No manual pre-processing decisions were necessary.
Simulation Notes
We sample 15,000 districting plans for Idaho, across 2 independent runs of the SMC algorithm.
We sample using the standard algorithmic municipality constraint.
No special techniques were needed to produce the sample.
Validation
SMC: 30,000 sampled plans of 2 districts on 922 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0
Plan diversity 80% range: 0.00 to 0.69
✖ WARNING: Low plan diversity
R-hat values for summary statistics:
pop_overlap total_vap plan_dev comp_edge comp_polsby pop_white
1.0000763 1.0000477 1.0000422 1.0000343 0.9999983 1.0000142
pop_black pop_hisp pop_aian pop_asian pop_nhpi pop_other
1.0000588 1.0000549 1.0000673 1.0000921 1.0001120 1.0001892
pop_two vap_white vap_black vap_hisp vap_aian vap_asian
1.0001284 1.0000298 1.0000802 1.0000640 1.0000557 1.0000990
vap_nhpi vap_other vap_two pre_16_rep_tru pre_16_dem_cli pre_20_rep_tru
1.0000699 1.0001531 1.0000987 1.0000118 1.0000496 1.0000090
pre_20_dem_bid uss_16_rep_cra uss_16_dem_stu uss_20_rep_ris uss_20_dem_jor gov_18_rep_lit
1.0000232 1.0000137 1.0000407 1.0000183 1.0000245 1.0000625
gov_18_dem_jor atg_18_rep_was atg_18_dem_bis sos_18_rep_den sos_18_dem_hum adv_16
1.0000375 1.0000434 1.0000486 1.0000658 1.0000445 1.0000465
adv_18 adv_20 arv_16 arv_18 arv_20 county_splits
1.0000458 1.0000230 1.0000021 1.0000732 1.0000155 1.0000026
muni_splits ndv nrv ndshare e_dvs egap
0.9999667 1.0000420 1.0000373 1.0000328 1.0000320 1.0000194
Sampling diagnostics for SMC run 1 of 2 (15,000 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 14,453 (96.4%) 2.9% 0.41 9,547 (101%) 7
Resample 13,236 (88.2%) NA% 0.41 9,091 ( 96%) NA
Sampling diagnostics for SMC run 2 of 2 (15,000 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 14,447 (96.3%) 3.3% 0.41 9,518 (100%) 6
Resample 13,219 (88.1%) NA% 0.41 9,095 ( 96%) 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.
2010 Idaho Congressional Districts
Redistricting requirements
In Idaho, districts must:
Algorithmic Constraints
We enforce a maximum population deviation of 0.5%.
Data Sources
Data for Idaho comes from the Loyala Law School (LLS) All About Redistricting project
Pre-processing Notes
No manual pre-processing decisions were necessary.
Simulation Notes
We sample 15,000 districting plans for Idaho, across 2 independent runs of the SMC algorithm. We sample using the standard algorithmic municipality constraint. 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 editeddelete this line and all the tags except the reviewers you need @christopherkenny