Closed christopherkenny closed 2 years ago
summary(plans_honolulu)
for reference
SMC: 5,000 sampled plans of 2 districts on 329 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0
Plan diversity 80% range: 0.31 to 0.59
Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 2,468 (98.7%) 6.9% 0.23 1,603 (101%) 6
Resample 2,377 (95.1%) NA% 0.23 1,536 ( 97%) 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 2,466 (98.7%) 7.1% 0.23 1,573 (100%) 6
Resample 2,372 (94.9%) NA% 0.23 1,564 ( 99%) NA
Carrying over diagnostics makes summary(plans)
give:
SMC: 5,000 sampled plans of 2 districts on 461 units
`adapt_k_thresh`=0.985 • `seq_alpha`=0.5
`est_label_mult`=1 • `pop_temper`=0
Plan diversity 80% range: 0.25 to 0.66
R-hat values for summary statistics:
total_vap plan_dev comp_edge comp_polsby pop_hisp pop_white pop_black pop_aian pop_asian
1.0002868 1.0007373 1.0001933 0.9998783 0.9999952 1.0022343 1.0010607 1.0011158 1.0002533
pop_nhpi pop_other pop_two vap_hisp vap_white vap_black vap_aian vap_asian vap_nhpi
1.0001349 1.0012130 1.0000471 1.0002944 1.0016297 1.0015171 1.0012796 1.0001531 1.0006517
vap_other vap_two pre_16_dem_cli pre_16_rep_tru uss_16_dem_sch uss_16_rep_car uss_18_dem_hir uss_18_rep_cur gov_18_dem_ige
1.0011878 1.0002517 0.9998244 0.9998939 0.9999832 1.0001158 0.9999012 1.0001883 0.9999943
gov_18_rep_tup pre_20_dem_bid pre_20_rep_tru arv_16 adv_16 arv_18 adv_18 arv_20 adv_20
1.0002951 0.9998419 0.9999805 0.9999493 0.9999208 1.0002241 0.9999463 0.9999805 0.9998419
muni_splits ndv nrv ndshare e_dvs egap
0.9998809 0.9998904 1.0000989 0.9998427 0.9998674 1.0000434
Sampling diagnostics for SMC run 1 of 2 (2,500 samples)
Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k
Split 1 2,468 (98.7%) 6.8% 0.23 1,559 ( 99%) 6
Resample 2,380 (95.2%) NA% 0.23 1,540 ( 97%) 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 2,468 (98.7%) 6.8% 0.23 1,594 (101%) 6
Resample 2,378 (95.1%) NA% 0.23 1,554 ( 98%) 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.
Redistricting requirements
In Hawaii, under HRS Title 1 S25, districts must:
Interpretation of requirements
We enforce a maximum population deviation of 0.5%. We use Census tracts are in accordance with (25-2(b)(4)). We use municipalities to attempt to follow (25-2(b)(6)) in absence of regional knowledge.
Data Sources
Data for Hawaii comes from the ALARM Project's 2020 Redistricting Data Files.
Pre-processing Notes
Islands are connecting in the adjacency graph, but this is not used for simulation purposes.
Simulation Notes
We sample 5,000 districting plans for Hawaii across 2 independent runs of the SMC algorithm. We use partial SMC to draw one district in the contiguous portion of Honolulu and assign the remainder to district 2. We use municipalities (or the county name if a tract is not assigned to a municipality) for the algorithmic constraint.
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
Note: Chain has to be assigned manually in
03_*.R
to usesummary(plans)
.