Searching for a Gerrymandering Antidote
Mentor 1
Matthew Petering
Start Date
1-5-2020 12:00 AM
Description
Gerrymandering is an issue that is relevant to every American citizen, regardless of an individual’s party preference. When election maps are gerrymandered, one side is given an unfair political advantage by the manipulation of district boundary lines. For example, in the 2018 midterm elections, Democratic candidates for Wisconsin State Assembly received 53% of all votes cast but only ended up winning 36% of the seats. On the other hand, in the same year Republican candidates for U.S. Congress in Maryland received 32.5% of all votes cast but only won 12.5%—one of eight—of Maryland’s U.S. Congressional districts. These and other cases of extreme gerrymandering have become serious enough to be argued before the U.S. Supreme Court. The primary goal of this project is to develop a three-step process for creating fair political districts without bias. In step 1, political and demographic data is obtained from online sources and then checked for correctness. In step 2, a computer algorithm coded in C++ automatically assigns small geographic units called wards to political districts. In step 3, the ArcGIS mapping software program is used to visualize the resulting assignments. This presentation focuses on steps 1 and 3 of the above process. We discuss where the data for this project was found and how it was checked. We then describe how maps were created to visualize the proposed district plans created by the computer algorithm. Several maps for Wisconsin’s eight U.S. Congressional Districts are proposed, and the advantages and disadvantages of each map are discussed in detail.
Searching for a Gerrymandering Antidote
Gerrymandering is an issue that is relevant to every American citizen, regardless of an individual’s party preference. When election maps are gerrymandered, one side is given an unfair political advantage by the manipulation of district boundary lines. For example, in the 2018 midterm elections, Democratic candidates for Wisconsin State Assembly received 53% of all votes cast but only ended up winning 36% of the seats. On the other hand, in the same year Republican candidates for U.S. Congress in Maryland received 32.5% of all votes cast but only won 12.5%—one of eight—of Maryland’s U.S. Congressional districts. These and other cases of extreme gerrymandering have become serious enough to be argued before the U.S. Supreme Court. The primary goal of this project is to develop a three-step process for creating fair political districts without bias. In step 1, political and demographic data is obtained from online sources and then checked for correctness. In step 2, a computer algorithm coded in C++ automatically assigns small geographic units called wards to political districts. In step 3, the ArcGIS mapping software program is used to visualize the resulting assignments. This presentation focuses on steps 1 and 3 of the above process. We discuss where the data for this project was found and how it was checked. We then describe how maps were created to visualize the proposed district plans created by the computer algorithm. Several maps for Wisconsin’s eight U.S. Congressional Districts are proposed, and the advantages and disadvantages of each map are discussed in detail.
