Redistricting analysis

Redistricting Analysis with the redistverse

A comprehensive reference for redistricting analysis in R using the redistverse ecosystem (library(redistverse) loads redist, redistmetrics, ggredist, geomander, sf, and adj).

Standard Analysis Pipeline

1. Load data — Download pre-built precinct map + demographics + elections via alarmdata, or assemble your own sf object and build a redist_map. → 12-alarmdata.md
2. Prepare adjacency — Verify and edit the adjacency graph (remove water-body edges, fix topological errors). → 08-adj.md, 09-geomander-adjacency.md
3. Set constraints — Define population tolerance, county-split penalties, VRA hinge constraints, compactness. → 02-redist-constraints.md
4. Simulate — Run redist_smc() (recommended) or MCMC to generate an ensemble of valid plans. → 01-redist-simulation.md
5. Validate — Check convergence (summary()), R-hat, effective sample size, and plan diversity. → 01-redist-simulation.md
6. Score — Compute compactness, partisan fairness, splits, and other metrics across the ensemble. → 04–06 redistmetrics references
7. Visualize & compare — Map plans, plot metric distributions, compare enacted plan to the baseline. → 07-ggredist.md

Quick Navigation

Package / Topic	Reference
redist_map, redist_smc, redist_mergesplit, redist_flip, redist_plans, diagnostics	01-redist-simulation.md
redist_constr, add_constr_splits, add_constr_grp_hinge, all soft constraints	02-redist-constraints.md
redist_enumpart, redist_shortburst, redist_cyclewalk, init_particles	03-redist-advanced.md
redistmetrics — Polsby-Popper, Reock, spanning tree, prep_perims, comp_* functions	04-redistmetrics-compactness.md
redistmetrics — efficiency gap, mean-median, bias, declination, part_* functions	05-redistmetrics-partisan.md
redistmetrics — county splits, segregation, competitiveness, incumbents, plan_parity, group_frac	06-redistmetrics-other.md
ggredist — geom_district, party color scales, cartographic palettes, theme_map	07-ggredist.md
adj — adjacency graph construction, edge operations, coloring, Laplacian	08-adj.md
geomander — adjacency construction, contiguity checks, seam_rip, edge editing	09-geomander-adjacency.md
geomander — geo_match, estimate_down/up, block2prec, spatial estimation	10-geomander-spatial.md
geomander — downloading VEST, ALARM, DRA, HEDA, get_lewis election data	11-geomander-data.md
alarmdata — alarm_50state_map, pre-built datasets, caching, alarm_add_plan	12-alarmdata.md
PL94171 — Census P.L. 94-171 decennial data ingestion	13-pl94171.md
censable (data + state IDs), easycensus (ACS), tinytiger (TIGER shapefiles)	14-census-utilities.md
baf — download official Census Bureau block assignment files	15-baf.md
rict — gt summary tables: population, demographics, elections, compactness, splits	16-rict.md
redistio — interactive Shiny plan drawing (draw()) and adjacency editor (adj_editor())	17-redistio.md

Key Data Structures

redist_map — an sf tibble with one row per precinct. Stores the adjacency graph, population column, number of districts, and population tolerance. Created by redist_map() or downloaded via alarm_50state_map(). Standard columns include pop, pop_black, pop_hisp, pop_asian, pop_white, pop_vap, pop_bvap, ndv (Democratic votes), nrv (Republican votes), and geometry.

redist_plans — a tibble with one row per district per plan (so nsims × ndists rows, plus reference plans). Stores district assignments in a hidden integer matrix; metrics are added as columns via mutate(). Access the plan matrix with get_plans_matrix().

adj — an S3 vector class representing an adjacency list. Each element is a zero-indexed integer vector of neighbors. Stored as the adj column in redist_map.

Population Tolerance Guidelines

Map type	Typical pop_tol	Legal basis
Congressional	0.005 (±0.5%)	Wesberry v. Sanders
State legislative	0.05 (±10%)	Reynolds v. Sims
Local	Varies by state	State law