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  Redistricting with 2020 Population Estimates (and 2016/2018 Political Data)
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Author Topic: Redistricting with 2020 Population Estimates (and 2016/2018 Political Data)  (Read 3678 times)
muon2
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« Reply #25 on: June 24, 2018, 02:42:58 pm »

The wild card is population loss - your method implies that if a county is losing population, it will lose a greater absolute # of people per year. That in turn implies, at least if one considers no in-migration, that a greater percentage of the existing population must be dying or moving away every year. But who knows, that could actually be the more accurate way to model some places like Detroit and to a lesser extent Cleveland.

In a county of declining population, the losses would diminish each year with my method. For example, consider a county with a 10%/year loss rate that starts with 1000 people. In the first year it would lose 100 people bringing it to 900. In the next year it would lose 90 people bringing it to 810. In the third year it would lose 81, and so on. The absolute number lost decreases each year.

Most population changes in nature are based on exponential change, which is what I model for my projections. It turns out that since financial functions require the same sort of exponential change, it's easy to set up my model on a spreadsheet and update each year as new estimates come out.

It's worth noting that some projection models only use a three (or two) year average rate of increase and project it to the end of the decade. In particular when the press reports the likely gains and losses for congressional seats each Dec, they are typically using a projection based on 2 or 3 prior years.
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« Reply #26 on: June 24, 2018, 03:20:11 pm »

In a county of declining population, the losses would diminish each year with my method. For example, consider a county with a 10%/year loss rate that starts with 1000 people. In the first year it would lose 100 people bringing it to 900. In the next year it would lose 90 people bringing it to 810. In the third year it would lose 81, and so on. The absolute number lost decreases each year.

Got it. Then your method is probably better for population loss to be sure. With Georgia, one problem that I had is that for some racial subgroups (mainly Native Americans and Asian, and Other) was that in some cases, I was getting negative numbers. This could happen in counties where there was a very small population and then the population projections had it declining significantly. This happened mainly given the inaccuracy/high margin of error for estimates of population loss/growth for very small populations. In those cases I simply set the population for that particular racial subgroup to a minimum of 0. But your method would, I think prevent that from happening, so I may try that for a future state, in particular if I try to make race data projections again.
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jimrtex
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« Reply #27 on: June 24, 2018, 03:39:19 pm »

(1+x)n approaches 1+nx as x and n approach zero. Estimates toward the end of the decade in effect are equal to projections since n is small.

Maybe there could be a least square weighted fit where the more recent estimates are weighted more. Calculate both an exponential and linear fit and take the mean of the two (arithmetic, geometric, and harmonic) and use that.
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jimrtex
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« Reply #28 on: June 24, 2018, 03:40:07 pm »

In a county of declining population, the losses would diminish each year with my method. For example, consider a county with a 10%/year loss rate that starts with 1000 people. In the first year it would lose 100 people bringing it to 900. In the next year it would lose 90 people bringing it to 810. In the third year it would lose 81, and so on. The absolute number lost decreases each year.

Got it. Then your method is probably better for population loss to be sure. With Georgia, one problem that I had is that for some racial subgroups (mainly Native Americans and Asian, and Other) was that in some cases, I was getting negative numbers. This could happen in counties where there was a very small population and then the population projections had it declining significantly. This happened mainly given the inaccuracy/high margin of error for estimates of population loss/growth for very small populations. In those cases I simply set the population for that particular racial subgroup to a minimum of 0. But your method would, I think prevent that from happening, so I may try that for a future state, in particular if I try to make race data projections again.
Since the Census Bureau produces racial estimate, why not use those directly?
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« Reply #29 on: June 24, 2018, 04:02:28 pm »

In a county of declining population, the losses would diminish each year with my method. For example, consider a county with a 10%/year loss rate that starts with 1000 people. In the first year it would lose 100 people bringing it to 900. In the next year it would lose 90 people bringing it to 810. In the third year it would lose 81, and so on. The absolute number lost decreases each year.

