Yesterday was so fun, but hectic! I gave a talk on http://redistrict.science [preprint: https://arxiv.org/abs/1711.04640 ] and it was also coincidentally #GISday. I was focussing on how I did what I did using Javascript, Cython, C, Python, GIS, etc. Today I'd like to talk about the science.
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In bottom row, you see the very sorry state (pun intended
) North Carolina is in! Charlotte (c) is cracked so much, reaching so far into the countryside. our solution shows how 2 urban districts for Charlotte make sense.
More: http://redistrict.science http://arxiv.org/abs/1711.04640 pic.twitter.com/tTqSxbRhCJ
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Another
in Utah. West Valley & Salt Lake Cities (a) are cracked so hard the district goes all the way to other end of state! Same with Provo! Our solution shows how you can fairly give the cities districts of their own.
More: http://redistrict.science http://arxiv.org/abs/1711.04640 pic.twitter.com/FEIqgpQirx
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A really fun thing happens in Arizona (d)! it's horrible in (c) but in our solution you get a
district because our algorithm really wants to give Tuscon its own district! In real world it's cracked 3 ways! Three!
More: http://redistrict.science http://arxiv.org/abs/1711.04640 pic.twitter.com/wl2n2ZPUGP
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Finally, some states really are good.
Omaha, Nebraska has it's own district (e) and then increasingly suburban areas have theires, and then rural theirs. Which is THE SAME as our algo (f) did just more smoothly.
More: http://redistrict.science http://arxiv.org/abs/1711.04640 pic.twitter.com/5UNEsay5KC
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Now I'd like to talk about the algo: I ran our version of k-means on Census data to http://redistrict.science each state.
@DrLoveBC came up with a type of k-means that weights each cluster/district by its population. https://arxiv.org/abs/1711.04640#MachineLearning#ArtificialIntelligencepic.twitter.com/lqk6DxqE4X
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I kind of question this. The Iowa system only looks in any way wonky because the state prioritizes maintaining continuity of existing political boundaries (counties, and cities and townships at the state district level). Not really better, just different priorities.
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Good and fair point. Agreed.
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I’m kind of curious how your algorithm handles the creation of minority majority districts as required by the Civil Rights Act? Do you have partisan modeling using prior cycle elections? Seems like prioritizing urban grouping is going to create D districts with huge
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@DrLoveBC and address this in the Discussion section of the preprint: https://arxiv.org/abs/1711.04640 -
that should have been "and I"
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Love GIS so thanks for posting. In what way do you see Iowa's improved? Specifically, why is it better to put Ames with Des Moines?
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Only under the definition of improvement as we use it in the preprint. https://arxiv.org/abs/1711.04640
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