Gerrymandering

Project Summary

Sophie Guo, Math/PoliSci major, Bridget Dou, ECE/CompSci major, Sachet Bangia, Econ/CompSci major, and Christy Vaughn spent ten weeks studying different procedures for drawing congressional boundaries, and quantifying the effects of these procedures on the fairness of actual election results.

Themes and Categories
Year
2015
Contact
Jonathan Mattingly
mathematics
jonm@math.duke.edu

Project Results

There has already been research done with North Carolina districts, described in http://today.duke.edu/2014/10/mathofredistricting. There, Jonathan Mattingly and Christy Vaughn showed that randomly re-drawing district boundaries would have dramatically changed election results. This summer's team extended the analysis to many more states, and found that states with independent election commissions (like Iowa) had statistically fairer results than states with very partisan districting systems (like Maryland).

L-R: Christy Vaughn; Sachet Bangia; Sophie Guo; Bridget Dou. Hard at work in SSRI.

Download the executive summary (PDF).

See Quantifying Gerrymandering, a website developed by Sachet Bangia, for more details about the project.

Gerrymandering work now posted on Arvix: https://arxiv.org/abs/1704.03360

Disciplines Involved

  • Political Science
  • Mathematics

Project Team

Undergraduates: Sophie Guo, Bridget Dou, and Sachet Bangia

Faculty Lead: Jonathan Mattingly, Professor, Mathematics

Graduate student mentor: Christy Vaughn, Program in Applied and Computational Mathematics, Princeton

Additional information

Relatively Prime podcast on the project:

Related People

Related Projects

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here:

 

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16th & 17th century maps & atlases of London and Lisbon. Over the course of the summer, the Mapping History team has developed its own unique analytical dataset by painstakingly labelling every element contained within these maps, used python to digitize this dataset, and, now in the projects final stage, has begun the process of reconstructing these historical perspectives in a 3D game engine.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

 

Watch the team's final presentation (on Zoom) below: