NEXT DATA SCIENCE NIGHT ONLINE: JUNE 29

5.15 p.m.  -   Data Science Night Welcoming

5.30 p.m. -  Presentation of Juandalyn Burke - PhD Candidate (ABD) at the University of Washington 

                             Using an Ecological Inference Software Tool to Detect Vote Dilution

Zoom link to join the webinar: 

https://northwestern.zoom.us/j/99588631168
Meeting ID: 995 8863 1168

6.00 p.m -  Hacking session on Gather Town
Link to join Gather.  You will need to use a Chrome or Firefox window. A password is necessary to join and will be provided at the end of the webinar.
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Juandalyn Burke,
PhD Candidate (ABD) at the University of Washington

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. ​
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