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During the summer of 2020, I studied critically engaged geography and electoral districting in a team of students and research mentors. After identifying variables that could signal potential communities of interest we implemented the machine learning MAPPER algorithm identify those communities in Charlotte, North Carolina to combat gerrymandering.
Motivation
Motivation
Gerrymandering is a method of drawing political boundaries such that a particular party has an overt advantage in voting power or breaking up communities of interest such to undermine their voting power. 2020 is a census year so electoral district lines will once again be redrawn.
Intention
Intention
Determine whether the machine learning MAPPER algorithm can be used to identify communities of interest to combat gerrymandering.
Process
Process
Currently I'm working on a contribution to a paper detailing our work.
Electoral Districting
Electoral Districting
Redistricting Criteria
Many states employ the criteria outlined below, and there are a number of states that have adopted criteria beyond the following:
compactness: minimizing the distance between all parts of the constituency
contiguity: all parts of the district are connected
preservation of county and political subdivisions: not crossing county or city boundaries
preservation of communities of interest: districts that preserve communities with shared interests or experiences affected by legislation
avoiding pairing incumbents: avoid districts that would create elections with incumbents in contest
There is an inherent tension in defining communities of interest and other redistricting criteria, since there are not necessarily geographically clustered.
What is gerrymandering?
Gerrymandering on the basis of race is a violation of the Voting Rights act and 14th Amendment. However, partisan gerrymandering continues to persist, as there is still no widely recognized metric to evaluate the degree of gerrymandering by the courts.
It is characterized by:
Cracking: fragmenting populations to keep districts majority
Stacking: combining concentrated populations within larger majority
Packing: concentrating folks in as few districts as possible
Why Charlotte, North Carolina?
We chose to work with Mecklenburg County since it has a strong history of gerrymandering with a number of recent court cases at both the state and federal level. Charlotte is the county seat, urban center, and has a diverse population in a variety of senses.
Data
Data
In order to capture the populations historically marginalized through gerrmandering and would qualify as communities of interest we considered a number of variables and data sets that would highlight communities that are underrepresented in Census data. However, the data that can be considered when drawing electoral district lines is very narrow, thus we focused our analysis on Census block group data, and sought out variables that could indicate communities of interest.
Thank you
Thank you
Dr. Jim Thatcher, Dr. Courtney Thatcher, Alisha Husain, Anthony Kolshorn, and the past REU participants and mentors that contributed to this project.
The University of Puget Sound, University of Washington Tacoma, and the National Science Foundation (NSF)
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