R/pop_overlap.R
redist.dist.pop.overlap.RdThis implements Crespin's 2005 measure of district continuity, as applied to the geographies represented by a plan, typically precincts or voting districts. This implementation assumes none of the precincts in plan_old or plan_new are split.
redist.dist.pop.overlap(plan_old, plan_new, total_pop, normalize_rows = TRUE)The reference or original plan to compare against
The new plan to compare to the reference plan
The total population by precinct This can also take a redist_map object and will use the population in that object. If nothing is provided, it weights all entries in plan equally.
Default TRUE. Normalize populations by row. If FALSE, normalizes by column. If NULL, does not normalize.
matrix with length(unique(plan_old)) rows and length(unique(plan_new)) columns
"Using Geographic Information Systems to Measure District Change, 2000-02", Michael Crespin, Political Analysis (2005) 13(3): 253-260
set.seed(5)
data(iowa)
iowa_map <- redist_map(iowa, total_pop = pop, pop_tol = 0.01, ndists = 4)
plans <- redist_smc(iowa_map, 2)
#> SEQUENTIAL MONTE CARLO
#> Sampling 2 99-unit maps with 4 districts and population between 753973 and 769205.
#> Split [0/3] ■ | ETA?
#> Split [3/3] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | ETA 0s
#>
plans_mat <- get_plans_matrix(plans)
ov <- redist.dist.pop.overlap(plans_mat[, 1], plans_mat[, 2], iowa_map)
round(ov, 2)
#> 1 2 3 4
#> 1 0.14 0.00 0.18 0.68
#> 2 0.05 0.17 0.78 0.00
#> 3 0.59 0.06 0.02 0.33
#> 4 0.22 0.76 0.02 0.00
ov_col <- redist.dist.pop.overlap(plans_mat[, 1], plans_mat[, 2], iowa_map, normalize_rows = FALSE)
round(ov_col, 2)
#> 1 2 3 4
#> 1 0.14 0.00 0.18 0.67
#> 2 0.05 0.17 0.78 0.00
#> 3 0.59 0.06 0.02 0.33
#> 4 0.22 0.77 0.02 0.00
ov_un_norm <- redist.dist.pop.overlap(plans_mat[, 1], plans_mat[, 2],
iowa_map, normalize_rows = NULL)
round(ov_un_norm, 2)
#> 1 2 3 4
#> 1 105215 0 138897 513848
#> 2 40648 131090 592727 0
#> 3 446353 43843 14928 252869
#> 4 165224 582584 18129 0
iowa_map_5 <- iowa_map <- redist_map(iowa, total_pop = pop, pop_tol = 0.01, ndists = 5)
plan_5 <- get_plans_matrix(redist_smc(iowa_map_5, 1))
#> SEQUENTIAL MONTE CARLO
#> Sampling 1 99-unit maps with 5 districts and population between 603178 and 615364.
ov4_5 <- redist.dist.pop.overlap(plans_mat[, 1], plan_5, iowa_map)
round(ov4_5, 2)
#> 1 2 3 4 5
#> 1 0.00 0.00 0.70 0.19 0.11
#> 2 0.00 0.25 0.00 0.37 0.39
#> 3 0.33 0.02 0.11 0.24 0.30
#> 4 0.47 0.53 0.00 0.00 0.00