When deciding what to do for my atlas, I knew I wanted to do something concrete with palpable real-world impacts. As someone who loves books—and for a class on literature and the city—I know how important it is just for my sanity, let alone my economic opportunities, to be able to read and write, so I decided to focus on literacy in Chicago. It is a subject I am familiar with because I volunteer with my sorority’s Read > Lead > Achieve initiative that focuses on tutoring and donating books as a means of increasing literacy rates, but I knew very little about what the Chicago-specific impacts of literacy were and how it varied from neighborhood to neighborhood. Throughout the quarter our readings also reinforced my desire to do this for my atlas project. I do not think there was one reading that made me pick this topic over any other; it was more so the act of reading, enjoying, and discussing literature that made me want to focus on literacy. Though, The Jungle stands out to me as a story that is relevant to the disparities in Chicago, so literacy, especially literacy in the English language, seemed like an obvious choice from there. That text did an excellent job of highlighting how inequality is perpetuated over time and how even being a native English speaker, along with being able to read and write in English, can make a huge difference in an individual’s prospects.
Thus, digging into the ward-by-ward data on literacy rates seemed like a great idea until I realized that such data did not exist. After that I was stuck in a rut and was not sure how to proceed because I had all of these ideas about how I wanted to look at literacy in comparison to other issues to see if there were any strong correlations. Emailing different literacy non-profits in Chicago ended up solving the problem after Literacy Works emailed me back and pointed me in the direction of Chicago Public Schools’ test scores. While not a perfect solution, the test scores gave me information on what percentages of students were preforming at or above the national average for over five hundred schools. Turning this into usable data was even more difficult than finding it in the first place as it involved a length process of finding address and coordinates for schools, then compiling that with the test data, an—though I looked over the final product for any mistakes—the sheer amount of data means that there could be inaccuracies simply due to human error. Further, with this new data I also had to change what I was comparing it to as I realized the information I had on graduation rates was nowhere near as complete or up to date as my information on reading success in standardized testing. I ended up cutting graduation rates along with declines in property value as they were both incomplete and not up to date, which would have led to inaccurate comparisons to the other data. I also realized that the data on the declines in property value did not give me the information I was hoping for (the actual taxable property value that would have gone to funding public schools) and was not the kind of direct comparison that would have been helpful for this project. Having the information on graduation would have been helpful, though, and it is disappointing that I did not have a complete and up-to-date data set for that.
The data that did end up being the most helpful to me was the average household income by area because of the direct correlation between wages and literacy rates. This is part of the central problem with low literacy rates, as I discuss in the ArcGIS story, because children in low-income homes are less likely to have access to books, which lowers their chances of being literate, and if they are not literate, they will have lower paying job and be more likely to face unemployment. Further, parents who are not literate are more likely to have kids who are not literate, making it exceedingly difficult to escape this cycle. This is the part that most reminded me of our discussion of The Jungle and how Sinclair illustrates how it is almost impossible to escape poverty. For the atlas project, it also helped that the income data mapped well with the test-score data, demonstrating the correlation between the two in a very clear manner.