Championing fair election maps with open data, law, and math: The Princeton Gerrymandering Project
when
October 1, 2020
|
10:00 am
(PST)
Type
Voting districts, or precincts, are central to how US elections work and how representative the results are. But the process of drawing district boundaries can get messy - fast. The Princeton Gerrymandering Project is working to achieve fair voting districts for everyone through data science and by building tools to involve citizens in the redistricting process. In this talk, Hope and Indraneel will share the ‘Why’ and ‘How’ of the OpenPrecincts initiative - including ways that you can work with the open data they are creating.
You’ll learn how to...
Explore the why and how of effective elections maps with a special speaker series and map-making contest. Learn more.
About the Princeton Gerrymandering Project
The Princeton Gerrymandering Project uses data, law, and math to achieve fair representation through redistricting reform. The Project uses data to draw and analyze fair maps, and to gather data for the OpenPrecincts project. Using math the team analyzes gerrymandering, suggests fixes, and proposes new metrics for measuring and opposing unfair districting. The Project uses law to analyze policies, advocate against gerrymandering standards, and suggest new standards for states.
Hope Johnson, Data Scientist
Hope Johnson analyzes and writes about data. Her work focuses on ending gerrymandering and supporting equitable distributions of power and representation. Hope develops code and designs graphics to dig into partisan gerrymandering claims. Hope holds a B.A. from Macalester College, where she studied statistics, and she has worked as a data scientist and data journalist.
Indraneel Purohit, Product Lead
Indraneel likes to work on software where it intersects with maps, music, and government. He is the product manager and software engineer for the Princeton Gerrymandering Project. He works on the Project's various web properties, including OpenPrecincts. Before PGP, he worked in transportation data at SharedStreets and as a software engineer at AppNexus. He holds a BS in Computer Science from Rutgers University.
