Census forms may be the place where America’s bizarre compromises related to race and citizenship appear most clearly. I’ve been exploring two datasets related to the Census for possible projects. One is the 1865 Census taken by New York State. The other includes a tabulation of that Census and election-district level returns for three failed constitutional amendments extending suffrage for Black men. Two researchers compiled this data in 1984 from the Census books and New York State Assembly records, and it’s now available in the Inter-university Consortium for Political and Social Research repository. I also have a third source, tabulations of the 1865 Census entries published by the New York Secretary of State in 1867. Please note that I am not sure how to handle the demeaning language used in the Census. I’m repeating it here to discuss a harmful categorization process but I need help to know if I’m doing that in a way that is respectful. Also please note that the nationalities assigned to White people are random. I wanted to see what I could do with nationality data in a visualization but matching them accurately for White people is a big project that I haven’t completed. The total counts of different countries of origin are accurate but I don’t know the gender ascribed to them in the Census and I don’t know how many were over the age of 21. I could do it for Black residents because there were so few of them and for Tuscarora residents for very questionable reasons discussed below.
Voter Categories
New York State held referendums in 1846, 1860, and 1869 asking if a property requirement for voting, which did not apply to white men, should be eliminated. These amendments were about moving some people into a new category: “native voters.” Voters have two columns in the 1865 Census: native or naturalized.
- White men over the age of 21 born in the United States with one year of residency in New York were native voters.
- There are two categories of non-voters: “aliens”, and “colored person not taxed.” All of the immigrants in the Census book are described as white. They could gain citizenship by renouncing any hereditary titles or loyalty to foreign governments, and vote without property restrictions (but they will always be marked in the Census as a different category than native voters).
- Men described in the Census as Black or mixed race go in the native voter column if they have lived in the state for three years, own property worth at least $250, and pay taxes on it. Very few people met the $250 threshold regardless of their other categorizations.
All of the referendums failed. The majority of Black men could not vote in New York State until the federal 15th Amendment was ratified in 1870.
Lewiston, New York
I made a typical dashboard using the voter returns and Census books for one town in Niagara County. I chose Lewiston because it’s small enough to work with (the Census has 2,998 entries) and two Black voters lived there. The other places with Black voters had too many people for what I wanted to do.
Lewiston is on the Niagara River (which is the Canadian border) and just north of Niagara Falls. It completely surrounds the Tuscarora Reservation (the Library of Congress has a map of Lewiston in 1860 accessible here). The Niagara Falls Underground Railroad Heritage Center has compiled evidence of significant activity in Lewiston and throughout Niagara County, including several safe houses and residents who had been enslaved and either settled in Niagara County or travelled through on their way to Canada. There was also significant organizing for abolition and before the late 1860s, organizing for universal suffrage for men and women. It is too easy to incorporate this history into myths about “free” states. The referendum votes add cracks to overly simplified stories about Northern states like New York.
Lewiston residents voted against the referendum, as did Niagara County overall:
I can complicate the yes or no story by showing how close the vote was, how many eligible voters did not participate, and how many were not allowed to. I can also nod towards intersectionality by showing gender, race, and nationality in one table. But using these sources and presenting them in this way requires adopting their “cultural logic” as Cottom describes.
Implied Precision and Stability
The cultural logic of Census forms is that race, gender, and nationality are distinct, factual categories and it is possible to count people according to those characteristics. Visualizing Census data in bar charts and tables requires obscuring the reality of Census data, which is that it is a mess. When you look at original hand-written Census records, you are more aware of the process. Things are misspelled or scribbled out, the handwriting can be hard to read. None of the population totals match across my three sources. Further, someone looking at one Census book might not understand how much movement occurs between categories. Even place of birth frequently changes across Census records for the same people.
Implied “Sameness”
Drucker’s visualizations show subjective experiences of time and space related to the point of view of the person experiencing them. This vote meant different things to people based on their bureaucratic identities, their personalities, and their life experiences. The first failed vote was experienced differently than the second, and the third. Showing that many people did not vote, even as others fought for the right to do so, points to different subjective experiences but it doesn’t visualize them. A person whose right to vote was in question probably experienced this event very differently from a person given the authority to decide that question for someone else.
Lack of Situated Knowledge
The state legislature tabulation for Lewiston includes a footnote: “in addition to the above 572 Tuscarora Indians are reported as residing in the town of Lewiston.” Population tables about the Tuscarora and people living in other reservations are in a separate section.
Everything about how I treated the people living in the Tuscarora reservation is questionable. Guiliano and Heitman describe how data has been used to fragment Native American and indigenous communities, and asks scholars to work with those communities before using data collected for the purposes of colonization. They describe a different data culture that necessitates collaboration. There are so many questions I can’t answer without doing this. For one, the Tuscarora Nation is not “in” Lewiston. I don’t know how someone living on the reservation would feel about being included in a Census of Lewiston. I assigned everyone in the reservation “Tuscarora” as a nationality and “American Indian” as a race, which is what the category is in a modern Census. But I doubt everyone living on the Tuscarora reservation are considered Tuscarora members. Even if they were, the word nationality may be inappropriate, and it may not make sense to treat it equivalently to a term like “American” or “German.” The population tables do not include race, so I can’t show that people of other races lived there. But I know that they did from other sources.
It felt wrong to ignore the Tuscarora because as Guiliano and Heitman describe, I’m in a data culture that values more and more public data. But the Census is such a harmful document that it is wrong to carelessly use it. If this was a real publication I would treat that data differently.
Strategy: Combining Close Reading with Distant Reading
D’Ignazio and Klein in Data Feminism and Cottom in “More Scale, More Questions,” describe restoring context and situated-ness to data visualizations as a strategy. I tried doing this in three ways: I re-created everyone in the Census as colorful shapes, added annotations, and added randomized numbers to avoid sorting people so strictly by category.
This is everyone with a Census entry in Lewiston in 1865:
In some ways this lessens the false preciseness because you can’t read it as a specific tally of any group. It might help a reader see that there are many possible points of view of the referendum vote, but not much more than the bar chart or table above. What I really wanted was a way to make the categories appear more porous. It’s still really sharp shapes when they should be fuzzy.
For this one, I added random numbers which are the coordinates for each line. I also added annotations with information about specific people or information that is relevant to an individual person’s experience:
I like that this is hard to read from a humanities standpoint even though it isn’t helpful for understanding the data. It reminds me that I’m never really seeing the people themselves when I’m looking at a Census entry. When I collect Census entries on people it really feels like I’m “seeing” them, but I’m not. It reminds me that data is taken.
It fails in terms of demonstrating how different people would have different experiences of the event. The people whose citizenship is in question are nearly impossible to find. In Tableau, you can pull them out with filters, which helps a little bit. The annotations may also help.