Got it. Then your method is probably better for population loss to be sure. With Georgia, one problem that I had is that for some racial subgroups (mainly Native Americans and Asian, and Other) was that in some cases, I was getting negative numbers. This could happen in counties where there was a very small population and then the population projections had it declining significantly. This happened mainly given the inaccuracy/high margin of error for estimates of population loss/growth for very small populations. In those cases I simply set the population for that particular racial subgroup to a minimum of 0. But your method would, I think prevent that from happening, so I may try that for a future state, in particular if I try to make race data projections again.
Since the Census Bureau produces racial estimate, why not use those directly?

I am using the census race estimates as the basis for projections, but I am not quite sure what you mean by using them "directly." My guess is that maybe you mean something like "why not just take the most recent race estimates from the American Community Survey and project them forward to 2020?"

If so, the reason for not doing that is that I was worried about the higher sampling error of race data as opposed to total population estimates. The other issue is that the American Community Survey data comes either as 5 year average estimates, 3 year estimates, or 1 year estimates, with the more recent estimates reflecting more recent population changes, but having higher error because they are based on fewer survey responses.

So my presumption was that the total population estimates for each county would generally have less measurement error. And secondly, my presumption was that it would probably reduce overall error by more if I used 5 year average (2012-2016) ACS data, to reduce the variance of race data, particularly for small counties, as opposed to using the most recent ACS 1-year race estimates in combination with the most recent ACS total population estimates. I would think this is especially important for getting somewhat accurate reads on the smaller racial groups (Asians, Native American, Other, etc), because in some years there may be literally 0 people who participate in the ACS in certain counties. I think I even remember from a few years ago seeing some cases where ACS doesn't provide estimates at all for certain geographies if the population is too small.

Of course, that could be wrong (and surely is, in at least some cases). In particular in very large counties (for example, Los Angeles County, CA), if there is a sufficient sample size in the 1 year ACS estimates, it would probably be more accurate to use the most recent 1 year estimates rather than jerry-rigging 2012-2016 race estimates onto 2017 total population estimates.
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« Reply #30 on: June 24, 2018, 04:28:56 pm »

i feel like you should project populations using a more recent time interval like 2014-2017 or 2015-2017 instead of 2011-2017 (north dakota projections for example would be drastically different)
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jimrtex
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« Reply #31 on: June 25, 2018, 07:55:53 pm »

In a county of declining population, the losses would diminish each year with my method. For example, consider a county with a 10%/year loss rate that starts with 1000 people. In the first year it would lose 100 people bringing it to 900. In the next year it would lose 90 people bringing it to 810. In the third year it would lose 81, and so on. The absolute number lost decreases each year.

Got it. Then your method is probably better for population loss to be sure. With Georgia, one problem that I had is that for some racial subgroups (mainly Native Americans and Asian, and Other) was that in some cases, I was getting negative numbers. This could happen in counties where there was a very small population and then the population projections had it declining significantly. This happened mainly given the inaccuracy/high margin of error for estimates of population loss/growth for very small populations. In those cases I simply set the population for that particular racial subgroup to a minimum of 0. But your method would, I think prevent that from happening, so I may try that for a future state, in particular if I try to make race data projections again.
Since the Census Bureau produces racial estimate, why not use those directly?

I am using the census race estimates as the basis for projections, but I am not quite sure what you mean by using them "directly." My guess is that maybe you mean something like "why not just take the most recent race estimates from the American Community Survey and project them forward to 2020?"
I assumed you used the Census Estimates, and not the ACS.

The Census provides annual estimates for counties, including breakdowns for race, ethnicity (Hispanic), and separately for age. It does not provide combined populations by race and age.

But you could project 2020 populations based on race and age (VAP). Then assume that the ratio of VAP/total is constant for each race to get projected VAP by race, and correct these totals to match the projected total VAP.
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muon2
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« Reply #32 on: June 26, 2018, 07:17:34 am »

i feel like you should project populations using a more recent time interval like 2014-2017 or 2015-2017 instead of 2011-2017 (north dakota projections for example would be drastically different)

As I noted above there are some professional organizations that use a 3-year growth pattern to forecast the next decade. I did something like that in the prior decade to compensate for the effects of Katrina on LA and TX and avoid 2005. Ideally one wants enough years to get a good average without glitch years, but not so many that it misses real trends that are different from those at the last Census.
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« Reply #33 on: June 30, 2018, 11:12:18 am »

I found one small bug in the Virginia population estimates. In the city of Bedford both precincts have no population.
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muon2
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« Reply #34 on: July 05, 2018, 08:46:02 am »

I found one small bug in the Virginia population estimates. In the city of Bedford both precincts have no population.

