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alarm-redist / fifty-states

Redistricting analysis for all 50 U.S. states
https://alarm-redist.github.io/fifty-states/
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Re-run 2020 Kansas Congressional Districts #102

Closed mzwu closed 2 years ago

mzwu commented 2 years ago

Redistricting requirements

In Kansas, according to the Proposed Guidelines and Criteria for 2022 Kansas Congressional Redistricting districts must:

  1. be contiguous
  2. have equal populations
  3. be geographically compact
  4. preserve county and municipality boundaries as much as possible
  5. preserve the cores of existing districts
  6. preserve communities of social, cultural, racial, ethnic, and economic interest to the extent possible

Interpretation of requirements

We enforce a maximum population deviation of 0.5%. We add a county constraint.

Data Sources

Data for Kansas comes from the ALARM Project's 2020 Redistricting Data Files. Data for the 2022 Kansas enacted congressional map comes from the American Redistricting Project.

Pre-processing Notes

To preserve the cores of prior districts, we merge all precincts which are more than two precincts away from a district border, under the 2010 plan. Precincts in counties which are split by existing district boundaries are merged only within their county.

Simulation Notes

We sample 5,000 districting plans for Kansas across two independent runs of the SMC algorithm. No special techniques were needed to produce the sample.

Validation

image

SMC: 5,000 sampled plans of 4 districts on 4,240 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.7
`est_label_mult`=1 • `pop_temper`=0

Plan diversity 80% range: 0.28 to 0.66

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby       pop_hisp      pop_white      pop_black       pop_aian 
     1.0118359      1.0224552      1.0031595      1.0023906      1.0135702      1.0009448      1.0018838      1.0011482      1.0027790 
     pop_asian       pop_nhpi      pop_other        pop_two       vap_hisp      vap_white      vap_black       vap_aian      vap_asian 
     1.0004842      0.9998856      1.0019285      1.0014793      1.0008936      1.0035224      1.0018307      1.0023448      1.0001488 
      vap_nhpi      vap_other        vap_two pre_16_dem_cli pre_16_rep_tru uss_16_dem_wie uss_16_rep_mor gov_18_dem_kel gov_18_rep_kob 
     0.9998051      1.0035229      1.0021341      1.0037526      1.0018046      1.0020234      1.0009911      1.0056895      1.0005406 
atg_18_rep_sch atg_18_dem_swa sos_18_rep_sch sos_18_dem_mcc pre_20_dem_bid pre_20_rep_tru uss_20_dem_bol uss_20_rep_mar         arv_16 
     1.0011371      1.0043595      1.0013143      1.0071854      1.0061924      1.0012160      1.0063252      1.0008371      1.0011882 
        adv_16         arv_18         adv_18         arv_20         adv_20  county_splits    muni_splits            ndv            nrv 
     1.0010069      1.0023530      1.0060069      1.0015454      1.0062749      1.0003289      1.0045727      1.0050435      1.0018384 
       ndshare          e_dvs         pr_dem          e_dem          pbias           egap 
     1.0038896      1.0038482      1.0002001      1.0039393      1.0049860      1.0028141 

Sampling diagnostics for SMC run 1 of 2
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,423 (96.9%)      3.9%        0.26 1,589 (101%)      9 
Split 2     1,117 (44.7%)      5.8%        0.36 1,492 ( 94%)      6 
Split 3       874 (35.0%)      1.9%        0.45   717 ( 45%)      4 
Resample      740 (29.6%)       NA%        7.24   876 ( 55%)     NA 

Sampling diagnostics for SMC run 2 of 2
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,420 (96.8%)      4.4%        0.27 1,589 (101%)      8 
Split 2     1,024 (41.0%)      6.5%        0.39 1,490 ( 94%)      5 
Split 3       870 (34.8%)      1.8%        0.45   652 ( 41%)      4 
Resample      504 (20.2%)       NA%        7.14   891 ( 56%)     NA

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