The output of these functions may be passed into redist_shortburst() as score_fn. Scoring functions have type redist_scorer and may be combined together using basic arithmetic operations.

scorer_group_pct(map, group_pop, total_pop, k = 1)


scorer_splits(map, counties)

scorer_multisplits(map, counties)


scorer_polsby_popper(map, perim_df = NULL, areas = NULL, m = 1)

scorer_status_quo(map, existing_plan = get_existing(map))



A redist_map object.


A numeric vector with the population of the group for every precinct.


A numeric vector with the population for every precinct.


the k-th from the top group fraction to return as the score.


A numeric vector with an integer from 1:n_counties


perimeter distance dataframe from redist.prep.polsbypopper


area of each precinct (ie st_area(map))


the m-th from the bottom Polsby Popper to return as the score. Defaults to 1, the minimum Polsby Popper score


A vector containing the current plan.


A scoring function of class redist_scorer. single numeric value, where larger values are better for frac_kept, group_pct, and polsby_popper and smaller values are better for splits and pop_dev.


Function details:

  • scorer_group_pct returns the k-th top group percentage across districts. For example, if the group is Democratic voters and k=3, then the function returns the 3rd-highest fraction of Democratic voters across all districts. Can be used to target k VRA districts or partisan gerrymanders.

  • scorer_pop_dev returns the maximum population deviation within a plan. Smaller values are closer to population parity, so use maximize=FALSE with this scorer.

  • scorer_splits returns the fraction of counties that are split within a plan. Higher values have more county splits, so use maximize=FALSE with this scorer.

  • scorer_frac_kept returns the fraction of edges kept in each district. Higher values mean more compactness.

  • scorer_polsby_popper returns the m-th Polsby Popper score within a plan. Higher scores correspond to more compact districts. Use m=ndists/2 to target the median compactness, m=1 to target the minimum compactness.

  • scorer_status_quo returns 1 - the rescaled variation of information distance between the plan and the existing_plan. Larger values indicate the plan is closer to the existing plan.


# \donttest{
iowa_map <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.05, total_pop = pop)

#> function(plans) {
#>         (edges - n_removed(adj, plans, ndists))/edges
#>     }
#> <environment: 0x156269438>
#> attr(,"class")
#> [1] "redist_scorer" "function"     
#> function(plans) {
#>         1 - 0.5*var_info_vec(plans, existing_plan, pop)/log(ndists)
#>     }
#> <bytecode: 0x16e777900>
#> <environment: 0x11d712758>
#> attr(,"class")
#> [1] "redist_scorer" "function"     
scorer_group_pct(iowa_map, dem_08, tot_08, k = 2)
#> function(plans) {
#>             group_pct_top_k(plans, group_pop, total_pop, k, ndists)
#>         }
#> <environment: 0x16e6ebd28>
#> attr(,"class")
#> [1] "redist_scorer" "function"     
1.5*scorer_frac_kept(iowa_map) + 0.4*scorer_status_quo(iowa_map)
#> function(plans) { fn1(plans) + fn2(plans) }
#> <environment: 0x16e669f90>
#> attr(,"class")
#> [1] "redist_scorer" "function"     
1.5*scorer_frac_kept(iowa_map) + scorer_frac_kept(iowa_map)*scorer_status_quo(iowa_map)
#> function(plans) { fn1(plans) + fn2(plans) }
#> <environment: 0x16e5f7ca8>
#> attr(,"class")
#> [1] "redist_scorer" "function"     
# }