Digital History

September 13, 2020 – Module Three: QGIS

Our QGIS tutorial began with data from Early Modern England, with two datasets related to populations in London, and information regarding parishes and the plague. Playing around with this data was relatively easy and more fun, in my opinion, especially with the visual aspect and Dr. Otis’s tutorial that felt clearer than last week (though, that was more likely related to my disdain for Open Refine and my usual scope of being asleep by 8:30 then anything to do with the tutorials.)

Working with vector layers, adding delimited text, and joining layers to create a visual map with named layers to narrate an argument or story intrigued me, especially because of my love of other mapping tools I have used before. I pulled up the data regarding elections in 2014 Fairfax County in the 10th District and viewed different columns and rows. Because of the plethora of different information in the dataset, including the name of the precinct, the winning party, total ballots, ballots cast in person, and the number of votes and its percentage for those on the ballot, the options for creating a number of visual maps that relayed different information became possible.

The first thing I did, of course, was upload the SHP and CSV files and viewed the attribute table, trying to decide which information I wanted to play with and display first. The resulting map tells very little, especially given all the information displayed in the attribute table from the CSV file.

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Says nothing – and defaulted to a horrific color!

The first label I created labeled the winning party in each precinct. Using Dr. Otis’s tutorial, I quickly pulled up layer properties and messed around with both the color of the map, the labels, and the symbology.

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Team literally any other color!

An improvement, I would argue, having the name of the winning politician on top of the precinct in the map, but it was messy, and it would be easy to get confused with the differing victors being shown in the same color. Time to do more.

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Now we can start to see what is going on in the 2014 election and that Republican candidate Barbara J. Comstock won most of the precincts in the 10th District. However, more information regarding population and number of votes in different precincts would further clarify if Comstock got enough votes to win the seat.

Going back into Layer Properties and then Symbology, I changed “Categorized” to “Graduated” and the value from “WINPARTY” to “TOTBALLOTS” and then pressed ‘classify’ to get the values.

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The numbers presented make sense because of the data, but aren’t very attractive or even for the legend. Also, I decided to change the color ramp since I already used red prior and wanted something separate from the earlier layers.

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Disliking the gray scale and realizing it did not help with visualizing the data or making it attractive, I went to change it in Layer Properties once more. I changed it to green, since some of the other color ramps still seemed too dark since I was relying on having the name of the winner on the precinct since I was displaying other data with the colors.

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Green is my favorite color so I’m sticking with it!
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Comstock won the one precinct with less than 500 votes.
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Comstock and Foust each won six districts with between 500 – 1000 votes cast.
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For precincts with between 1000 – 1500 votes cast, Comstock one thirteen and Foust won only two, pushing her into a clear lead at this layer.
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At this layer, we see Comstock still leading, with precincts with 1500-2000 votes cast, Comstock won twelve and Foust again only taking two precincts.
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This last layer has the least precincts but the most votes, with between 2000 – 2584 cast. With only six precincts in this layer, we see Comstock won four and Foust only two.

By looking at each layer to see who won where and viewing the range of votes cast in each precinct, the viewer can see that Barbara J. Comstock, the Republican candidate, took most of the precincts including the ones with larger voter turnout. Fact-checking my assertion that Comstock won the 10th Congressional District election for the House of Representatives seat in 2014, we can see that this QGIS map helps visualize the data of Comstock winning as well as voter turnout.

Playing around with this dataset and the map, including creating some layers and visuals that I found to be less-than-helpful or just unattractive at times, helped me see what QGIS could do and how it could create a map that could be used in articles or other historical projects to supplement arguments and help with narratives. Though there would be no issue with using QGIS by itself, I agree with Dr. Grunewald’s assertion in her AHA article “BEYOND THE ARCHIVE: What GIS Mapping Reveals about German POWs in Soviet Russia” that “Digital mapping works best in conjunction with more traditional sources. It can be crucial to making research conclusions, but not without the context and evidence of the standard building blocks of historical studies. Indeed, GIS mapping and the traditional methods and sources of history are symbiotic.”

The map I created was a good visual about the 2014 elections and could be useful for historical research related to local elections, party affiliation, voter turnout, and potentially other projects. But with less context or a larger project, it is hard for the map to be immensely helpful on its own. I will say that I plan on using QGIS for my own research related to female preachers in the early republic. I have previously used StoryMaps for both mapping for my thesis and the beginnings of a DH project I have created on my own, but I think that QGIS could help with mapping different aspects of my research and visualizing it for an article or more.

3 replies on “September 13, 2020 – Module Three: QGIS”

Caroline! This blog is amazing, and now is bookmarked so that I can go back and check my work! You were so successful in your work with QGIS, well done! And I also agree with Grunewald’s assertion that GIS mapping is most successful when the researcher utilizes both new and traditional sources. It seems important to constantly compare the two, and make sure all of one’s sources line up in a way that reflects the history. The way I have thought about maps in relation to the larger research project is that it is a piece of the puzzle, but could really be a corner edge piece! I think the challenges that I could see arising are issues with super messy data that doesn’t translate well to this format (cue Open Refine!) or issues with incorrect data that if left unchecked, could potentially lead a researcher down an incredibly wrong path.

You did amazing work with QGIS and this is such a helpful tutorial with all of your visuals! I am so jealous of how easy you found it, as well as how much fun you appeared to have while working with it. I agree with Grunewald’s quote on how maps are the most effective when used alongside more traditional methods of research. I think this has a lot to do with how the people who create maps are able to pick and choose what they want to be seen. If you decided to change how the names were displayed and choosing different colors for the maps it is conveying a message. It also has to do with the possibility of putting in information in wrong. As a visual to accompany the research, rather than the main source, it adds to an argument rather than creating a new one.

This is so neat! You created a really nice display of your process and it’s a great tutorial! I kinda just went through the motions and when I finally got a result that showed readable data, I gave up. I can definitely see where I can go back and really pull out the data to make it more readable. I am eager to go and back to see what I can do QGIS for other projects. I agree with you about the quote from Grunewald. This type of mapping of data is best used in conjunction with other research and traditional sources. It is a great tool for visualizing the data to draw conclusions from or display the complexities of an argument.

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