What’s the Big Deal about Big Data? New Data Methods Combat Gerrymandering


Gerrymandering – the process of remapping voting districts to aid those currently in power —  has been an American political tradition since 1812 until the present day. This month, for example, the U.S. Supreme Court heard testimony on two gerrymandering cases which allegedly deprived black voters in two Southern states of their full electoral impact.

“This is a really troubling area, a hugely troubling area,” said political scientist Gary King. “It is the most conflictual form of politics in the United States, the most predictable form of conflictual politics in the United States, short of violence … It produces a train wreck of litigation, and on cue.”

King is also the director of Harvard’s Institute for Quantitative Social Science, and wearing that hat he describes – in a talk drawn from a presentation  King made this summer on Capitol Hill to an audience filled with policymakers and fellow academics – how computational social science attempts to reduce some of this friction.

In this sixth video drawn from a talk, he discusses how data scientists tackled some of the outstanding issues involved in creating voting districts, such as determining what the fairest ground rules are and figuring out how people –in particular minority communities — voted despite the secrecy of the ballot.

And as he has explained throughout this series, the solution didn’t lie in the data but in the innovative analysis of that data. In the process, social scientists came up with standards for fairness (mostly) accepted by all the stakeholders and with techniques for estimating voter behavior. To learn more about this timely subject, watch the video below:

King’s talk, “The big deal about big data,” was hosted by SAGE Publishing with co-sponsors the American Political Science Association and the American Statistical Association.

Videos in the series

  1. Ziyad Marar On The Opportunities That Big Data Provides Social Scientists
  2. What Makes Big Data Valuable?
  3. Examples: Exciting Data That Is Useless Without Analytics
  4. Example: Social Scientists Determine Cause Of Death At A Distance
  5. Example: Analysis Rids Social Security Forecasts Of Bias
  6. Example: New Data Methods Combat Gerrymandering
  7. Example: Big Data Much Better Than People At Determining Keywords
  8. Example: Watching Chinese Citizens Get Around Censorship
  9. Example: Learning The Real Reason For Chinese Censorship
  10. The Spectacular Success Of Quantitative Social Science
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