The independent city of Bedford VA was reabsorbed into the county in 2013. The city had estimates made in 2011 and 2012, but not after 2013. However the base population for Bedford county was increased to reflect the added population from the city.
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« Reply #35 on: July 08, 2018, 02:58:28 pm »

Alright, next up is Texas. It turns out that the Texas state government has its own population projections for 2020, which include race data by county. Although these estimates were apparently made in 2014, so they may not quite include the very latest changes in population trends, it saves a lot of work to just use those. And if they are good enough for the Texas state government to use in their own planning, they are good enough for my purposes.

https://www.dshs.texas.gov/chs/popdat/st2020.shtm


The only thing previously holding back Republican gerrymanders of Texas from being more lopsided than the current map is was the Voting Rights Act. However, with the retirement of Kennedy and Kennedy's failure to do anything about partisan gerrymandering, it is my presumption that the Supreme Court with Trump's new nominee will allow Texas to basically ignore the VRA (and perhaps even declare that its application to redistricting is unconstitutional) and redistrict for partisan purposes however they want. Moreover, Texas Republicans have not been known for restraining themselves in redistricting.

Therefore I would not expect the voting rights act to stand as a limit on Republican gerrymandering any longer. The result of this is likely to be a highly effective and partisan map similar to the current North Carolina and Ohio maps, which entirely disregards all other considerations besides the partisan rigging Congressional of elections.

There is no prospect I can see of either a court drawn map/neutral map or a Democratic gerrymander. So I drew a Republican gerrymander using this 2020 data, presuming that Texas gains 2 Congressional districts (so TX will have 38 districts). The map is a 35-3 Republican map, in which all 35 Republican districts are extremely safe (about R+11.5 each). The purpose of this map is to institute apartheid in Texas:









