The redist_smc() and redist_mergesplit() algorithms in this package allow
for additional constraints on the redistricting process to be encoded in the
target distribution for sampling. These functions are provided to specify
these constraints. All arguments are quoted and evaluated in the context of
the data frame provided to redist_constr().
add_constr_status_quo(constr, strength, current)add_constr_grp_pow(
constr,
strength,
group_pop,
total_pop = NULL,
tgt_group = 0.5,
tgt_other = 0.5,
pow = 1
)
add_constr_grp_hinge(
constr,
strength,
group_pop,
total_pop = NULL,
tgts_group = c(0.55)
)
add_constr_grp_inv_hinge(
constr,
strength,
group_pop,
total_pop = NULL,
tgts_group = c(0.55)
)
add_constr_compet(constr, strength, dvote, rvote, pow = 0.5)
add_constr_incumbency(constr, strength, incumbents)
add_constr_splits(constr, strength, admin)
add_constr_multisplits(constr, strength, admin)
add_constr_total_splits(constr, strength, admin)
add_constr_pop_dev(constr, strength)
add_constr_segregation(constr, strength, group_pop, total_pop = NULL)
add_constr_polsby(constr, strength, perim_df = NULL)
add_constr_fry_hold(
constr,
strength,
total_pop = NULL,
ssdmat = NULL,
denominator = 1
)
add_constr_log_st(constr, strength, admin = NULL)
add_constr_edges_rem(constr, strength)
add_constr_custom(constr, strength, fn)
A redist_constr() object
The strength of the constraint. Higher values mean a more restrictive constraint.
The reference map for the status quo constraint.
A vector of group population
A vector of total population. Defaults to the population vector used for sampling.
Target group shares for the power-type constraint.
The exponent for the power-type constraint.
A vector of target group shares for the hinge-type constraint.
A vector of Democratic or Republican vote counts
A vector of unit indices for incumbents. For example, if three incumbents live in the precincts that correspond to rows 1, 2, and 100 of your redist_map, entering incumbents = c(1, 2, 100) would avoid having two or more incumbents be in the same district.
A vector indicating administrative unit membership
A dataframe output from redistmetrics::prep_perims
Squared distance matrix for Fryer Holden constraint
Fryer Holden minimum value to normalize by. Default is 1 (no normalization).
A function
All constraints are fed into a Gibbs measure, with coefficients on each
constraint set by the corresponding strength parameter.
The strength can be any real number, with zero corresponding to no constraint.
Higher and higher strength values will eventually cause the algorithm's
accuracy and efficiency to suffer. Whenever you use constraints, be sure to
check all sampling diagnostics.
The status_quo constraint adds a term measuring the variation of
information distance between the plan and the reference, rescaled to [0, 1].
The grp_hinge constraint takes a list of target group percentages. It
matches each district to its nearest target percentage, and then applies a
penalty of the form \(\sqrt{max(0, tgt - grouppct)}\), summing across
districts. This penalizes districts which are below their target percentage.
Use plot.redist_constr() to visualize the effect of this constraint and
calibrate strength appropriately.
The grp_inv_hinge constraint takes a list of target group percentages. It
matches each district to its nearest target percentage, and then applies a
penalty of the form \(\sqrt{max(0, grouppct - tgt)}\), summing across
districts. This penalizes districts which are above their target percentage.
Use plot.redist_constr() to visualize the effect of this constraint and
calibrate strength appropriately.
The grp_pow constraint (for expert use) adds a term of the form
\((|tgtgroup-grouppct||tgtother-grouppct|)^{pow})\), which
encourages districts to have group shares near either tgt_group
or tgt_other. Values of strength depend heavily on the values of these
parameters and especially the pow parameter.
Use plot.redist_constr() to visualize the effect of this constraint and
calibrate strength appropriately.
The compet constraint encourages competitiveness by applying the grp_pow
constraint with target percentages set to 50%. For convenience, it is
specified with Democratic and Republican vote shares.
The incumbency constraint adds a term counting the number of districts
containing paired-up incumbents.
Values of strength should generally be small, given that the underlying values are counts.
The splits constraint adds a term counting the number of
counties which are split once or more.
Values of strength should generally be small, given that the underlying values are counts.
The multisplits constraint adds a term counting the number of
counties which are split twice or more.
Values of strength should generally be small, given that the underlying values are counts.
The total_splits constraint adds a term counting the total number of times
each county is split, summed across counties (i.e., counting the number of
excess district-county pairs). Values of strength should generally be
small, given that the underlying values are counts.
The edges_rem constraint adds a term counting the number of edges removed from the
adjacency graph. This is only usable with redist_flip(), as other algorithms
implicitly use this via the compactness parameter. Values of strength should
generally be small, given that the underlying values are counts.
The log_st constraint constraint adds a term counting the log number of spanning
trees. This is only usable with redist_flip(), as other algorithms
implicitly use this via the compactness parameter.
The polsby constraint adds a term encouraging compactness as defined by the
Polsby Popper metric. Values of strength may be of moderate size.
The fry_hold constraint adds a term encouraging compactness as defined by the
Fryer Holden metric. Values of strength should be extremely small, as the
underlying values are massive when the true minimum Fryer Holden denominator is not known.
The segregation constraint adds a term encouraging segregation among minority groups,
as measured by the dissimilarity index.
The pop_dev constraint adds a term encouraging plans to have smaller population deviations
from the target population.
The custom constraint allows the user to specify their own constraint using
a function which evaluates districts one at a time. The provided function
fn should take two arguments: a vector describing the current plan
assignment for each unit as its first argument, and an integer describing the
district which to evaluate in the second argument. which([plans == distr])
would give the indices of the units that are assigned to a district distr
in any iteration. The function must return a single scalar for each plan -
district combination, where a value of 0 indicates no penalty is applied. If
users want to penalize an entire plan, they can have the penalty function
return a scalar that does not depend on the district. It is important that
fn not use information from precincts not included in distr, since in the
case of SMC these precincts may not be assigned any district at all (plan
will take the value of 0 for these precincts). The flexibility of this
constraint comes with an additional computational cost, since the other
constraints are written in C++ and so are more performant.
data(iowa)
iowa_map <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.05)
constr <- redist_constr(iowa_map)
constr <- add_constr_splits(constr, strength = 1.5, admin = name)
constr <- add_constr_grp_hinge(constr, strength = 100,
dem_08, tot_08, tgts_group = c(0.5, 0.6))
# encourage districts to have the same number of counties
constr <- add_constr_custom(constr, strength = 1000, fn = function(plan, distr) {
# notice that we only use information on precincts in `distr`
abs(sum(plan == distr) - 99/4)
})
print(constr)
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