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 |