Congressional Redistricting Algorithm
Start Date
August 2024
End Date
August 2024
Location
ALT 206
Abstract
Gerrymandering is the idea of purposely drawing political maps in a specific way to favor a certain individual or party. It has been around since the early eighteen hundreds and despite its extremely controversial nature, it remains present in our current-day politics. In an effort to combat gerrymandering, this project attempts to create an algorithm that can mathematically produce ideal voting districts. The algorithm runs on four factors, which are fairness, competitiveness, equal population, and compactness. These factors are adjusted to create one score where each map is graded. The algorithm follows the ideas of evolution and natural selection. Maps are generated with small random mutations, and if favorable, will move on to the next generation of maps where they have the opportunity to mix with other favorable maps. This project has been in the works for many years and a large part of the work we did this summer was updating the algorithm to current day standards. Over the past few years, the coding software we use, R, has fundamentally changed the way mapping and spatial data function. We also worked to make the maps more automated as well as bringing in more current data.
Congressional Redistricting Algorithm
ALT 206
Gerrymandering is the idea of purposely drawing political maps in a specific way to favor a certain individual or party. It has been around since the early eighteen hundreds and despite its extremely controversial nature, it remains present in our current-day politics. In an effort to combat gerrymandering, this project attempts to create an algorithm that can mathematically produce ideal voting districts. The algorithm runs on four factors, which are fairness, competitiveness, equal population, and compactness. These factors are adjusted to create one score where each map is graded. The algorithm follows the ideas of evolution and natural selection. Maps are generated with small random mutations, and if favorable, will move on to the next generation of maps where they have the opportunity to mix with other favorable maps. This project has been in the works for many years and a large part of the work we did this summer was updating the algorithm to current day standards. Over the past few years, the coding software we use, R, has fundamentally changed the way mapping and spatial data function. We also worked to make the maps more automated as well as bringing in more current data.
