alarm-redist / fifty-states

Redistricting analysis for all 50 U.S. states
https://alarm-redist.github.io/fifty-states/
Other
9 stars 7 forks source link

# 2010 Minnesota Congressional Districts #134

Closed Jfer09 closed 1 year ago

Jfer09 commented 1 year ago

Redistricting requirements

In Minnesota, districts must:

  1. be contiguous 2, have equal populations
  2. comply with VRA section 2
  3. be geographically compact
  4. preserve political subdivisions and communities of interest as possible
  5. avoid pairing incumbents but also cannot give unfair advantage to incumbents (least important criteria)

https://www.mncourts.gov/mncourtsgov/media/CIOMediaLibrary/2011Redistricting/A110152Order11-4-11.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 use a pseudo-county constraint to help preserve county and municipality boundaries.

Data Sources

Data for Minnesota comes from the ALARM Project's 2010 Redistricting Data Files.

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 5,000 districting plans for Minnesota across two independent runs of the SMC algorithm. We use a pseudo-county constraint to limit the county and municipality splits. Municipality lines used are in , which are all counties with populations larger than 60% the target population for district.

Validation

validation_20220923_0027

SMC: 5,000 sampled plans of 8 districts on 4,139 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0

Plan diversity 80% range: 0.75 to 0.96

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge 
     1.0005790      1.0056396      1.0056415      1.0049476 
   comp_polsby      pop_white      pop_black       pop_hisp 
     1.0046960      1.0108363      0.9998412      1.0063150 
      pop_aian      pop_asian       pop_nhpi      pop_other 
     1.0104688      1.0001160      1.0035693      1.0027999 
       pop_two      vap_white      vap_black       vap_hisp 
     1.0010168      0.9999362      1.0004351      1.0057413 
      vap_aian      vap_asian       vap_nhpi      vap_other 
     1.0150546      1.0003509      1.0088918      1.0038647 
       vap_two pre_16_rep_tru pre_16_dem_cli pre_20_dem_bid 
     1.0030227      1.0056811      1.0035710      1.0015246 
pre_20_rep_tru uss_18_rep_new uss_18_dem_klo uss_20_dem_smi 
     1.0078803      1.0127675      1.0037961      1.0019473 
uss_20_rep_lew gov_18_rep_joh gov_18_dem_wal atg_18_rep_war 
     1.0245385      1.0107032      1.0064958      1.0110698 
atg_18_dem_ell sos_18_rep_how sos_18_dem_sim         adv_16 
     1.0057198      1.0118992      1.0041557      1.0035710 
        adv_18         adv_20         arv_16         arv_18 
     1.0051309      1.0012599      1.0056811      1.0117627 
        arv_20  county_splits    muni_splits            ndv 
     1.0109896      1.0090998      1.0011957      1.0032745 
           nrv        ndshare          e_dvs         pr_dem 
     1.0271921      1.0006879      1.0005442      1.0011941 
         e_dem          pbias           egap 
     0.9999207      1.0017316      1.0002931 

Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique
Split 1     2,458 (98.3%)      9.8%        0.26 1,585 (100%)
Split 2     2,412 (96.5%)     13.8%        0.35 1,567 ( 99%)
Split 3     2,367 (94.7%)     17.6%        0.45 1,574 (100%)
Split 4     2,326 (93.1%)     19.4%        0.52 1,551 ( 98%)
Split 5     2,267 (90.7%)     17.1%        0.58 1,545 ( 98%)
Split 6     2,242 (89.7%)      7.8%        0.59 1,455 ( 92%)
Split 7     2,280 (91.2%)      4.4%        0.55 1,326 ( 84%)
Resample    1,647 (65.9%)       NA%        0.56 1,920 (121%)
         Est. k 
Split 1      13 
Split 2       8 
Split 3       5 
Split 4       4 
Split 5       4 
Split 6       7 
Split 7       4 
Resample     NA 

Sampling diagnostics for SMC run 2 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique
Split 1     2,459 (98.4%)     11.6%        0.26 1,592 (101%)
Split 2     2,417 (96.7%)     15.8%        0.35 1,560 ( 99%)
Split 3     2,393 (95.7%)     18.5%        0.42 1,571 ( 99%)
Split 4     2,305 (92.2%)     20.0%        0.50 1,533 ( 97%)
Split 5     2,290 (91.6%)     16.4%        0.55 1,535 ( 97%)
Split 6     2,300 (92.0%)     17.4%        0.54 1,439 ( 91%)
Split 7     2,285 (91.4%)      4.5%        0.54 1,364 ( 86%)
Resample    1,583 (63.3%)       NA%        0.55 1,907 (121%)
         Est. k 
Split 1      11 
Split 2       7 
Split 3       5 
Split 4       4 
Split 5       4 
Split 6       3 
Split 7       4 
Resample     NA 

Checklist

@CoryMcCartan @christopherkenny

Jfer09 commented 1 year ago

Redistricting requirements

In Minnesota, districts must:

  1. be contiguous
  2. have equal populations
  3. comply with VRA section 2
  4. be geographically compact
  5. preserve political subdivisions and communities of interest as possible
  6. avoid pairing incumbents but also cannot give unfair advantage to incumbents (least important criteria)

https://www.mncourts.gov/mncourtsgov/media/CIOMediaLibrary/2011Redistricting/A110152Order11-4-11.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 use a pseudo-county constraint to help preserve county and municipality boundaries.