TX-1 (R+11.0): 40.2% White, 8.1% Black, 41.1% Hispanic, 37.3% Obama '08, 62.7% McCain '08
TX-2 (R+11.1): 48.7% White, 21.2% Black, 23.9% Hispanic, 43.4% Obama '08, 56.6% McCain '08
TX-3 (R+11.8 ): 51.3% White, 11.1% Black, 19% Hispanic, 39.1% Obama '08, 60.9% McCain '08
TX-4 (R+11.8 ): 44.1% White, 15.8% Black, 35.1% Hispanic, 40.6% Obama '08, 59.4% McCain '08
TX-5 (R+11.7): 47.7% White, 7.9% Black, 33.5% Hispanic, 39.6% Obama '08, 60.4% McCain '08
TX-6 (R+11.7): 48.4% White, 15.5% Black, 27.3% Hispanic, 41.1% Obama '08, 58.9% McCain '08
TX-7 (R+11.2): 35.8% White, 17.2% Black, 31.4% Hispanic, 40% Obama '08, 60% McCain '08
TX-8 (R+11.9): 48.4% White, 12% Black, 26.6% Hispanic, 38.8% Obama '08, 61.2% McCain '08
TX-9 (R+11.1): 44.3% White, 12.8% Black, 30.7% Hispanic, 40% Obama '08, 60% McCain '08
TX-10 (R+11.7): 40.9% White, 3.1% Black, 50.8% Hispanic, 41.7% Obama '08, 58.3% McCain '08
TX-11 (R+11.1): 33.8% White, 11.9% Black, 41.7% Hispanic, 38.6% Obama '08, 61.4% McCain '08
TX-12 (R+11.7): 40.1% White, 13.5% Black, 41.5% Hispanic, 40.4% Obama '08, 59.6% McCain '08
TX-13 (R+11.5): 25.9% White, 3.7% Black, 68.1% Hispanic, 38.4% Obama '08, 61.6% McCain '08
TX-14 (R+11.4): 33.2% White, 4.3% Black, 59.1% Hispanic, 41.7% Obama '08, 58.3% McCain '08
TX-15 (R+11.3): 31.9% White, 4.6% Black, 59.9% Hispanic, 41.7% Obama '08, 58.3% McCain '08
TX-16 (R+11.4): 33.1% White, 3.5% Black, 60.3% Hispanic, 40.8% Obama '08, 59.2% McCain '08
TX-17 (R+11.6): 63.8% White, 3.4% Black, 25.1% Hispanic, 41.4% Obama '08, 58.6% McCain '08
TX-18 (D+36.9): 8.6% White, 50.4% Black, 35.6% Hispanic, 88.5% Obama '08, 11.5% McCain '08
TX-19 (R+11.5): 32.8% White, 4.2% Black, 58.7% Hispanic, 39.3% Obama '08, 60.7% McCain '08
TX-20 (R+11.4): 36.9% White, 4.4% Black, 54.5% Hispanic, 41.1% Obama '08, 58.9% McCain '08
TX-21 (R+11.6): 51.5% White, 12.2% Black, 27% Hispanic, 42.4% Obama '08, 57.6% McCain '08
TX-22 (R+11.0): 39.3% White, 13.9% Black, 26% Hispanic, 38.7% Obama '08, 61.3% McCain '08
TX-23 (R+11.6): 39.7% White, 9.3% Black, 47.2% Hispanic, 42.3% Obama '08, 57.7% McCain '08
TX-24 (R+11.9): 46.1% White, 11% Black, 29.6% Hispanic, 39% Obama '08, 61% McCain '08
TX-25 (R+11.6): 44.4% White, 14.6% Black, 36.4% Hispanic, 42% Obama '08, 58% McCain '08
TX-26 (R+11.7): 46.5% White, 9.5% Black, 37.2% Hispanic, 41.3% Obama '08, 58.7% McCain '08
TX-27 (R+11.5): 34.1% White, 5.8% Black, 55.9% Hispanic, 41.2% Obama '08, 58.8% McCain '08
TX-28 (R+11.5): 32.5% White, 3.4% Black, 61.9% Hispanic, 40.7% Obama '08, 59.3% McCain '08
TX-29 (R+11.0): 30.8% White, 7.9% Black, 57.6% Hispanic, 39.4% Obama '08, 60.6% McCain '08
TX-30 (D+34.5): 9.9% White, 49.2% Black, 37.3% Hispanic, 85.1% Obama '08, 14.9% McCain '08
TX-31 (R+11.5): 50% White, 4.2% Black, 40.2% Hispanic, 40.3% Obama '08, 59.7% McCain '08
TX-32 (R+11.7): 48% White, 11.7% Black, 34.9% Hispanic, 42.3% Obama '08, 57.7% McCain '08
TX-33 (R+11.6): 44.6% White, 9.4% Black, 34.9% Hispanic, 40.1% Obama '08, 59.9% McCain '08
TX-34 (R+11.5): 30.4% White, 0.8% Black, 66.6% Hispanic, 42% Obama '08, 58% McCain '08
TX-35 (D+25.2): 41.1% White, 8.4% Black, 41.6% Hispanic, 74% Obama '08, 26% McCain '08
TX-36 (R+11.1): 37.2% White, 16.4% Black, 42.9% Hispanic, 41.1% Obama '08, 58.9% McCain '08
TX-37 (R+11.1): 34.5% White, 13.1% Black, 48.2% Hispanic, 39% Obama '08, 61% McCain '08
TX-38 (R+11.5): 31.9% White, 2% Black, 64% Hispanic, 39.4% Obama '08, 60.6% McCain '08

There is only one single Democratic district in Houston, only one single Democratic district in Dallas, and only one single Democratic district in Austin. Because Hispanic voter turnout is so low, it is more efficient in nakedly partisan terms to cede a safe D seat in Austin than one in San Antonio or the McAllen area. San Antonio, El Paso, and the rest of the Hispanic areas along the border are all cracked between multiple ~R+11.5 safe Republican districts.

