Determining our fates with cold, apathetic, wonderful math

June 12, 2011 9:19 pm Published by 1 Comment

USsplitLine

Minnesota is currently debating how to redraw our state district borders, as we do every 10 years, with new census data to guide us. Creating fair, impartial boundaries seems impossible, because it’s always tempting for the party in power to gerrymander everything to give themselves more power. I’ve long been critical of the shape of our 6th district, home of state shame Michele Bachmann. It conveniently bends around liberally-leaning downtown areas and grabs many of the richer, fiscally-conservative parts of the state and merges them with rural, socially-conservative parts of the state.

But how the hell do you draw fair, unbiased district boundaries? On the one hand, people don’t want their communities split down the middle. On the other, it’s really easy to lump certain communities together to create districts that are easy for one party or the other to control.

Here’s one way that’s pretty interesting: math.

The first method I found uses a “shortest splitline algorithm” (whatever that is) to slice each state into simple shapes, based on population numbers. I really like it. It’s cold, it goes right through communities, but it seems fair.

The second method I found is also pretty cool, but seems to be a bit less heartless about chopping communities into pieces. The borders it draws are much more complex, but it’s hard to argue with the results.

Our Republican-controlled legislature passed their own hellish redistricting plans, but naturally it was vetoed by our Democrat governor. Big surprise. Of course, the same thing would’ve happened with a Democratic legislature and Republican governor. (If they were all Republicans, it would’ve easily passed. And if they were all Democrats, they’d still be arguing about it now.) Apparently, the courts will decide what to do.

I have no expectation that we’ll start using science-based methods any time soon, but I think it’s pretty interesting to see what’s possible with math I’ll never understand.

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This post was written by Bevans

1 Comment

  • There’s a half-way point between using cold-hard math and drawing the boundaries wherever you feel like. It’s the method that we use in our work with school districts to redraw elementary school boundaries. There is first an initial profile that’s drawn up by census block, which is essentially a group of parcels lumped together as the smallest unit of aggregation of census data available to the public. They usually respect natural boundaries, like freeways, rivers, lakes, railroad tracks, etc. and city boundaries. The size of the census block is determined by population density; the more dense the population, the smaller the block.

    Now we look at these blocks and group them together into “natural neighborhoods” looking at groups of housing developments, highways, schools, business/commercial areas and large features like water, golf courses, airports and the like. We then get a profile of each of those natural neighborhoods for race, gender, age, household income and home value (for schools we include acheivement scores, grade levels and f/r lunch).

    To make the boundaries we use various scenario modelling techniques to draw boundary lines around the natural neighborhoods so as to create the most even and equal profile that is possible. If applied to politics it would mean creating district boundaries that, for the most part, had similar numbers for ethnic diversity, income, and age as the districts surrounding it. This means that some of the suburban districts would have parts that extend into the metro, some rural districts would have parts that extend into suburbs, but a greater equality would be maintained and it would be an anti-gerrymandering process that would hopefully yield election results that are more closely representative of Minnesotans, because Michelle Bachmann ain’t it. She makes the whole state look bad and I’m tired of her dragging my state through the mud for her bad politics and personal ambitions.

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