Segregation index calculation for MCMC redistricting.

redist.segcalc calculates the dissimilarity index of segregation (see Massey & Denton 1987 for more details) for a specified subgroup under any redistricting plan.

segregation_index(
  map,
  group_pop,
  total_pop = map[[attr(map, "pop_col")]],
  .data = cur_plans()
)

redist.segcalc(plans, group_pop, total_pop)

Arguments

map

a redist_map object

group_pop

A vector of populations for some subgroup of interest.

total_pop

A vector containing the populations of each geographic unit.

.data

a redist_plans object

plans

A matrix of congressional district assignments or a redist object.

Value

redist.segcalc returns a vector where each entry is the dissimilarity index of segregation (Massey & Denton 1987) for each redistricting plan in algout.

References

Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain Monte Carlo." Working Paper. Available at http://imai.princeton.edu/research/files/redist.pdf.

Massey, Douglas and Nancy Denton. (1987) "The Dimensions of Social Segregation". Social Forces.

Examples

# \donttest{
data(fl25)
data(fl25_enum)
data(fl25_adj)

## Get an initial partition
init_plan <- fl25_enum$plans[, 5118]
fl25$init_plan <- init_plan

## 25 precinct, three districts - no pop constraint ##
fl_map <- redist_map(fl25, existing_plan = 'init_plan', adj = fl25_adj)
#> Projecting to CRS 3857
alg_253 <- redist_flip(fl_map, nsims = 10000)
#> 
#> ── redist_flip() ───────────────────────────────────────────────────────────────
#> 
#> ── Automated Redistricting Simulation Using Markov Chain Monte Carlo ──
#> ℹ Preprocessing data.
#> ℹ Starting swMH().
#> ■                                  0% | ETA: 8s
#> ■■■■                              11% | ETA:  2s | MH Acceptance: 0.79
#> ■■■■■■■                           21% | ETA:  2s | MH Acceptance: 0.78
#> ■■■■■■■■■■                        31% | ETA:  1s | MH Acceptance: 0.78
#> ■■■■■■■■■■■■■                     41% | ETA:  1s | MH Acceptance: 0.79
#> ■■■■■■■■■■■■■■■■                  51% | ETA:  1s | MH Acceptance: 0.78
#> ■■■■■■■■■■■■■■■■■■■               61% | ETA:  1s | MH Acceptance: 0.78
#> ■■■■■■■■■■■■■■■■■■■■■■■           72% | ETA:  1s | MH Acceptance: 0.78
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■        82% | ETA:  0s | MH Acceptance: 0.77
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■     92% | ETA:  0s | MH Acceptance: 0.77
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s | MH Acceptance: 0.77
#> 


## Get Republican Dissimilarity Index from simulations
# old: rep_dmi_253 <- redist.segcalc(alg_253, fl25$mccain, fl25$pop)
rep_dmi_253 <- seg_dissim(alg_253, fl25, mccain, pop)  |>
    redistmetrics::by_plan(ndists = 3)
# }