Re-run 2020 Colorado Congressional Districts

[christopherkenny]

Redistricting requirements

In Colorado, districts must:

1. be contiguous
2. have equal populations -- Section 44.3 1(a)
3. be geographically compact -- Section 44.3 2(b)
4. preserve county and municipality boundaries as much as possible -- Section 44.3 2(a)
5. preserve whole communities of interest -- Section 44.3 2(a)
6. maximize the number of politically competitive districts -- Section 44.3 3(a)
7. not be drawn protect incumbents

Interpretation of requirements

We enforce a maximum population deviation of 0.5%.

Data Sources

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

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 5,000 districting plans for Colorado across 2 independent runs of the SMC algorithm. A partisan competitiveness constraint was used.

Validation

SMC: 5,000 sampled plans of 8 districts on 3,108 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0

Plan diversity 80% range: 0.46 to 0.76

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby       pop_hisp 
      1.033023       1.012775       1.003907       1.006380       1.013037       1.009250 
     pop_white      pop_black       pop_aian      pop_asian       pop_nhpi      pop_other 
      1.016050       1.015970       1.014766       1.013059       1.024864       1.006674 
       pop_two       vap_hisp      vap_white      vap_black       vap_aian      vap_asian 
      1.007744       1.011416       1.016515       1.015863       1.016873       1.010805 
      vap_nhpi      vap_other        vap_two pre_16_dem_cli pre_16_rep_tru uss_16_dem_ben 
      1.027309       1.005074       1.007367       1.006796       1.013571       1.006939 
uss_16_rep_gle gov_18_dem_pol gov_18_rep_sta atg_18_dem_wei atg_18_rep_bra sos_18_dem_gri 
      1.014184       1.007110       1.013171       1.006367       1.013952       1.006508 
sos_18_rep_wil pre_20_dem_bid pre_20_rep_tru uss_20_dem_hic uss_20_rep_gar         arv_16 
      1.014136       1.006460       1.013171       1.005776       1.013983       1.013733 
        adv_16         arv_18         adv_18         arv_20         adv_20  county_splits 
      1.007289       1.014109       1.006836       1.013468       1.005985       1.003699 
   muni_splits            ndv            nrv        ndshare          e_dvs         pr_dem 
      1.000444       1.006130       1.013439       1.010806       1.010808       0.999802 
         e_dem          pbias           egap 
      1.005082       1.004414       1.005532 

Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     1,790 (71.6%)     19.2%        0.51 1,582 (100%)      9 
Split 2     1,790 (71.6%)     25.5%        0.63 1,330 ( 84%)      6 
Split 3     1,783 (71.3%)     30.5%        0.67 1,327 ( 84%)      4 
Split 4     1,821 (72.8%)     33.2%        0.68 1,288 ( 82%)      3 
Split 5     1,837 (73.5%)     37.4%        0.69 1,321 ( 84%)      2 
Split 6     1,891 (75.7%)     27.3%        0.68 1,279 ( 81%)      2 
Split 7     1,780 (71.2%)      5.6%        0.68 1,200 ( 76%)      4 
Resample      906 (36.2%)       NA%        1.20 1,107 ( 70%)     NA 

Sampling diagnostics for SMC run 2 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     1,821 (72.9%)     12.4%        0.51 1,634 (103%)     14 
Split 2     1,776 (71.0%)     19.6%        0.64 1,346 ( 85%)      8 
Split 3     1,753 (70.1%)     25.4%        0.68 1,322 ( 84%)      5 
Split 4     1,818 (72.7%)     25.7%        0.72 1,318 ( 83%)      4 
Split 5     1,850 (74.0%)     22.7%        0.67 1,322 ( 84%)      4 
Split 6     1,675 (67.0%)     17.7%        0.72 1,268 ( 80%)      4 
Split 7     1,790 (71.6%)      5.7%        0.74 1,162 ( 74%)      4 
Resample      961 (38.5%)       NA%        1.21 1,149 ( 73%)     NA 

•  Watch out for low effective samples, very low acceptance rates (less than 1%), large
std. devs. of the log weights (more than 3 or so), and low numbers of unique plans. R-hat
values for summary statistics should be between 1 and 1.05.

Checklist

• [x] I have followed the instructions
• [x] I have updated the tracker
• [x] All TODO lines from the template code have been removed
• [x] I have merged in the master branch and then recalculated summary statistics
• [x] I have run enforce_style() to format my code
• [x] The documentation copied above is up-to-date
• [x] There are no data files in this pull request
• [x] None of the file output paths (for the redist_map and redist_plans objects, and summary statistics) have been edited

delete this line and all the tags except the reviewers you need @CoryMcCartan

Notes

Competitiveness constraint comparison