Data Sources

Data for Minnesota comes from the ALARM Project's 2010 Redistricting Data Files.

Pre-processing Notes

No manual pre-processing decisions were necessary.

Simulation Notes

We sample 5,000 districting plans for Minnesota across two independent runs of the SMC algorithm. To balance county and municipality splits, we create pseudocounties for use in the county constraint. These are counties, outside of Dakota County, Hennepin County, Ramsey, which are the counties with populations larger than 60% the target population for districts. Within Allegheny County, Montgomery County, and Philadelphia County, each municipality is its own pseudocounty as well.

Validation

validation_20220923_0027

SMC: 5,000 sampled plans of 8 districts on 4,139 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0

Plan diversity 80% range: 0.75 to 0.96

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge 
     1.0005790      1.0056396      1.0056415      1.0049476 
   comp_polsby      pop_white      pop_black       pop_hisp 
     1.0046960      1.0108363      0.9998412      1.0063150 
      pop_aian      pop_asian       pop_nhpi      pop_other 
     1.0104688      1.0001160      1.0035693      1.0027999 
       pop_two      vap_white      vap_black       vap_hisp 
     1.0010168      0.9999362      1.0004351      1.0057413 
      vap_aian      vap_asian       vap_nhpi      vap_other 
     1.0150546      1.0003509      1.0088918      1.0038647 
       vap_two pre_16_rep_tru pre_16_dem_cli pre_20_dem_bid 
     1.0030227      1.0056811      1.0035710      1.0015246 
pre_20_rep_tru uss_18_rep_new uss_18_dem_klo uss_20_dem_smi 
     1.0078803      1.0127675      1.0037961      1.0019473 
uss_20_rep_lew gov_18_rep_joh gov_18_dem_wal atg_18_rep_war 
     1.0245385      1.0107032      1.0064958      1.0110698 
atg_18_dem_ell sos_18_rep_how sos_18_dem_sim         adv_16 
     1.0057198      1.0118992      1.0041557      1.0035710 
        adv_18         adv_20         arv_16         arv_18 
     1.0051309      1.0012599      1.0056811      1.0117627 
        arv_20  county_splits    muni_splits            ndv 
     1.0109896      1.0090998      1.0011957      1.0032745 
           nrv        ndshare          e_dvs         pr_dem 
     1.0271921      1.0006879      1.0005442      1.0011941 
         e_dem          pbias           egap 
     0.9999207      1.0017316      1.0002931 

Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique
Split 1     2,458 (98.3%)      9.8%        0.26 1,585 (100%)
Split 2     2,412 (96.5%)     13.8%        0.35 1,567 ( 99%)
Split 3     2,367 (94.7%)     17.6%        0.45 1,574 (100%)
Split 4     2,326 (93.1%)     19.4%        0.52 1,551 ( 98%)
Split 5     2,267 (90.7%)     17.1%        0.58 1,545 ( 98%)
Split 6     2,242 (89.7%)      7.8%        0.59 1,455 ( 92%)
Split 7     2,280 (91.2%)      4.4%        0.55 1,326 ( 84%)
Resample    1,647 (65.9%)       NA%        0.56 1,920 (121%)
         Est. k 
Split 1      13 
Split 2       8 
Split 3       5 
Split 4       4 
Split 5       4 
Split 6       7 
Split 7       4 
Resample     NA 

Sampling diagnostics for SMC run 2 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique
Split 1     2,459 (98.4%)     11.6%        0.26 1,592 (101%)
Split 2     2,417 (96.7%)     15.8%        0.35 1,560 ( 99%)
Split 3     2,393 (95.7%)     18.5%        0.42 1,571 ( 99%)
Split 4     2,305 (92.2%)     20.0%        0.50 1,533 ( 97%)
Split 5     2,290 (91.6%)     16.4%        0.55 1,535 ( 97%)
Split 6     2,300 (92.0%)     17.4%        0.54 1,439 ( 91%)
Split 7     2,285 (91.4%)      4.5%        0.54 1,364 ( 86%)
Resample    1,583 (63.3%)       NA%        0.55 1,907 (121%)
         Est. k 
Split 1      11 
Split 2       7 
Split 3       5 
Split 4       4 
Split 5       4 
Split 6       3 
Split 7       4 
Resample     NA 

Checklist

@CoryMcCartan @christopherkenny