redist (version 4.2.0)

redist.crsg: Redistricting via Compact Random Seed and Grow Algorithm

Description

redist.crsg generates redistricting plans using a random seed a grow algorithm. This is the compact districting algorithm described in Chen and Rodden (2013).

Usage

redist.crsg(
  adj,
  total_pop,
  shp,
  ndists,
  pop_tol,
  verbose = TRUE,
  maxiter = 5000
)

Value

list, containing three objects containing the completed redistricting plan.

• plan: A vector of length N, indicating the district membership of each precinct.

• district_list A list of length Ndistrict. Each list contains a vector of the precincts in the respective district.

• district_pop A vector of length Ndistrict, containing the population totals of the respective districts.

Arguments

adj

List of length N, where N is the number of precincts. Each list element is an integer vector indicating which precincts that precinct is adjacent to. It is assumed that precinct numbers start at 0.

total_pop

numeric vector of length N, where N is the number of precincts. Each element lists the population total of the corresponding precinct, and is used to enforce pop_tol constraints.

shp

An sf dataframe to compute area and centroids with.

ndists

integer, the number of districts we want to partition the precincts into.

pop_tol

numeric, indicating how close district population targets have to be to the target population before algorithm converges. pop_tol=0.05 for example means that all districts must be between 0.95 and 1.05 times the size of target.pop in population size.

verbose

boolean, indicating whether the time to run the algorithm is printed.

maxiter

integer, indicating maximum number of iterations to attempt before convergence to population constraint fails. If it fails once, it will use a different set of start values and try again. If it fails again, redist.rsg() returns an object of all NAs, indicating that use of more iterations may be advised. Default is 5000.

References

Jowei Chen and Jonathan Rodden (2013) ``Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures.'' Quarterly Journal of Political Science. 8(3): 239-269.

Examples

data("fl25")
adj <- redist.adjacency(fl25)
redist.crsg(adj = adj, total_pop = fl25$pop, shp = fl25, ndists = 2, pop_tol = .1)