2014 election data
2010 redistricting cycle
This project began when we wanted to know more about the use of gerrymandering in the United States. Our interest and subsequent exploration seems timely given the amount coverage gerrymandering has received in the news recently—this is clearly an important issue that many people are concerned about, and that many more people should be made aware of.
After researching, we took a quantitative approach. We collected, visualized, and analyzed publicly available datasets to test our hypotheses about how political parties are able to gain what looks like an unfair advantage in Congress, given the voter demographics of some states.
Is gerrymandering an issue for our congressional districts? Do you feel that your congressperson represents your views?
We hope you take the time to explore these questions and decide for yourself. You can continue our analysis from our SVDS Github repository.
Please contact us with questions and comments at email@example.com!
Metric of how tightly the area of a shape is packed into its boundary. Less compact is more squiggly. The most compact possible shape is a perfect circle.
The Cook Partisan Voting Index (PVI) measures how strongly a district leans towards the Democratic or Republican party compared to the nation as a whole.
Proportion of votes for Democratic or Republican candidates.
CFscore from the Database on Ideology, Money in Politics, and Elections (DIME), a score on a liberal to conservative spectrum based on the ideological stance of financial contributors to a representative’s campaign.
Shapefiles from the US Census were used for calculating compactness.
2012 popular vote data collected from The Green Papers.
2014 popular vote data collected from this spreadsheet linked on the Wikipedia page for the 2014 House of Representatives elections.
Blending both industrial and academic research, Tatsiana is an expert at solving hard business problems. She brings a background in both mathematics and statistics, and has deep experience researching and implementing models for predicting user behavior.
With a background in cognitive psychology and neuroscience, Matt has extensive experience in hypothesis testing and the analysis of complex datasets. He is excited about using predictive models and other statistical methods to solve real-world problems.
Excited about data visualization and design, Susie is experienced as a front end developer and has spent recent years researching data visualization. She draws from her degrees in art and engineering to bring a well-rounded perspective to data science.
With over 15 years in advanced analytical applications and architecture, John is dedicated to helping organizations become more data-driven. He combines deep expertise in analytics and data science with business acumen and dynamic engineering leadership.