alarm-redist / fifty-states

Redistricting analysis for all 50 U.S. states
https://alarm-redist.github.io/fifty-states/
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2010 New Mexico Congressional Districts #148

Closed mzwu closed 1 year ago

mzwu commented 1 year ago

Redistricting requirements

In New Mexico, according to the New Mexico Legislative Council Guidelines, 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 communities of interest
  6. preserve the core of existing districts

Algorithmic Constraints

We enforce a maximum population deviation of 0.5%. We add a hinge Gibbs constraint targeting the same number of majority-minority districts as the enacted plan. We also apply a hinge Gibbs constraint to discourage packing of minority voters.

Data Sources

Data for New Mexico comes from the ALARM Project's 2020 Redistricting Data Files. Data for the 2010 New Mexico enacted congressional map comes from All About Redistricting.

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 2000 plan.

Simulation Notes

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

Validation

validation_20221217_1944

SMC: 5,000 sampled plans of 3 districts on 1,448 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0

Plan diversity 80% range: 0.18 to 0.51

R-hat values for summary statistics:
   pop_overlap      total_vap       plan_dev      comp_edge    comp_polsby      pop_white      pop_black 
     1.0012804      0.9998424      0.9999730      1.0029989      1.0002152      1.0003091      1.0002312 
      pop_hisp       pop_aian      pop_asian       pop_nhpi      pop_other        pop_two      vap_white 
     1.0003233      1.0001935      1.0002644      1.0000568      1.0003827      1.0003439      1.0001395 
     vap_black       vap_hisp       vap_aian      vap_asian       vap_nhpi      vap_other        vap_two 
     1.0001620      0.9998905      1.0000895      1.0001015      1.0002450      1.0002742      1.0003503 
pre_16_dem_cli pre_16_rep_tru pre_20_dem_bid pre_20_rep_tru uss_18_dem_hei uss_18_rep_ric uss_20_dem_luj 
     0.9998202      1.0004816      0.9998269      1.0001452      0.9998456      1.0003610      0.9998388 
uss_20_rep_ron gov_18_dem_luj gov_18_rep_pea atg_18_dem_bal atg_18_rep_hen sos_16_dem_oli sos_16_rep_esp 
     1.0002078      0.9999613      1.0002115      0.9999101      1.0001633      0.9998014      1.0004577 
sos_18_dem_tou sos_18_rep_cla         adv_16         adv_18         adv_20         arv_16         arv_18 
     0.9998031      1.0002771      0.9998143      0.9998903      0.9998311      1.0004027      1.0002958 
        arv_20  county_splits    muni_splits            ndv            nrv        ndshare          e_dvs 
     1.0002606      0.9998038      1.0022875      0.9998335      1.0005793      0.9998990      0.9998947 
         e_dem          pbias           egap 
     1.0015263      0.9998328      1.0038788 

Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
         Eff. samples (%) Acc. rate Log wgt. sd  Max. unique Est. k 
Split 1     2,246 (89.9%)      5.0%        0.27 1,581 (100%)      8 
Split 2     2,287 (91.5%)      3.0%        0.33 1,440 ( 91%)      5 
Resample    1,781 (71.2%)       NA%        0.73 1,948 (123%)     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     2,242 (89.7%)      5.6%        0.28 1,590 (101%)      7 
Split 2     2,302 (92.1%)      3.7%        0.33 1,459 ( 92%)      4 
Resample    1,826 (73.0%)       NA%        0.69 1,993 (126%)     NA

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