redist (version 2.0.2)

redist.enumerate: Exact Redistricting Plan Enumerator

Description

redist.enumerate uses a spanning-tree method to fully enumerate all valid redistricting plans with $n$ districts given a set of geographic units. redist.enumerate also allows suers to implement minimum and maximum numbers of geographic units per district, as well as population parity requirements.

Usage

redist.enumerate(adjobj,
ndists = 2, popvec = NULL, nconstraintlow = NULL,
nconstrainthigh = NULL, popcons = NULL, contiguitymap = "rooks")

Arguments

adjobj

An adjacency list, matrix, or object of class SpatialPolygonsDataFrame.

ndists

The desired number of congressional districts. The default is 2.

popvec

A vector of geographic unit populations. The default is NULL.

nconstraintlow

Lower bound for number of geographic units to include in a district. The default is NULL.

nconstrainthigh

Lower bound for number of geographic units to include in a district. The default is NULL.

popcons

The strength of the hard population constraint. popcons = 0.05 means that any proposed swap that brings a district more than 5% away from population parity will be rejected. The default is NULL.

contiguitymap

Use queens or rooks distance criteria for generating an adjacency list from a "SpatialPolygonsDataFrame" data type. Default is "rooks".

Value

redist.enumerate returns an object of class "list". Each entry in the list is a vector of congressional district assignments, where the first entry in the vector corresponds to the congressional district assignment of the first geographic unit.

Details

This function allows users to input a set of geographic units to generate all valid partitions of $n$ congressional districts. The function uses a set of spanning-tree methods to generate all valid, contiguous partitions, which makes it more efficient than brute-force methods. However, even with these methods, full redistricting problems quickly become intractable, necessitating the use of the MCMC-based methods implemented in redist.mcmc.

References

Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain Monte Carlo." Working Paper. Available at http://imai.princeton.edu/research/files/redist.pdf.

Examples

# NOT RUN {
data(algdat.pfull)
test <- redist.enumerate(adjobj = algdat.pfull$adjlist)
# }