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Redistricting analysis for all 50 U.S. states
https://alarm-redist.github.io/fifty-states/
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2010 South Carolina Congressional Districts #127

Closed Jfer09 closed 1 year ago

Jfer09 commented 2 years ago

Redistricting requirements

In South Carolina, districts must:

  1. be contiguous
  2. have equal populations
  3. be geographically compact
  4. preserve county and municipality boundaries as much as possible

Interpretation of requirements

We enforce a maximum population deviation of 0.5%.

Data Sources

Data for South Carolina comes from the ALARM Project's 2020 Redistricting Data Files.

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 6,000 districting plans for South Carolina. We impose a hinge constraint on the Black Voting Age Population so that it encourages district BVAP between 40 percent and 60 percent, but districts with BVAP of 30% or less are not penalized as much. We impose a municipality-split constraint to lower the number of municipality splits.

Validation

this one

Screen Shot 2022-06-25 at 9 21 39 PM 
R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby       pop_hisp 
     1.0040012      1.0105256      1.0004111      1.0140118      1.0031822      1.0199507 
     pop_white      pop_black       pop_aian      pop_asian       pop_nhpi      pop_other 
     1.0037479      1.0033236      1.0095031      1.0056256      1.0028691      0.9999751 
       pop_two       vap_hisp      vap_white      vap_black       vap_aian      vap_asian 
     1.0054153      1.0131019      1.0003802      1.0036146      1.0115376      1.0064758 
      vap_nhpi      vap_other        vap_two pre_16_dem_cli pre_16_rep_tru uss_16_dem_dix 
     1.0011768      1.0016542      1.0010121      1.0054428      1.0061057      1.0029835 
uss_16_rep_sco gov_18_dem_smi gov_18_rep_mcm atg_18_dem_ana atg_18_rep_wil sos_18_dem_whi 
     0.9998713      1.0108486      1.0058166      1.0099854      1.0042444      1.0137063 
sos_18_rep_ham pre_20_rep_tru pre_20_dem_bid uss_20_rep_gra uss_20_dem_har         arv_16 
     1.0053737      1.0016564      1.0046326      1.0005294      1.0049484      1.0012903 
        adv_16         arv_18         adv_18         arv_20         adv_20  county_splits 
     1.0050325      1.0051256      1.0124208      1.0009286      1.0049237      1.0160643 
   muni_splits            ndv            nrv        ndshare        e_dvs.x       pr_dem.x 
     1.0115901      1.0099238      1.0030191      1.0002831      1.0002762      1.0103265 
       e_dem.x        pbias.x         egap.x        e_dvs.y       pr_dem.y        e_dem.y 
     1.0023588      1.0111069      1.0025052      1.0002762      1.0103265      1.0023588 
       pbias.y         egap.y 
     1.0111069      1.0025052 

Sampling diagnostics for SMC run 1 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,906 (96.9%)     12.8%        0.27 1,900 (100%)      8 
Split 2     2,787 (92.9%)     18.6%        0.41 1,840 ( 97%)      5 
Split 3     2,671 (89.0%)     20.2%        0.47 1,854 ( 98%)      4 
Split 4     2,502 (83.4%)     23.1%        0.55 1,768 ( 93%)      3 
Split 5     2,408 (80.3%)     25.5%        0.63 1,710 ( 90%)      2 
Split 6     2,283 (76.1%)      9.1%        0.65 1,512 ( 80%)      2 
Resample    1,139 (38.0%)       NA%        1.13 1,447 ( 76%)     NA 

Sampling diagnostics for SMC run 2 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,905 (96.8%)     11.3%        0.27 1,916 (101%)      9 
Split 2     2,783 (92.8%)     15.5%        0.39 1,864 ( 98%)      6 
Split 3     2,661 (88.7%)     21.4%        0.47 1,844 ( 97%)      4 
Split 4     2,535 (84.5%)     23.6%        0.57 1,807 ( 95%)      3 
Split 5     2,404 (80.1%)     25.3%        0.63 1,690 ( 89%)      2 
Split 6     2,304 (76.8%)      8.4%        0.63 1,476 ( 78%)      2 
Resample    1,245 (41.5%)       NA%        1.14 1,464 ( 77%)     NA

Checklist

@CoryMcCartan @christopherkenny

Jfer09 commented 2 years ago

2010 South Carolina Congressional Districts

Redistricting requirements

In South Carolina, districts must:

  1. be contiguous
  2. have equal populations
  3. be geographically compact
  4. preserve county and municipality boundaries as much as possible
  5. pass pre-clearance from the DOJ

https://redistricting.scsenate.gov/Documents/RedistrictingGuidelinesAdopted041311.pdf https://redistricting.schouse.gov/archives/2011/6334-1500-2011-Redistricting-Guidelines-(A0404871).pdf

Interpretation of requirements

We do not adhere to all criteria in the guidelines. We include the following constraints:

  1. We enforce a maximum population deviation of 0.5%.
  2. We impose a hinge constraint on the Black Voting Age Population so that it encourages districts with BVAP above 40%, but districts with BVAP of 30% or less are not penalized as much.
  3. We impose a municipality-split constraint to lower the number of municipality splits.

