scrapped that after realizing the previous map by Lincoln Mullen we examined last week had already done that. I then attempted to see if I could notice any patterns in how the older (over 45) and younger (under 14) enslaved populations were distributed, but quickly realized the sheer difference in population sizes spread over the US and between these two age demographics made creating “bins” from which anything meaningful could be discerned difficult. Thus, this led me to narrow my scope down to the single state of Alabama in examining demographics of sufficiently comparable scale: male and female white and enslaved populations.

Considering the prevalence of rape and sexual exploitation in the history of American slavery, I wanted to know if there was a tendency for areas that had higher populations of white men and lower populations of white women to also have disproportionately high populations of Black enslaved women. In other words, I wanted to know if the census figures would suggest evidence of male enslavers actively seeking out black women as sexual property on a large scale. The map I created, with the population categorized into 24 evenly-sized bins, does not appear to confirm this hypothesis. Counties that had high populations of Black enslaved women also tended to have comparable populations of Black enslaved men. Quite the opposite of my original thoughts, some counties that had more enslaved women than men appeared to have similar white male and female populations. In hindsight, this should have been an expected result: if enslaved women were preferred as domestic servants, they would be in greater demand in counties that had more white married families. Additionally, as we know from reading both Miles’s and Schermerhorn’s books, accounts of married men sexually abusing their slaves were far from uncommon. That said, something that did jump out at me in this map is how, regardless of the numbers of white women or enslaved men, counties having higher numbers of enslaved women than white men were not uncommon, and were rather prevalent towards the middle of Alabama. If nothing else, a map such as this is an example of the dangers that can come with data mapping without sufficient care: while the map does not provide direct evidence for white men explicitly purchasing Black women as sexual objects, there is still plenty of room for such circumstances to have taken place.

Activity 10: Strengths and Critique of Geospatial data in History

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.

Portfolio Activity 9: Narrative Maps

https://uploads.knightlab.com/storymapjs/e70a01f18bb38d50c1704b28f7882a37/hst-251-test-map/index.html

For this exercise in creating a narrative map, I chose to use Storymap JS to trace the path of a beaver pelt took from its moment of acquisition from Native trappers, through its transport by enslaved labor, and to its final consumption as an alienated commodity across the Atlantic. The “on the rails,” step-by-step nature of Storymap JS seemed to make perfect sense for this style of commodity history. However, I quickly discovered the matter was not as straight forward as I originally assumed: beavers were hunted and entered the commodity chain at numerous points of entry. In the real world, there was no single, teleological path leading from Wisconsin to England. Some traders went east to New York, while others went north to Quebec. I wanted to show multiple branching and intersecting paths, yet the “rails” began to feel constricting. Additionally, when it came to my document methodology, the very structure of the “rails” felt shaky. Ideally, this singular-path narrative would be perfect if I had the documentation to identify the purchase of a singular beaver pelt and follow it through every point of exchange. But aside from such a perfect set of documentation existing being slim, the odds of finding it digitized are even slimmer. Thus, the path I “reconstructed” felt a little anachronistic in how it pulled documents ranging from 1763 to 1835. It was also disappointing to discover that much of the rich documentation Tiya Miles drew from for the intersection of the slavery and the fur trade simply were not digitized.

Nonetheless, after completing this assignment, I could not help but feel a sense of pride that was rather unique compared to past assignments. While it is far from polished, what I created is still a piece of presentation-friendly material that I wish I had had the knowhow for in the past. And even on a more intellectual level, this exercise forced me to more closely grasp how history transpires over space (something I did not realize I was lacking before). I have dealt with commodity histories in past classes, but this narrative mapping enabled me to grasp the complexity of commodity chains in a way that simple textual narratives are unable to.

Week 8 Text Analysis

This week, I learned about text analysis using Voyant and topic modelling with Mallet. Admittedly, even though this was not my first time seeing Voyant, the tool and methodology involved still can feel like an overwhelming experience. The very idea of “reading without reading” a whole corpus of texts does not always feel intuitive, and it still took some time for me to get my barings toward understanding “what” exactly is being represented in the various windows in Voyant.

