Can an Algorithm Cure Gerrymandering?

Mapping congressional districts functions at a dynamic intersection of geography, politics, and community. Can technology, as with so many other issues at this intersection, improve the current system?
June 9, 2014, 10am PDT | James Brasuell | @CasualBrasuell
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"Brian Olson is a software engineer in Massachusetts who wrote a program to draw 'optimally compact' equal-population congressional districts in each state, based on 2010 census data. Olson's algorithm draws districts that respect the boundaries of census blocks, which are the smallest geographic units used by the Census Bureau. This ensures that the district boundaries reflect actual neighborhoods."

The article shares a few examples of the difference between Olson's "optimally compact" districts and the gerrymandered reality of congressional districts.

An interesting question raised by speculating on a new system for districting, is the question of "communities of interest": "As Jonathan Bernstein wrote last year, a community of interest could be defined based on rural/urban divides, shared cultural background, economic interest, ethnic background, demographic similarity, political boundaries, geographic boundaries and on and on." In other words, "You can define a 'community of interest' pretty much however you want," which introduces the kind legalistic arguments that can enable, you know, gerrymandering.

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Published on Tuesday, June 3, 2014 in The Washington Post - Wonkblog
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