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_pop_dev(map)
scorer_splits(map, counties)
scorer_multisplits(map, counties)
scorer_frac_kept(map)
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{
data(iowa)
iowa_map <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.05, total_pop = pop)
scorer_frac_kept(iowa_map)
#> function(plans) {
#> (edges - n_removed(adj, plans, ndists))/edges
#> }
#> <environment: 0x156269438>
#> attr(,"class")
#> [1] "redist_scorer" "function"
scorer_status_quo(iowa_map)
#> 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"
# }