The result of this is that the 2 Dallas/Houston seats will be dominated by Black Democratic voters, and the Austin seat will end up being dominated by White Democratic voters (although there is a substantial Hispanic population in Austin, turnout remains low). All the other 35 seats will be dominated by White Republican voters. No seats whatsoever will be dominated by Hispanic voters (despite the fact that Hispanics make up a plurality of the Texas population in these 2020 estimates).

I won't make any particular effort to distinguish which district is which, since it doesn't really matter - all the R districts are basically the same, about R+11.5, give or take. The lines are more erose than they really need to be for 2 reasons -

1) Because I tried to give all Republican districts roughly the same partisanship. If you are willing to accept some districts that are "only" R+10, R+9, etc, then you can make the lines more compact looking.

2) Because I tried to give all (or at least almost all) of the Republican districts some heavily R rural areas, not just suburbs. That way, if the suburbs swing D, the rural areas probably won't, and that helps keep the district safe R.



Is this a dummymander? I think you would be hard pressed to argue that it is. Even if Texas trends Democratic, all of these districts are significantly more Republican than Texas as a whole. The 3 Democratic pack districts are very "efficient" at removing any realistic prospect of any of the other districts ever having a remotely competitive election. Furthermore, if any districts do become remotely competitive, Texas Republicans can always redistrict mid-decade to make whatever adjustments are necessary to rig the elections in their favor. You could add another Dem pack district or two in San Antonio, El Paso, or McAllen-Brownsville, but this only has the effect or increasing the R+ of the other districts by about 1 point per additional vote sink because Hispanic turnout is so low. Why bother conceding low-turnout Hispanic vote sinks when you can just mid-decade redistrict if you feel the need to concede an additional vote sink?

Moreover, even in the seemingly unlikely event that the Supreme Court rules against a hyper-aggressive TX Republican gerrymander, the litigation process is likely to take several election cycles worth of time before it finally gets to SCOTUS, during which time the GOP will have successfully rigged the elections and sabotaged American democracy for probably at least half a decade.



My further conclusion (from a Dem perspective) based on this map is that if Democrats want to have any hope of controlling Congress in the 2020s, they had better hurry up and abolish California's Independent Redistricting Commission and pass a 53-0 Democratic Gerrymander there in order to offset the TX GOP gerrymander and other gerrymanders. This way, although the electoral system will still be rigged, it will at least be rigged symmetrically.
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« Reply #36 on: July 08, 2018, 03:27:13 pm »
« Edited: July 08, 2018, 03:35:09 pm by Solid4096 »

I think Texas would add more than 3 Democratic Packs, because they will probably target PVI values of R+10, and after the 2020 elections, 3 Packs will be insufficient to make everything else that high. I think most likely is that they will draw 4 or 5 Packs.
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« Reply #37 on: July 13, 2018, 07:22:58 am »

https://drive.google.com/file/d/147QKnQZqHFZAqKQoVyY88IBf4IPY8haR/view

I made my own Iowa population estimates.
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« Reply #38 on: July 13, 2018, 09:05:09 am »

Alright, next up is Texas. It turns out that the Texas state government has its own population projections for 2020, which include race data by county. Although these estimates were apparently made in 2014, so they may not quite include the very latest changes in population trends, it saves a lot of work to just use those. And if they are good enough for the Texas state government to use in their own planning, they are good enough for my purposes.

https://www.dshs.texas.gov/chs/popdat/st2020.shtm

2 in Houston
2 in Dallas
1 in Austin
2 in the Valley
1 in El Paso
1 in San Antonio

30 Republican seats.
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Skill and Chance
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« Reply #39 on: July 14, 2018, 03:30:30 pm »

Interesting that the Des Moines district is now the most promising IA seat for Dems on paper.
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« Reply #40 on: November 19, 2018, 12:34:19 am »
« Edited: November 19, 2018, 01:56:57 am by Queen Pelosi, Regina of the House, Regnant of Amerittania 👁 »

I am cross-posting this in this old thread, adding a link to the 2016 Clinton precinct estimates file for Dave's Redistricting App.

I am thinking of also doing the same thing of estimating 2016 or 2018 precinct data for some other states potentially (maybe Beto-Cruz TX-Sen and Abrams-Kemp GA-GOV precinct results estimates for TX and GA in particular, and maybe Clinton-Trump precinct estimates for NY).





