Last week, I worked to create a network of non-white judges. i theorized that Federal Judges would come from the same networks of schools, often historically black Colleges and Universities, due to increased segregated schooling in the US South. I was incorrect as there were few networks and over 200 different schools represented among the 368 Judges.
This week, I decided to map the birthplaces and schools of Federal non-white judges to determine if additional patterns might arise.
For best results, I’d recommend zooming into the map or maximizing it in a new window and viewing the United States or particular US regions (though internationally born judges do provide some interesting data points). I simply added the birth place columns (City, State) and School. I gave these a spectrum of colors (5 ranges) to indicate change over time by birth year. Colleges received red colors and birth places yellow shades to distinguish them from one another.
I expect the higher proportion of African American judges born in the South would mirror population concentrations and likewise for Hispanic Judges born in Texas and California. One interesting fact exposed by the map is the great distance that many judges seemed to travel between birth place and school. It would be interesting to see if the same held for white Judges.
Unfortunately, Google doesn’t provide this type of functionality beyond manually calculating distance for each judge.
Mapping the judges was incredibly easy, because I’d done the work last week to clean up the data first. I simply loaded the CSV into Google and played with the settings for a few minutes, chose the light political basemap to highlight the data: Presto! A decent map.
But what makes this easy- the lack of options, also creates some mapping problems. The college networks are further de-emphasized because each college receives only one “pin”, even though Howard, for example, has at least 12 Federal Judge alumni. Birth places suffered from the same issue (e.g. New York, NY babies). Using a method to “weight” the number of alumni or hometowns would provide a more accurate representation of Judge patterns.
The schools data was also more suspect than the birth place data. I’m unsure why Brown University, for example, is located over the University of Virginia. Why Google placed Jersey State College over State College, Pennsylvania is more obvious, but difficult to fix without manually changing the data.
Ultimately, Google provides an easy and effective method when the data exists and is in the proper format. A project for Clio 3 also illustrates the benefits of previously clean data and building on the work of others.
I’m attempting to analyze and possibly map Decisions of the Indian Claims Commission. Fortunately, there are some fantastic geographers who have come before me and geo-rectified Charles Royce’s maps (1899). Charles Royce created maps of Indian land title ceded to the Federal government Nicole Smith first created the georectified maps, sharing them as shapefiles. Matthew McCarthy, a George Mason University Geography Dept graduate student, expanded upon her work, adding significant geographic analysis. His maps also includes some helpful features like present-day reservations. My map is currently far more primitive (and inaccurate in some ways), though I hope it will provide the bones on which future textual analysis might rest:
Using more powerful tools (open source QGIS), I’ve created a custom map that is not as “pretty” as Google’s, but will provide a more robust platform for future Work. And with good data (provided by kind souls), it’s already effective in showing patterns of Western migration and Indian land cession.