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Algorithmic Congressional Redistricting

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Algorithmic Congressional Redistricting 1 Algorithmic Congressional Redistricting This thesis has been approved by The Honors Tutorial College and the Department of Mathematics __________________________ Dr. Razvan Bunescu Associate Professor, Electrical Engineering and Computer Science Thesis Adviser ___________________________ Dr. Alexei Davydov Director of Studies, Mathematics ___________________________ Cary Roberts Frith Interim Dean, Honors Tutorial College 2 ALGORITHMIC CONGRESSIONAL REDISTRICTING ____________________________________ A Thesis Presented to The Honors Tutorial College Ohio University ____________________________________ In Partial Fulfillment of the Requirements for Graduation from the Honors Tutorial College with the degree of Bachelor of Science in Mathematics ____________________________________ By Gareth A. Whaley April 2019 3 Table of Contents Abstract 4 0: Introduction 4 1: What is Gerrymandering? 7 History of Gerrymandering 7 Types of Gerrymandering 8 2: Metrics: What Are We Looking For? 12 Compactness 13 Fairness 16 Racial Gerrymandering 19 Other Metrics 22 The Fitness Function 23 3: Algorithmic Solutions 26 The Shortest-Splitline Algorithm 26 BDistricting 28 Evolutionary Methods 30 FairVote and Proportional Representation 33 Software Solutions 36 4: Case Study: Ohio 41 6: Conclusion 51 7: Bibliography 53 4 Abstract In this thesis I explore the ways in which computational methods can be applied to solve the problem of congressional redistricting. This involves a brief overview of the historical and legal context behind gerrymandering and an in-depth analysis of the different types of metrics that answers the question “What makes a good border proposal?” I additionally conduct a survey of the array of algorithmic redistricting solutions that are currently available, and an analysis of the different ways in which algorithmic solutions can be applied for Ohio specifically. I arrive at three main conclusions: first, that Ohio’s current congressional borders as they currently stand are very obviously gerrymandered; second, that using multi-member districts diminishes the tradeoff between compactness and fairness that exists in the single-member districting process, and third, that the process of algorithmic redistricting is not inherently unbiased – subjective decisions and prioritizations must always be made throughout the process, and bad-faith actors can still use objective methods to achieve biased outcomes. 0: Introduction Over the past few decades, the methods by which we draw the boundary lines that determine congressional districts have had certain vulnerabilities exposed. The process of gerrymandering, in which congressional boundaries are intentionally drawn in ways that lead to a specific desired outcome, has been recognized as a serious issue in our current electoral system and a tool that can be used to subvert the fundamental assumptions of representative democracy. There are a wide variety of ways that this can be applied, to achieve any number of goals, but it is generally understood that the practice of 5 gerrymandering has a generally negative effect on the structure of our government; by concentrating the power to redraw congressional boundaries in the hands of small, potentially biased committees, the general principles of democracy – principles of a government of the people, by the people, and for the people – are threatened. This immediately raises the question – what countermeasures exist? If the current system of congressional redistricting is flawed, what preferred alternatives can we implement? A number of proposals have been offered, but this thesis takes a look at one of the most interesting, unbiased, and potentially viable options – algorithmic redistricting. The field of algorithmic congressional redistricting seeks to use quantitative methods to arrive at a border proposal that would serve as a usable, unbiased alternative. By developing metrics that can be used to determine the extent to which a proposal has desirable quantities – measures of compactness, electoral fairness, racial representation, etc. – the process of redistricting can be framed as an optimization problem, and modern methods can then be applied to generate a proposal that best satisfies the selected criteria. The original aim of this thesis was to develop a piece of software that could apply these algorithmic methods automatically, and, given user input, could prioritize certain metrics over others. It would then generate a border proposal that optimally satisfies the constraints posed by the user – this could be used as a toolkit in future research to automatically generate and analyze border proposals. In the process of developing this toolkit, however, I