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redistricting-analysis

Analyze U.S. electoral district plans in R with redistverse. Guides data preparation, simulations under population and geographic constraints, diagnostic checks, and comparisons of district shape, partisan fairness, and administrative splits through maps and summary tables.

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Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.

Run score Not tested

See how to install

Good for

  • Preparing census, election, and geographic data
  • Simulating district plans under population constraints
  • Comparing enacted plans with simulated alternatives
  • Creating district maps and summary tables

Files included (18)

  • SKILL.md5.8 KB
  • references/01-redist-simulation.md5.7 KB
  • references/02-redist-constraints.md5.0 KB
  • references/03-redist-advanced.md3.2 KB
  • references/04-redistmetrics-compactness.md4.1 KB
  • references/05-redistmetrics-partisan.md4.6 KB
  • references/06-redistmetrics-other.md4.5 KB
  • references/07-ggredist.md4.6 KB
  • references/08-adj.md4.2 KB
  • references/09-geomander-adjacency.md6.1 KB
  • references/10-geomander-spatial.md6.7 KB
  • references/11-geomander-data.md4.3 KB
  • references/12-alarmdata.md4.2 KB
  • references/13-pl94171.md3.6 KB
  • references/14-census-utilities.md5.2 KB
  • references/15-baf.md3.4 KB
  • references/16-rict.md3.4 KB
  • references/17-redistio.md4.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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 / TopicReference
redist_map, redist_smc, redist_mergesplit, redist_flip, redist_plans, diagnostics01-redist-simulation.md
redist_constr, add_constr_splits, add_constr_grp_hinge, all soft constraints02-redist-constraints.md
redist_enumpart, redist_shortburst, redist_cyclewalk, init_particles03-redist-advanced.md
redistmetrics — Polsby-Popper, Reock, spanning tree, prep_perims, comp_* functions04-redistmetrics-compactness.md
redistmetrics — efficiency gap, mean-median, bias, declination, part_* functions05-redistmetrics-partisan.md
redistmetrics — county splits, segregation, competitiveness, incumbents, plan_parity, group_frac06-redistmetrics-other.md
ggredist — geom_district, party color scales, cartographic palettes, theme_map07-ggredist.md
adj — adjacency graph construction, edge operations, coloring, Laplacian08-adj.md
geomander — adjacency construction, contiguity checks, seam_rip, edge editing09-geomander-adjacency.md
geomander — geo_match, estimate_down/up, block2prec, spatial estimation10-geomander-spatial.md
geomander — downloading VEST, ALARM, DRA, HEDA, get_lewis election data11-geomander-data.md
alarmdata — alarm_50state_map, pre-built datasets, caching, alarm_add_plan12-alarmdata.md
PL94171 — Census P.L. 94-171 decennial data ingestion13-pl94171.md
censable (data + state IDs), easycensus (ACS), tinytiger (TIGER shapefiles)14-census-utilities.md
baf — download official Census Bureau block assignment files15-baf.md
rict — gt summary tables: population, demographics, elections, compactness, splits16-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 typeTypical pop_tolLegal basis
Congressional0.005 (±0.5%)Wesberry v. Sanders
State legislative0.05 (±10%)Reynolds v. Sims
LocalVaries by stateState law

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