Visualizations: U.S.-American Claims Against Mexico

[cross-posted at the class blog]

As our summer History and New Media minor field readings course draws to a close, one of the final assignments is to create our own data visualizations.

For my fall 2012 “Clio 3” class, I built a database of U.S.-American claims against Mexico before 1846. These claims form a collection in Record Group 76 at the U.S. National Archives. You can learn more about the database and the project here. As part of that project, I included a series of visualizations. For this assignment, I’m building on those visualizations.

At the time I was in CLio 3, I had 38 of the 109 claims heard by the 1839 U.S.-Mexican Commission in the database. Although I recently went to the National Archives and am now up to claim 61, I haven’t inputted the data yet. As such, I instead am making my visualizations based on a chart from pages 181-185 of Dr. Peter Jonas‘s dissertation about the process of resolving the claims. His chart includes claims up to 1837, with short summaries.

First I went through the chart and placed the claims into eight categories:

  • Seizure/destruction of goods/ship
  • False imprisonment, harassment, expulsion
  • Seizure/destruction of goods/ship with crew harm
  • Nonpayment for goods/loan
  • Forced loan
  • Libel
  • Ship fired upon
  • Ship conscripted

Initially I only had seven categories, but I noticed that some claims included both the seizure of a ship or goods, as well as physical harm to the crew, so I made that into its own category.

I then created a spreadsheet in Google Docs with each claim and its classification. Following that, I totaled each category and made a pie chart, showing the percentage of the 57 claims that fell into each:

Sadly the embed code that Google gave me didn’t work, so we’ll have just to use a picture of the chart for now, and for this assignment I didn’t bother with using the Google Charts API that I did for Clio 3.

This chart tells us some information. We can see that most of the claims had to do with confiscations of ships; this often happened due to customs authorities and ship owners/captains interpreting customs laws differently.

But this chart can only tell us so much. It lacks one of the essential elements of history: change over time. So my next step was adding the number of claims that fell into each category by year, and creating a visualization of that:

This is an even more interesting visualization, as it shows both changes in the numbers of claims by year of the incident and what types of incidents took place. So then that leads to more questions, particularly about why certain incidents took place when.

Let’s take, for example, the large spike we see in 1836. In that year, the relationship between the United States and Mexico deteriorated seriously as mostly U.S.-born rebels in Mexican Texas, with a great deal of unofficial aid from across the Sabine River, revolted and split Texas from Mexico. Is the rise in incidents against U.S. citizens, particularly those involving harm to the crews of confiscated ships (a category that also spikes in that year) indicative of the deteriorating official relationship between the two countries? This reflects what I see being one of the central questions of my future work: How did the deteriorating diplomatic and cultural relationship play out on the ground? Is this spike indicative of a correlation? Or is it indicative of claimants feeling they needed to add a physical harm component to an economic one to get recompense? Those answers will only come from further archival research on my part.

But this exercise shows that visualizations are not merely ways to illustrate a point, but to raise new questions. I most likely would not have seen that particular pattern had I not run these visualizations. I will be curious what patterns will emerge as I input more data into my database, and categorize those claims. Stay tuned!

2 Thoughts to “Visualizations: U.S.-American Claims Against Mexico”

  1. […] in a blog post for my New Media minor field readings, I did some more visualizations of other aggregated data (via Jonas’s dissertation) to find patterns in the […]

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