Here a rudimentary 2020 map showing that you can still get two Blue seats out of downstate - as you can see I'm still working on Chicagoland. Both seats are between D+4 and D+5 PVI, and they can get more democratic. I wanted to keep my tentacles 'thick' and actually logical, but this adds pubs. If you connect the cites using thin bacon strips, you can hit D+5 or more.

A lot I feel depends on 2020 regarding Illinois. How democratic the collar counties get/stay/remain and whether IL-14 remains in dem hands determines if the seat needs to go Pub to shore up everyone else.

I made a 14-3 Illinois map a while back, but that was under the assumption of Hultgren holding on and mashing him up with Kinzinger. There were two snake-like downstate Democratic districts and 12 Chicagoland Hillary seats. All met VRA requirements and all were at least 55% Hillary (the 12 Chicagoland seats were all at least 58% Hillary). Could probably baconstrip out Chicagoland a little more for Lauren Underwood and try to get a 15-2.

It is very easy to draw a 14-3 for IL. The Democratic Party of Illinois should just go ahead and disband if they can't even manage to draw a 14-3 while Rs draw egregious maps in other states.

IL is a state where PVI including 2012 results is not really as informative as you would like. I don't think the suburban/rural trends are going away, and I think it sort of makes more sense to draw districts based on the 2016 vote more than PVI (many states are like this to some degree, but IL probably in particular).

I started trying to draw an IL map with 2016 PVI data, but it was too cumbersome and annoying to not have the Clinton-Trump numbers. So I made 2016 precinct result estimates for IL and used those instead as my basis for drawing districts. Since the only precinct results data directly accessible in the DRA files is unfortunately the 2008 results (PVI does not show up in the files that download to your computer, despite the fact that they obviously have it), so the 2016 estimates are made using 3 assumptions:

1) The distribution of turnout across precincts within each county was constant from 2008 to 2016 (basically, turnout increased by the same % in each precinct within the same county, but precincts within different counties went up by a different % than each other).
2) That within each county, each voter regardless of precinct had the same individual probability of switching from Obama to Trump, Obama to 3rd Party, or McCain to Clinton.

Estimating the precinct results is not as good as having the actual precinct results, obviously, but I think it is better than just having PVI. You can see there is a lot more deep red in rural downstate IL (which makes it easier to vote sink the Rs) and a lot more blue in suburban Chicagoland (which makes it much easier to draw Dem districts there). The numbers still display in the DRA interface as "President 2008," but you can see that the actual #s of votes match the 2016 results (Clinton getting 3.09 million votes and Trump getting 2.15 million, but the interface still displays them as "Oba" and "McC"):



I uploaded the estimates here in case anyone else wants to try using them here:

http://s000.tinyupload.com/index.php?file_id=31184931103049287168



It is very annoying how they put the PVI data in to Dave's Redistricting App, because first of all you can't see it visually on the map, and secondly because it doesn't show the actual underlying data. You know that they have to have both the 2012 and 2016 precinct results in order to calculate PVI, but you can't see the results separately.

For districts where there is a large trend (like in both rural and suburban IL), if you are trying to draw a map that will be resistant against a continuation of the trend, it is much more helpful if you can separately see the Obama-Romney and Clinton-Trump numbers than if you can only see them mashed together into PVI.

Using 2016 results actually seems to make IL much easier to gerrymander than it was in 2010 or than it was with even 2016 PVI, because with 2016 data, polarization is stronger and Rs can be more easily packed into rural vote sinks. In general higher polarization makes it easier to gerrymander effectively, whereas it is harder if every precinct is 55-45.



Anyway, so here is a 14-3 map, which has 2 downstate Dem districts drawn in the obvious way and 12 Chicagoland districts. All Democratic incumbents should live in their districts (or else very nearby, so that they could be easily drawn in), and I tried to preserve as much of their current territory as reasonably possible.









