Redistricting via Random Seed and Grow Algorithm

Source: R/rsg.R

redist.rsg generates redistricting plans using a random seed a grow algorithm. This is the non-compact districting algorithm described in Chen and Rodden (2013). The algorithm can provide start values for the other redistricting routines in this package.

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

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 population constraints.

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. thresh=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.

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.

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.

Author

Benjamin Fifield, Department of Politics, Princeton University benfifield@gmail.com, https://www.benfifield.com/

Michael Higgins, Department of Statistics, Kansas State University mikehiggins@k-state.edu, https://www.k-state.edu/stats/about/people/HigginsMichael.html

Kosuke Imai, Department of Politics, Princeton University imai@harvard.edu, https://imai.fas.harvard.edu

James Lo, jameslo@princeton.edu

Alexander Tarr, Department of Electrical Engineering, Princeton University atarr@princeton.edu

Examples

### Real data example from test set
data(fl25)
data(fl25_adj)

res <- redist.rsg(adj = fl25_adj, total_pop = fl25$pop,
    ndists = 3, pop_tol = 0.05)
#> 
#> ==================== 
#> redist.rsg(): Automated Redistricting Starts
#> 
#> 
#> 	3 districts built using 25 precincts in 0 seconds...
#>