Description:

JUNE MEETING: Tuesday, June 29, 2021 at 5:30pm (US Central) via Zoom and Gather

DATA SCIENCE NIGHTS are monthly hack nights on popular data science topics, organized by Northwestern University graduate students and scholars. Aspiring, beginning, and advanced data scientists are welcome!

AGENDA:

5:15: Welcome to Data Science Nights via Zoom
* Zoom Link: https://northwestern.zoom.us/j/99588631168
5:30: Presentation by Juandalyn Burke, University of Washington
6:00: Hacking session via Gather
* Gather link: https://gather.town/app/UCTJAHOgQi2FLx4O/DSN

SPEAKER: Juandalyn Burke, Ph.D. Candidate, Biomedical Informatics and Medical Education Department, University of Washington

TOPIC: Using an Ecological Inference Software Tool to Detect Vote Dilution 

The most basic characteristic of a democratic system is the right to vote. The Voting Rights Act (VRA) of 1965 was established to ensure fair voting practices were enacted and that elected officials were representative of the community they served. The VRA prohibits unfair and discriminatory voting practices, including racially polarized voting and vote dilution, based on the race or an individual’s association with minority language groups.  However, in the United States, violations of the VRA are difficult to prove because information on race and ethnicity is not collected in the voting process.  By definition, racially polarized voting occurs when distinct racial or ethnic groups vote divergently to elect their separate candidates of choice. Vote dilution occurs when the racial majority group votes to block the minority group from electing their preferred candidate. The eiCompare software package detects both racially polarized voting and vote dilution by inferring the race or ethnicity of the voters in a population using several methods of ecological inference. We improved and added features to the eiCompare package including: geocoding, more accurate procedures in detecting the race of voters, better visualization of ecological inference outcomes, parallel processing, and analysis of historical voting data. We think these new features will allow for better detection of racially polarized voting and vote dilution and will help to support evidence presented in voting rights litigation.

ADDITIONAL HACKING SESSION INFO

For anyone interested in building and analyzing networks, Jenny Liu will be at the "hack" sessions with code related to networks. The goal will be to go through some basic exercises from a book, then move onto reproducing the results of some papers.

For more info: data-science-nights.org

Supporting Groups:

This event is supported by the Northwestern Institute for Complex Systems and the Northwestern Data Science Initiative.