We sample 5,000 districting plans for West Virginia, across 4 independent runs of the SMC algorithm.
No special techniques were needed to produce the sample.
Validation
SMC: 5,000 sampled plans of 2 districts on 55 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0
Plan diversity 80% range: 0.097 to 0.434
✖ WARNING: Low plan diversity
R-hat values for summary statistics:
pop_overlap total_vap plan_dev comp_edge comp_polsby pop_white pop_black pop_hisp pop_aian pop_asian
1.0020773 0.9998947 0.9999536 1.0003087 1.0007092 1.0002150 1.0005952 1.0005577 1.0014897 1.0006485
pop_nhpi pop_other pop_two vap_white vap_black vap_hisp vap_aian vap_asian vap_nhpi vap_other
1.0001644 1.0010625 1.0022005 1.0001193 1.0005887 1.0000335 1.0006007 1.0005678 1.0001469 1.0008277
vap_two pre_20_rep_tru pre_20_dem_bid adv_20 arv_20 ndv nrv ndshare e_dvs egap
1.0016263 1.0009999 1.0011749 1.0011749 1.0009999 1.0011749 1.0009999 1.0006942 1.0006942 1.0014646
Sampling diagnostics for SMC run 1 of 4 (1,250 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 1,239 (99.1%) 2.8% 0.2 784 ( 99%) 4
Resample 1,211 (96.8%) NA% 0.2 778 ( 98%) NA
Sampling diagnostics for SMC run 2 of 4 (1,250 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 1,239 (99.1%) 2.7% 0.2 783 ( 99%) 4
Resample 1,209 (96.8%) NA% 0.2 777 ( 98%) NA
Sampling diagnostics for SMC run 3 of 4 (1,250 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 1,239 (99.1%) 2.7% 0.2 770 ( 97%) 4
Resample 1,211 (96.9%) NA% 0.2 783 ( 99%) NA
Sampling diagnostics for SMC run 4 of 4 (1,250 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 1,239 (99.1%) 2.9% 0.2 806 (102%) 4
Resample 1,209 (96.7%) NA% 0.2 789 (100%) 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.
• Low diversity: Check for potential bottlenecks. Increase the number of samples. Examine the diversity plot with `hist(plans_diversity(plans),
breaks=24)`. Consider weakening or removing constraints, or increasing the population tolerance. If the accpetance rate drops quickly in the final splits,
try increasing `pop_temper` by 0.01.
Redistricting requirements
In West Virginia, under the state constitution, districts must:
Interpretation of requirements
We enforce a maximum population deviation of 0.5%. We simulate at the county level.
Data Sources
Data for West Virginia comes from the The Upshot Presidential Precinct Map data and are joined with county level Census data.
Pre-processing Notes
Data is aggregated to the county level.
Simulation Notes
We sample 5,000 districting plans for West Virginia, across 4 independent runs of the SMC algorithm. No special techniques were needed to produce the sample.
Validation
Checklist
TODO
lines from the template code have been removedenforce_style()
to format my coderedist_map
andredist_plans
objects, and summary statistics) have been edited@CoryMcCartan
Additional Notes:
Appears to me that the diversity is actually fine. This is a 55 county into 2 districts problem.