Data Sources

Data for South Carolina comes from the ALARM Project's 2020 Redistricting Data Files. <- not sure what I should put for 2010 because I couldn't find it in the ALARM website :(

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 6,000 districting plans for South Carolina. No special techniques were needed to produce the sample.

Validation

validation_20220706_2203

partisanship_of_seats

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby      pop_white      pop_black       pop_hisp 
     1.0017184      1.0076736      1.0000837      1.0014655      1.0080891      1.0053784      1.0150619      1.0100801 
      pop_aian      pop_asian       pop_nhpi      pop_other        pop_two      vap_white      vap_black       vap_hisp 
     0.9998978      1.0005043      1.0024173      1.0025930      1.0016946      1.0043995      1.0126980      1.0093582 
      vap_aian      vap_asian       vap_nhpi      vap_other        vap_two pre_16_rep_tru pre_16_dem_cli pre_20_rep_tru 
     1.0001556      1.0006002      1.0041020      1.0017029      1.0025194      1.0040227      1.0061493      1.0035632 
pre_20_dem_bid uss_16_rep_sco uss_16_dem_dix uss_20_rep_gra uss_20_dem_har gov_18_dem_smi gov_18_rep_mcm atg_18_dem_ana 
     1.0013353      1.0022584      1.0117500      1.0052989      1.0006937      1.0032953      1.0096629      1.0029299 
atg_18_rep_wil sos_18_dem_whi sos_18_rep_ham         adv_16         adv_18         adv_20         arv_16         arv_18 
     1.0085896      1.0078122      1.0090309      1.0093736      1.0033046      1.0016536      1.0023676      1.0089577 
        arv_20  county_splits    muni_splits            ndv            nrv        ndshare          e_dvs         pr_dem 
     1.0043677      1.0082489      1.0058738      1.0012658      1.0053194      1.0049363      1.0049391      1.0009597 
         e_dem          pbias           egap 
     1.0021474      1.0082487      1.0003794 

Sampling diagnostics for SMC run 1 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,850 (95.0%)     11.1%        0.32 1,882 ( 99%)      9 
Split 2     2,722 (90.7%)     15.1%        0.44 1,839 ( 97%)      6 
Split 3     2,637 (87.9%)     20.0%        0.50 1,792 ( 94%)      4 
Split 4     2,554 (85.1%)     14.7%        0.57 1,779 ( 94%)      5 
Split 5     2,398 (79.9%)     18.4%        0.63 1,758 ( 93%)      3 
Split 6     2,257 (75.2%)      8.2%        0.69 1,475 ( 78%)      2 
Resample    1,186 (39.5%)       NA%        1.22 1,454 ( 77%)     NA 

Sampling diagnostics for SMC run 2 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,849 (95.0%)     14.4%        0.33 1,902 (100%)      7 
Split 2     2,719 (90.6%)     18.5%        0.43 1,854 ( 98%)      5 
Split 3     2,607 (86.9%)     20.3%        0.53 1,825 ( 96%)      4 
Split 4     2,525 (84.2%)     23.0%        0.62 1,764 ( 93%)      3 
Split 5     2,418 (80.6%)     24.3%        0.64 1,767 ( 93%)      2 
Split 6     2,282 (76.1%)      8.2%        0.63 1,521 ( 80%)      2 
Resample    1,241 (41.4%)       NA%        1.16 1,453 ( 77%)     NA

Checklist

@kuriwaki

kuriwaki commented 2 years ago

The performance plot is giving signs that the most Black district ("Ordered district 7") in a decent chunk of the plans are not performing, i.e. they are more Republican than Democrat. That might not be compliant, especially given that 2010 districts would have fallen under pre-clearance. So we may need to strengthen the constraints so we are left with all performant districts.

