An introduction to the project
The aim of this project is to develop insight and an understanding of the various approaches - from modern mathematics and computer science - being used to detect partisan gerrymandering and why doing so is imperative for the future of democratic systems around the world.
Gerrymandering is a long-standing political phenomena that individuals, organizations, and/or parties abuse for an unfair advantage over the other. It’s just like any tool in the book. However, in my opinion, the ramifications of gerrymandering run far deeper than other political maneuvers used to gain a seat here or there.
Ultimately, this project is an effort to understand & answer the following questions:
1. What are the current methods being used to detect partisan gerrymandering? How effective are they?
2. Why is there even a need to detect gerrymandering in the first place? What’s the big deal?
3. What impact does gerrymandering have in the US today?
An important disclaimer/clarification to make before getting into the other sections of this project is that this work will be focused on partisan gerrymandering in the United States. The reason for this is twofold:
a. The United States just completed the 2020 Census which is essentially the process of counting how many people are present in the country and where. This is a procedure that occurs once every decade and determines the allocation of funds and resources to communities in America and informs where schools, hospitals, and roads etc. need to be built in the country. Every census is followed by a phase of redistricting where lawmakers redraw/readjust their maps based on the results of the census. This is where the bulk of partisan gerrymandering occurs and the phase of redistricting is about to start very soon in the United States, making this study extremely relevant to the political discourse in this country at the time of writing.
b. DHSS is based in the United States and I am also studying at Lafayette, which is in the United States, so it makes logical sense to focus on the data available here and the implications it has on the places around me/DHSS/Lafayette as a whole. This does not change anything in the grand scheme of things because gerrymandering isn’t unique to the United States and detection methods can be applied in any country since they are just mathematical and/or computational models and algorithms. In other words: the lessons learned here and the conclusions made can be applied universally. What this does is that it enables me to narrow the scope of my project to a reasonable degree and let’s me work with data/case studies that are readily accessible to me and applicable to scenarios around me.