As an AHA-Mellon Career Diversity Fellow, I used GIS technology to craft a series of maps presenting new interpretations of the Santa Fe Trail for the National Park Service. I show how humans, particularly Anglo American property owners, dominate place names along the trail; when they are stripped away, however, a rich tapestry of plants, animals, and landmarks emerges.
Like all good projects, this one started in a library, where I used place name dictionaries to research the 194 Santa Fe Trail sites that the National Park Service has identified as "high potential." Then I matched each site with its latitude/longitude, giving me an Excel spreadsheet that I could import into the mapping platform Carto. I owe a debt to Mike Olsen, historian and member of Santa Fe Trail Association, for helping me find categories (i.e. linguistic origin) that might complicate the traditional narrative of the trail.
In the spring of 2016, I took a seminar called "Mapping Latin America" taught by Jeff Erbig. I'm not incredibly well-versed in Latin American history, but the spatial aspect spoke to me--especially when I found out that, before the U.S. completed its transcontinental railroad, the quickest way from the East Coast to the West Coast was through Panama; American investors actually incorporated the railroad the same year that gold was discovered in California. Thus, even though American eyes viewed Panama as part of the tropics--languid, unhurried, exotic--they also recognized its potential for efficient transcontinental transportation.
Luckily, the steamship lines on both sides of the isthmus produced timetables; these allowed me to calculate how much faster it would have been to take the railroad than to sail around Cape Horn. Once I had basic points for every steamship route and an estimate of time saved, I imported my data into Carto. The resulting maps attest to Panama's incredibly fortuitous location--and the world's growing recognition of it.
I live right next to Albuquerque's Highlands District, so I definitely pay attention to the eating opportunities there; I also pay attention to the fact that barely anyone uses the neighborhood before 9 am or after 5 pm. Armed with a basic architectural history of the neighborhood and some city directories dating back to the 1930s, I identified some businesses conducive to neighborhood life (groceries, restaurants) and others more conducive to vice and/or transience (liquor stores, motels). Paradoxically, motels were by far the longest-tenured establishments in the area. Grocery stores, on the other hand, rarely cropped up--and when they did, they didn't stay long. Even though the Highlands District showed signs of becoming another prosperous postwar suburb, the composition of its businesses speaks to the difficulties of maintaining a neighborhood feel in an area catering to motorists that are just passing through.
I started by checking city directories every ten years (1949-1999). Once I had a spreadsheet of businesses and addresses for each of my six benchmark years, I wrote a Python script to turn street addresses into longitude/latitude data. Once I color-coded each type of business, I had six distinct snapshots of the Highland District's business composition. Next I aggregated my spreadsheets, using the mapping engine Palladio to show the relative tenure of each business (as demonstrated by the size of their respective dots).