discovered a different program that overshadowed and, frankly, functionally eclipsed the initially proposed project. Obviously, this made necessary a reexamination of the scope of the project – instead of developing a tool, I had the 6 opportunity to use this tool for further analysis of the options presented by the process of algorithmic redistricting. In section 1, “What is Gerrymandering?”, I will cover the origins of the practice of gerrymandering, and the ways in which its historical context informs today’s debate. Section 2 will look at the metrics that are used to assess a border proposal, answering the fundamental question of what properties we look for in the optimization process. The next section, “Algorithmic Solutions,” looks at the best methods that have been developed to date – this includes a review of the relevant literature on the topic. In the “Case Study: Ohio” section of this thesis, I will share my findings – ways in which a variety of algorithms from the previous chapter can be applied specifically to the state of Ohio, and the different properties of the proposals that they produce. The fifth section, “The Redistricting Process,” looks at the redistricting process from an institutional standpoint; essentially, it serves as a brief survey of the obstacles – some legal, some political – that must be overcome for these results to be applied in our government. The mathematical methods and elements used in this thesis are as follows: 1. Discrete optimization – using computational methods to optimize a complex system of multiple constraints. 2. Geospatial analysis – application and assessment of metrics of compactness, electoral fairness, and others on geographic data. 3. Analysis of statistical data – predicting and evaluating trends and outliers in aggregated data. 4. Evolutionary computing – specifically, the assessment and implementation of genetic algorithms to solve optimization problems. 7 1: What is Gerrymandering? History of Gerrymandering The term “gerrymander” originates from a Massachusetts redistricting bill signed in 1812 by Governor Eldridge Gerry. In the proposal, a bizarrely-shaped district was drawn in a way that gave Gerry’s party, the Democratic-Republicans, three congressional seats out of five when previously it had had none. A political cartoon, noting that the Figure 1.1: The "Gerry-Mander" - a bizarrely-shaped district that wrapped around the state of Massachusetts, shaped like a salamander. district was shaped like a salamander, dubbed it the “Gerry-Mander,” and the word has 8 since come to mean any set of congressional districts that is drawn in order to favor one party over another.1 Another historical instance of gerrymandering occurred after the Voting Rights Act of 1965, when some states redrew their borders in ways that created “majority- minority” districts that clustered together large groups of nonwhite voters. This was seen as “affirmative gerrymandering,” but was ruled unconstitutional in the 1996 Supreme Court Case Bush v. Vera (for more information, see “Racial Gerrymandering” in section 3). In the past couple of decades, and especially following the state redistricting bills passed in 2010, there has been major public outcry in response to districts that have been gerrymandered in an especially heinous manner. Following a number of high-profile supreme court cases, and the backing of certain high-profile activists like former Governor of California Arnold Schwarzenegger, anti-gerrymandering efforts have very recently become one of the most important and most popular forms of electoral reform. Redistricting reform has been advocated for on a national level, but state initiatives such as the passage of Issue 2 in Ohio in May 2018 have also been very successful in pushing for a new solution. Types of Gerrymandering The most common kind of gerrymandering is known as “cracking and packing,” a strategy which refers to the joint process of separating voters of a similar party to 1 Barasch. Emily. “The Twisted History of Gerrymandering in American Politics.” The Atlantic, 2012. URL https://www.theatlantic.com/politics/archive/2012/09/the-twisted-history-of-gerrymandering-in- american-politics/262369/ 9 diminish their influence, and of “packing” them tightly into a smaller number of districts. This is also one of the most easily-detected forms of gerrymandering, but it is nevertheless a serious hindrance to fair elections.2 Figure 1.2: An illustration of the ways in which different districting methods can lead to very different outcomes. In addition, small-scale methods such as “hijacking” and “kidnapping” target candidates’ homes instead of larger electoral groups. For example, Ohio’s 9th district, sometimes referred to as the “mistake on the lake,” was redrawn in 2010 to “hijack” Democratic Representative Marcy Kaptur’s district by
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