redistricting-analysis

Automate redistricting analysis with redistverse packages for Census data and simulations.

14|2|Updated Feb 26, 2026

Search Tags:#simulation #mapping #adjacency #redistricting #redistverse #plan-metrics

Ready to run

npx skills add https://github.com/christopherkenny/skills --skill redistricting-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: redistricting-analysis
Source: https://github.com/christopherkenny/skills/tree/main/skills/redistricting-analysis
Command: npx skills add https://github.com/christopherkenny/skills --skill redistricting-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Redistricting analysis is a complex, data-heavy workflow; this Skill provides an end-to-end pipeline based on the redistverse ecosystem to streamline data loading, adjacency construction, simulation, constraint application, diagnostics, metrics, and visualization.

Core Features & Use Cases

End-to-end workflow covering data loading, adjacency graph construction, SMC/MCMC simulation, constraints, convergence diagnostics, plan metrics, visualization, and interactive plan drawing.
Compare enacted vs simulated plans, explore plan diversity, and assess compactness and partisan fairness across ensembles.
Reproduce standard ALARM-based analyses and customize pipelines for state or district-level studies.

Quick Start

Load a pre-built Maryland map via alarm_50state_map('MD'), run a redist_smc ensemble, and compare the enacted plan using alarm_add_plan().

Frequently Asked Questions about redistricting-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run redistricting simulations in R using the redistverse?▼

Redistricting simulations in R use redistverse packages to load Census data, build adjacency graphs, and run SMC or mergesplit ensembles. This automates the end-to-end pipeline from redist_map creation to plan comparison.

What's the best way to compare enacted redistricting plans against simulated ensembles?▼

Comparing enacted redistricting plans against simulated ensembles uses redistmetrics to compute compactness and partisan fairness metrics. The pipeline generates comparison visuals to assess plan diversity and evaluate enacted maps against baseline simulations.

Does this redistricting workflow support ALARM-based plan analysis for state-level studies?▼

ALARM-based redistricting analysis is supported through pre-built state maps like alarm_50state_map. You can reproduce standard analyses, add enacted plans with alarm_add_plan, and customize pipelines for state or district-level studies.

Can I apply constraints and convergence diagnostics to redistricting SMC ensembles?▼

Constraints and convergence diagnostics apply directly to redistricting SMC ensembles within the redistverse pipeline. The workflow supports constraint application during simulation and provides diagnostics to validate ensemble results before computing metrics.

Do I need Census data and adjacency graphs before building redist_map objects?▼

Census data and adjacency graphs are required before building redist_map objects for redistricting analysis. The pipeline automates data loading and adjacency graph construction as foundational steps before running simulations or computing plan metrics.

What limitations exist when visualizing redistricting plan comparisons in R?▼

Redistricting plan visualization in R is limited to redistverse-supported outputs for comparison visuals and interactive plan drawing. The workflow depends on R-based environments and Census data availability, constraining analysis to compatible geographic frameworks.