https://github.com/alarm-redist/redist
Simulation methods for legislative redistricting.
https://github.com/alarm-redist/redist
geospatial gerrymandering r redistricting sampling
Last synced: about 2 months ago
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Simulation methods for legislative redistricting.
- Host: GitHub
- URL: https://github.com/alarm-redist/redist
- Owner: alarm-redist
- License: gpl-2.0
- Created: 2014-09-08T14:09:15.000Z (over 11 years ago)
- Default Branch: main
- Last Pushed: 2026-02-06T03:22:55.000Z (about 2 months ago)
- Last Synced: 2026-02-06T06:01:04.196Z (about 2 months ago)
- Topics: geospatial, gerrymandering, r, redistricting, sampling
- Language: C++
- Homepage: http://alarm-redist.org/redist/
- Size: 117 MB
- Stars: 72
- Watchers: 13
- Forks: 27
- Open Issues: 7
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
---
output: github_document
editor_options:
markdown:
wrap: 80
---
# **redist**: Simulation Methods for Legislative Redistricting
[](https://github.com/alarm-redist/redist/actions/workflows/check-standard.yaml)
[](https://cran.r-project.org/package=redist)
```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, fig.path = "man/figures/README-", comment = "#>")
set.seed(5118)
```
This R package enables researchers to sample redistricting plans from a
pre-specified target distribution using Sequential Monte Carlo and Markov
Chain Monte Carlo algorithms. The package supports various constraints in
the redistricting process, such as geographic compactness and population
parity requirements. Tools for analysis, including computation of various
summary statistics and plotting functionality, are also included.
## Installation Instructions
`redist` is available on CRAN and can be installed using:
```{r eval = FALSE}
install.packages("redist")
```
You can also install the most recent development version of `redist`
(which is usually quite stable) using the `remotes` package.
```{r eval=FALSE}
if (!require(remotes)) install.packages("remotes")
remotes::install_github("alarm-redist/redist@dev", dependencies=TRUE)
```
## Getting started
A basic analysis has two steps. First, you define a redistricting plan using
`redist_map`. Then you simulate plans using one of the algorithm functions:
`redist_smc`, `redist_flip`, and `redist_mergesplit`.
```{r message=FALSE}
library(redist)
library(dplyr)
data(iowa)
# set a 0.1% population constraint
iowa_map = redist_map(iowa, existing_plan=cd_2010, pop_tol=0.001, total_pop = pop)
# simulate 500 plans using the SMC algorithm
iowa_plans = redist_smc(iowa_map, nsims=500)
```
After generating plans, you can use `redist`'s plotting functions to study the
geographic and partisan characteristics of the simulated ensemble.
```{r readme-plot}
library(ggplot2)
library(patchwork) # for plotting
redist.plot.plans(iowa_plans, draws=c("cd_2010", "1", "2", "3"), shp=iowa_map)
iowa_plans = iowa_plans %>%
mutate(Compactness = comp_polsby(pl(), iowa_map),
`Population deviation` = plan_parity(iowa_map),
`Democratic vote` = group_frac(iowa_map, dem_08, tot_08))
hist(iowa_plans, `Population deviation`) + hist(iowa_plans, Compactness) +
plot_layout(guides="collect") +
plot_annotation(title="Simulated plan characteristics")
redist.plot.scatter(iowa_plans, `Population deviation`, Compactness) +
labs(title="Population deviation and compactness by plan")
plot(iowa_plans, `Democratic vote`, size=0.5, color_thresh=0.5) +
scale_color_manual(values=c("black", "tomato2", "dodgerblue")) +
labs(title="Democratic vote share by district")
```
A more detailed introduction to redistricting methods and the package can be
found in the [Get Started](https://alarm-redist.org/redist/articles/redist.html)
page. The package [vignettes](https://alarm-redist.org/redist/articles/)
contain more detailed information and guides to specific workflows.