Nonetheless, after downloading the corpus of plain text files off of the Documenting the American South website, I ran the corpus through Mallet to organize the texts into 40 different topics. Like everyone in the class, the first topic I examined in Voyant was the Haitian revolution (distinguished by the appearance of words like “Hayti” or “Ouverture”), but I quickly realized that text analysis is subject to similar limitations as primary reading, but in a scaled up manner: it is hard to comprehend what you are looking at without enough prior knowledge. Thus, after taking another look at the topics identified by Mallet, I noticed that one appeared to deal with the slave trade (“distinguished by words like, “Africa”, “ship”, and “boarded”). I then took the top 25 documents in that topic and labelled them with their respective publication date, as I was interested in seeing if there was any way rhetoric surrounding the slave trade had evolved over time. After loading the 25 documents into Voyant, I noticed the “correlations” feature said that there was a correlation between the words “Africa” and “great” in the corpus. I was then curious to see if this was a rhetorical correlation that evolved over time: were people more inclined to use the word “great” while referring to Africa as time progressed, and the slave trade increasingly became seen as an “antique of the past”?

The answer turned out to be a resounding “no”. The figures at the bottom of this post were generated using the “StreamGraph” and “Trends” tools in Voyant, where blue in the graphs represents the word “great” and the brown represents “africa”. While it is clear to see that someone who was speaking of Africa was also more likely to use the adjective “great”, by no means is there a clear chronological pattern. In hindsight, this should not have been surprising: while I was going through all the CSV file containing the metadata for each document, I noticed that most documents identified by Mallet in this topic were slave narratives. In that case, the real correlation might be as simple as, the more someone talked about Africa, the more likely they were to use a celebratory word like “great.” This also raises an issue that might arise from using topic modeling to guide text analysis: people that were indicting the slave trade were the most likely to be talking about it extensively in the first place. But as a consequence, topic modeling carries the risk of cherrypicking your document sample if done carelessly.

Week 7 Website Architecture

For this week’s exercise in conceptualizing the website architecture of digital history projects, I was inspired after reading in a separate class W.J. Rorabaugh’s The Alcoholic Republic to base my website concept around the intersection of the movements for alcohol prohibition and slavery abolition. More specifically, the two issues were frequently intermingled, with most temperance warriors also being abolitionists who would treat slavery to a master and slavery to “the demon rum” as analogous issues of unfreedom. Thus, what you see below is a conceptual architecture on how I would design a site seeking to convey the conflation of slavery and alcohol prohibition.

At the homepage or “base” of the site, I would include an introduction and summary of the argument I wish to convey. From there, the site branches out into four main pages for the reader to visit in whatever order they choose. It is important to note that one of these pages is a centralized bibliography for my website, containing citations for all the secondary literature I draw from and a “pool” for all the primary source documents I incorporate into the website. The three remaining pages are each a subtopic, with the relevant primary documents stored within them (albeit, were I to actually create such a website, the question of “what documents can I have digitized?” would be a far more pressing issue than the hypothetically “ideal” documents I use as examples in my hand-drawn map). Each subtopic would contain a brief descriptive overview, along with a collection of documents where, if the visitor were to click on them, they could read them personally and view commentary and annotations. The first sub-topic, “Restricting the Enslaved”, would cover what is known about the drinking patterns of those held in bondage: many enslavers used alcohol rewards to incentivize the compliance of the enslaved, but the flow of alcohol in this context was seen as something that had to be tightly limited and controlled so as to not spark “rowdiness.” I am aware that both the former colony of West Jersey and the state of North Carolina passed laws making it illegal for slaves to purchase alcohol for this very reason, and I would use documents like these pieces of legislation for visitors to view. The next subtopic, “Dry Abolitionists”, would cover the aforementioned conflation of drunkenness and unfreedom, and would incorporate Temperance propaganda, like Herman Humphrey’s poster, “Parallel between Intemperance and the Slave-Trade” (also viewable in Rorabaugh, p. 215), along with accounts of the Black Temperance movement, such as Frederick Douglass taking the abstinence pledge. The third subtopic, “The Transnational Legacy”, would ideally be more interactive (assuming I have the technical knowhow): the conflation of alcohol and slavery bled over into the Liberian Temperance movement and into the Scramble for Africa, in which the Brussels Treaty of 1890 being signed by America and the European powers to prohibit both the slave trade and liquor trade in Africa. Ideally, I would want visitors to spatially understand the extension of abolitionism and temperance outside the US after the Civil War. Thus, this would ideally contain a map with points for where each document comes from.

 

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