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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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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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.
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