This is one of the maps I generated for my project. It demonstrates that, in the 1960s, most church members lived in predominantly white areas and many lived outside of the city of Philadelphia.
In my junior paper, I explored how the racial demographics of a Philadelphia church transformed from exclusively White to predominantly Black within a few short years. I started this project in the archive, looking for any documents that could point to when and how the church’s congregation began to transform. In minutes recorded of church meetings, I identified a period of white flight, where the neighborhood around the church became increasingly non-White, and, in response, many of the church’s White members relocated to the suburbs. I knew that I could describe this congregational migration in a written narrative, but I also wanted my readers to be able to visualize it, so I turned to digital mapping.
A figure of national research stations in Antarctica, which I recently created for my research using the Python library Cartopy.
Ever since I was a child, I’ve always loved maps—I was a major geography nerd growing up. Jumping forward to today, my like-minded roommates are just as obsessed as I am: the walls of our dorm are literally covered floor to ceiling with maps. These include (but are not limited to) a glaciological map of Antarctica, public transport maps of numerous cities (Toulouse, Christchurch, and New York are just some examples), and a road map of my home state of Washington!
Maps aren’t just a fun hobby: They’re also enormously important in numerous research fields (in addition, of course, to just being plain useful). Whether your research field of interest is history or meteorology or epidemiology, there’s a good chance that you’ll be reading—and making!—some maps. In my own field of glaciology, maps are of paramount importance, whether it’s a map of glacier melt contribution from southeast Alaska or a map of Antarctic ice core sites. I’ve written this guide to provide some helpful resources and tools for making maps for your research, so hopefully it will serve as a good starting point! I should note that this isn’t a tutorial, but plenty of great tutorials should exist on the Internet for all of these tools.
Last year, Princeton announced a plan to expand the University by 2026, adding another residential college and building new athletic facilities on the south side of Lake Carnegie. In what ways will this latest expansion transform our campus, and how does that change fit with the university’s historic land use? These are the questions that my twin brother, GEO senior Benjy Getraer, set out to answer last year in a class project for GEO 90 “Analyzing Ecological Integrity: An Assessment of Princeton’s Natural Areas.”
To address these types of large-scale research objectives or answer smaller questions such as Benjy’s, you can use Geographic Information System (GIS) software to display, edit, and analyze geospatial data. Spatial analysis provides a unique way to study data and add diversity to figures and data visualization. In this post I will introduce basic concepts of geospatial data and one application of GIS analysis by walking through Benjy’s project, mapping land use change on Princeton’s campus.
Benjy Getraer ’19 analyzed historical imagery of Princeton’s campus to track land use change over the past 75+ years. The first step was to align the original overhead photography by geo-referencing the raster images.
