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)



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.


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.


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


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.


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


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.


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.


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


Benjamin Fifield, Department of Politics, Princeton University,

Michael Higgins, Department of Statistics, Kansas State University,

Kosuke Imai, Department of Politics, Princeton University,

James Lo,

Alexander Tarr, Department of Electrical Engineering, Princeton University


### Real data example from test set

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...