Here are the stats for the districts. This includes the (estimated) Clinton 2016 vote, the PVI that averages 2016 and 2012, and as normal, and . I drew the districts basically entirely on the basis of the estimated Clinton vote. Since the 2016 data is just an estimate and is based on the geographical distribution of Dem votes within each county from 2008, the relationship between the estimated Clinton vote and PVI is not exact. In general it should be possible to draw districts with a similar Clinton vote as in this map, but the PVIs may be a bit different and the actual precincts include may differ a bit because of the fact that Dems have in reality gained more or lost more in certain areas within each county than others. I basically kept the inner Chicago area districts about the same as they are now, although if it were me making the map in reality I would probably want to add a second Hispanic district. But the 2020 population estimates (I am using the ones cvparty made) seem to underestimate the Hispanic population a bit as compared to current #s. If the Clinton vote on any of the suburban Chicago districts is not considered safe enough, it is pretty easy to gerrymander more because there are plenty of extra votes in Chicago, particularly in IL-05 and IL-09.

IL-01: Clinton 71.1% - Trump 24.1%, (D+21.7) [[Obama 77.4% - McCain 21.9%]], Majority African American
IL-02: Clinton 72.3% - Trump 23.1%, (D+24.4) [[Obama 77.3% - McCain 21.9%]], Majority African American
IL-03: Clinton 57.8% - Trump 38.4%, (D+4.6) [[Obama 57.4% - McCain 41.4%]]
IL-04: Clinton 79.6% - Trump 15.6%, (D+34.0) [[Obama 81.8% - McCain 16.9%]], Supermajority Hispanic
IL-05: Clinton 69.1% - Trump 26.1%, (D+19.6) [[Obama 70.1% - McCain 28.6%]]
IL-06: Clinton 55.1% - Trump 39.3%, (D+3.8 ) [[Obama 55.5% - McCain 43.2%]]
IL-07: Clinton 84.7% - Trump 10.8%, (D+36.1) [[Obama 87.5.5% - McCain 11.7%]], Majority African American
IL-08: Clinton 55.0% - Trump 39.8%, (D+3.1) [[Obama 57.5% - McCain 41.2%]]
IL-09: Clinton 64.6% - Trump 31.5%, (D+13.9) [[Obama 64.6% - McCain 34.2%]]
IL-10: Clinton 55.2% - Trump 38.8%, (D+4.5) [[Obama 58.8% - McCain 40.0%]]
IL-11: Clinton 54.7% - Trump 39.4%, (D+6.2) [[Obama 59.1% - McCain 39.8%]]
IL-12: Clinton 28.4% - Trump 67.2%, (R+17.6) [[Obama 44.8% - McCain 53.4%]]
IL-13: Clinton 50.3% - Trump 43.4%, (D+5.2) [[Obama 61.5% - McCain 36.9%]]
IL-14: Clinton 52.9% - Trump 40.8%, (D+3.8 ) [[Obama 58.3% - McCain 40.3%]]
IL-15: Clinton 27.0% - Trump 68.1%, (R+20.0) [[Obama 42.7% - McCain 55.6%]]
IL-16: Clinton 36.0% - Trump 58.1%, (R+11.0) [[Obama 47.4% - McCain 50.9%]]
IL-17: Clinton 49.5% - Trump 43.8%, (D+4.4) [[Obama 59.7% - McCain 38.7%]]



So I think this shows it is pretty easy to draw a 14-3 map. However, I think this most obvious way is probably not the *best* way to do so. Mainly this is because IL-13 and IL-17 both only voted for Clinton by about 6-7 points, and are vulnerable to potentially trending further Republican if Republicans continue to gain among white voters outside of major urban/suburban areas.

So, here is an alternative that tries to avoid that being a problem.

IL-13 is made significantly more Democratic by giving it the pick of Democratic precincts in Bloomington and Carbondale, in addition to East St. Louis, Springfield, Decatur, and Champaign which it already had in the previous map.

And IL-17 is sured up by drawing it into Chicagoland - specifically by dropping Peoria and Bloomington and instead drawing it in to Waukegan. Why Waukegan specifically? First, it is a pretty easy straight shot along the IL border from Rockford. And secondly, Waukegan is filled with lots and lots of non-whites. With the 2020 estimates from cvparty (I think they may be underestimates on the racial #s), that drops IL-17 all the way from to only 68% white, compared to the current real life IL-17 which is 83% white. So that makes Cheri Bustos far far less vulnerable to potential further GOP gains among white voters. Alternatively, you could maybe draw IL-17 into other areas with Dem voters like De Kalb, Elgin, or Auraura to achieve a similar effect. But I wanted to keep most of those for IL-14.

