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o_guest's profile
Olivia Guest | Ολίβια Γκεστ
Olivia Guest | Ολίβια Γκεστ
Olivia Guest | Ολίβια Γκεστ
@o_guest

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Olivia Guest | Ολίβια Γκεστ

@o_guest

• goth gremlin • computational cognitive/neuroscience modeling • geek & techish Cypriot • plant aficionada • came up with #bropenscience • http://neuroplausible.com  •

Τότεναμ, Λονδίνο & Cyprus
olivia.science
Joined October 2015

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    1. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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      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.

      3 replies 7 retweets 26 likes
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    2. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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      What we found is that not all gerrymandering is purposeful. It arises because states are hard to redistrict, especially larger states. The more districts a state has the more it is likely to be gerrymandered. [maps: http://redistrict.science  — preprint: https://arxiv.org/abs/1711.04640  ]pic.twitter.com/MgRWq0rwWj

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    3. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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      The redder a state is the more our method improvesd it. As you can see from the map (and based on analyses we did), bigger/more populous states are redder. Interactive maps can be found at: http://redistrict.science  and the preprint: https://arxiv.org/abs/1711.04640  has many more details.pic.twitter.com/AeESSkcidx

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    4. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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      Take top row [taken from the preprint https://arxiv.org/abs/1711.04640 ], that's Iowa (a), our algorithm (b) improves on it, even tho they are trying their best. That's the thing! Even when it's done by impartial committees, computers still district better! More: http://redistrict.science pic.twitter.com/1oYxiAMbMK

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    5. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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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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    6. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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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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    7. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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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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      Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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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

      6:32 AM - 16 Nov 2017
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        1. Olivia Guest | Ολίβια Γκεστ‏ @o_guest 16 Nov 2017
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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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