@Jfer09 can we try summary statistics like the below and see what the numbers are? You can check out how it was summarized in 2020 in the PR post, right under the performance plot.

https://github.com/alarm-redist/fifty-states/blob/5165d8aa073fa6a6fc07aa0f2fca014972b2a6e1/analyses/SC_cd_2020/03_sim_SC_cd_2020.R#L79-L94

Jfer09 commented 2 years ago

2010 South Carolina Congressional Districts

Redistricting requirements

In South Carolina, districts must:

  1. be contiguous
  2. have equal populations
  3. be geographically compact
  4. preserve county and municipality boundaries as much as possible
  5. pass pre-clearance from the DOJ

https://redistricting.scsenate.gov/Documents/RedistrictingGuidelinesAdopted041311.pdf https://redistricting.schouse.gov/archives/2011/6334-1500-2011-Redistricting-Guidelines-(A0404871).pdf

Interpretation of requirements

We do not adhere to all criteria in the guidelines. We include the following constraints:

  1. We enforce a maximum population deviation of 0.5%.
  2. We impose a hinge constraint on the Black Voting Age Population so that it encourages districts with BVAP above 50%, but districts with BVAP of 30% or less are not penalized as much. This ensures that districts with high BVAP are able to elect their candidate of choice.
  3. We impose a municipality-split constraint to lower the number of municipality splits.

Data Sources

Data for South Carolina comes from the ALARM Project's 2020 Redistricting Data Files. <- not sure what I should put for 2010 because I couldn't find it in the ALARM website :(

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 6,000 districting plans across two independent runs of the SMC algorithm. We then remove all plans that do not contain any district that has both a BVAP of over 30% and an average vote share that is more Democratic than Republican. This removal occurs after verifying that such plans comprise less than 1% of the 6,000 plans. We then thin the sample down to exactly 5,000 plans. We also set the population tempering to 0.01 to avoid bottlenecks.

Validation

validation_20220804_1452 Rplot

91% of the highest-BVAP district and 28.5% of the second highest-BVAP district are blue in this plot.

n_black_perf    n
1            1 3996
2            2  997
3            3    7

Summary of Plan Object:

SMC: 6,000 sampled plans of 7 districts on 2,122 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0.01

Plan diversity 80% range: 0.48 to 0.83

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby      pop_white 
     1.0120475      1.0042467      1.0016465      1.0014947      1.0304959      1.0015804 
     pop_black       pop_hisp       pop_aian      pop_asian       pop_nhpi      pop_other 
     1.0050795      1.0005244      1.0076705      1.0002316      1.0035516      1.0016537 
       pop_two      vap_white      vap_black       vap_hisp       vap_aian      vap_asian 
     1.0008869      1.0011788      1.0051134      1.0049364      1.0080004      1.0002082 
      vap_nhpi      vap_other        vap_two pre_16_rep_tru pre_16_dem_cli pre_20_rep_tru 
     1.0034503      1.0010427      0.9999715      1.0154680      1.0074157      1.0092683 
pre_20_dem_bid uss_16_rep_sco uss_16_dem_dix uss_20_rep_gra uss_20_dem_har gov_18_dem_smi 
     1.0037990      1.0051767      1.0055020      1.0078760      1.0057119      1.0081538 
gov_18_rep_mcm atg_18_dem_ana atg_18_rep_wil sos_18_dem_whi sos_18_rep_ham         adv_16 
     1.0137191      1.0108854      1.0127145      1.0109889      1.0141728      1.0069088 
        adv_18         adv_20         arv_16         arv_18         arv_20  county_splits 
     1.0104412      1.0051354      1.0107766      1.0138879      1.0088795      1.0093744 
   muni_splits            ndv            nrv        ndshare          e_dvs         pr_dem 
     1.0178542      1.0098929      1.0118179      1.0123676      1.0121183      1.0020453 
         e_dem          pbias           egap 
     1.0109351      1.0076293      1.0278558 

Sampling diagnostics for SMC run 1 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     1,569 (52.3%)     11.1%        0.32 1,882 ( 99%)      9 
Split 2     1,998 (66.6%)     16.2%        0.90 1,490 ( 79%)      6 
Split 3     2,064 (68.8%)     20.5%        0.70 1,636 ( 86%)      4 
Split 4     2,094 (69.8%)     23.6%        0.70 1,726 ( 91%)      3 
Split 5     1,953 (65.1%)     24.3%        0.71 1,679 ( 89%)      2 
Split 6     1,678 (55.9%)      8.4%        0.81 1,436 ( 76%)      2 
Resample      358 (11.9%)       NA%        1.54 1,136 ( 60%)     NA 

Sampling diagnostics for SMC run 2 of 2 (3,000 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     1,595 (53.2%)     14.4%        0.33 1,902 (100%)      7 
Split 2     1,971 (65.7%)     20.0%        0.88 1,513 ( 80%)      5 
Split 3     2,024 (67.5%)     21.2%        0.72 1,631 ( 86%)      4 
Split 4     2,093 (69.8%)     23.3%        0.71 1,681 ( 89%)      3 
Split 5     2,107 (70.2%)     18.2%        0.71 1,681 ( 89%)      3 
Split 6     1,654 (55.1%)      8.8%        0.74 1,473 ( 78%)      2 
Resample       235 (7.8%)       NA%        1.48 1,065 ( 56%)     NA

Checklist

@kuriwaki