Since IL-17 no longer has Peoria, that leaves it up for grabs. Who gets it? In this case, IL-14. Springfield does actually have a decently sized African American population, and the way IL-14 is drawn in this map brings it down to 60% White and up to 9% African American. Since the real life current version of IL-14 is 86% White and only 3% African American, and since Lauren Underwood is African American, although she is clearly able to win over white voters, she probably won't complain too much about being given Peoria and bumping up the African American population a tad. Another way to look at it is that it gives her some downstate exposure to set her up for a statewide run later on if she wants to give that a try at some point.










IL-01: Clinton 71.1% - Trump 24.1%, (D+21.7) [[Obama 77.1% - McCain 22.1%]], Majority African American
IL-02: Clinton 72.3% - Trump 23.1%, (D+24.4) [[Obama 77.7% - McCain 21.4%]], Majority African American
IL-03: Clinton 57.8% - Trump 38.4%, (D+4.6 ) [[Obama 57.4% - McCain 41.4%]]
IL-04: Clinton 79.6% - Trump 15.6%, (D+34.0) [[Obama 81.8% - McCain 16.9%]], Supermajority Hispanic
IL-05: Clinton 69.1% - Trump 26.1%, (D+19.6 ) [[Obama 70.1% - McCain 28.6%]]
IL-06: Clinton 55.1% - Trump 39.3%, (D+3.8) [[Obama 55.5% - McCain 43.2%]]
IL-07: Clinton 84.7% - Trump 10.8%, (D+36.1) [[Obama 87.5.5% - McCain 11.7%]], Majority African American
IL-08: Clinton 54.2% - Trump 40.9%, (D+2.7) [[Obama 56.7% - McCain 41.9%]]
IL-09: Clinton 63.3% - Trump 32.7%, (D+12.4) [[Obama 63.4% - McCain 35.5%]]
IL-10: Clinton 54.0% - Trump 40.2%, (D+3.5) [[Obama 57.9% - McCain 40.9%]]
IL-11: Clinton 53.7% - Trump 40.4%, (D+5.0) [[Obama 58.2% - McCain 40.6%]]
IL-12: Clinton 29.0% - Trump 66.5%, (R+17.1) [[Obama 44.9% - McCain 53.3%]]
IL-13: Clinton 53.1% - Trump 40.0%, (D+7.8) [[Obama 63.5% - McCain 34.8%]]
IL-14: Clinton 53.5% - Trump 39.8%, (D+5.8) [[Obama 60.2% - McCain 38.3%]]
IL-15: Clinton 29.0% - Trump 65.8%, (R+19.0) [[Obama 42.3% - McCain 56.0%]]
IL-16: Clinton 32.8% - Trump 61.5%, (R+12.6) [[Obama 47.3% - McCain 51.0%]]
IL-17: Clinton 51.7% - Trump 42.1%, (D+4.9) [[Obama 60.7% - McCain 37.8%]]


If any of the Chicagoland districts are not considered to be safe enough, it is pretty easy to make them all safer by giving IL-09 some more of Lake County, etc. But as long as Dems continue to do well with suburban voters in Chicagoland, and the trend in the Trump era is not reversed, these districts should all be pretty safely Dem for the foreseeable future.



So, since 14-3 is clearly quite easy to do, what about 15-2? For that, it seems like you have to baconmander quite a bit more. I haven't played around with it enough to figure out the best way of doing it, but it is definitely possible. It is (probably) ugly though. Here is a partial attempt I made at 15-2 (which also adds a 2nd Hispanic district and draws Lipinski out to Peoria), but I gave up on it since it was both clear that it was possible to get to 15-2 but also clear that it would be ugly. But maybe someone can come up with a cleaner looking way of doing it. I also had to use touch point contiguity to have IL-15 cross through the tentacle of IL-12 that connects East St. Louis and Springfield:

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