R/add_manual_locked_constraints.R
add_manual_locked_constraints.Rd
Add constraints to a conservation planning problem()
to ensure
that solutions allocate (or do not allocate) specific planning units to
specific management zones. This function offers more fine-grained control
than the add_locked_in_constraints()
and
add_locked_out_constraints()
functions.
add_manual_locked_constraints(x, data)
# S4 method for ConservationProblem,data.frame
add_manual_locked_constraints(x, data)
# S4 method for ConservationProblem,tbl_df
add_manual_locked_constraints(x, data)
problem()
(i.e., ConservationProblem
) object.
data.frame
or tibble::tibble()
object.
See the Data format section for more information.
Object (i.e., ConservationProblem
) with the constraints
added to it.
The argument to data
should be a data.frame
with the following fields
(columns):
integer
planning unit identifier.
character
names of zones. Note that this
argument is optional for arguments to x
that contain a single
zone.
numeric
values indicating how much
of each planning unit should be allocated to each zone in the solution.
For example, the numeric
values could be binary values (i.e., zero
or one) for problems containing binary-type decision variables
(using the add_binary_decisions()
function). Alternatively,
the numeric
values could be proportions (e.g., 0.5) for problems
containing proportion-type decision variables (using the
add_proportion_decisions()
).
See constraints for an overview of all functions for adding constraints.
Other constraints:
add_contiguity_constraints()
,
add_feature_contiguity_constraints()
,
add_linear_constraints()
,
add_locked_in_constraints()
,
add_locked_out_constraints()
,
add_mandatory_allocation_constraints,ConservationProblem-method
,
add_manual_bounded_constraints()
,
add_neighbor_constraints()
# set seed for reproducibility
set.seed(500)
# load data
data(sim_pu_polygons, sim_features, sim_pu_zones_polygons,
sim_features_zones)
# create minimal problem
p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.2) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create problem with locked in constraints using add_locked_constraints
p2 <- p1 %>% add_locked_in_constraints("locked_in")
# create identical problem using add_manual_locked_constraints
locked_data <- data.frame(pu = which(sim_pu_polygons$locked_in),
status = 1)
p3 <- p1 %>% add_manual_locked_constraints(locked_data)
# \dontrun{
# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)
# plot solutions
par(mfrow = c(1,3), mar = c(0, 0, 4.1, 0))
plot(s1, main = "none locked in")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s2, main = "add_locked_in_constraints")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s3, main = "add_manual_constraints")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)
# }
# create minimal problem with multiple zones
p4 <- problem(sim_pu_zones_polygons, sim_features_zones,
c("cost_1", "cost_2", "cost_3")) %>%
add_min_set_objective() %>%
add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
ncol = 3)) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create data.frame with the following constraints:
# planning units 1, 2, and 3 must be allocated to zone 1 in the solution
# planning units 4, and 5 must be allocated to zone 2 in the solution
# planning units 8 and 9 must not be allocated to zone 3 in the solution
locked_data2 <- data.frame(pu = c(1, 2, 3, 4, 5, 8, 9),
zone = c(rep("zone_1", 3), rep("zone_2", 2),
rep("zone_3", 2)),
status = c(rep(1, 5), rep(0, 2)))
# print locked constraint data
print(locked_data2)
#> pu zone status
#> 1 1 zone_1 1
#> 2 2 zone_1 1
#> 3 3 zone_1 1
#> 4 4 zone_2 1
#> 5 5 zone_2 1
#> 6 8 zone_3 0
#> 7 9 zone_3 0
# create problem with added constraints
p5 <- p4 %>% add_manual_locked_constraints(locked_data2)
# \dontrun{
# solve problem
s4 <- solve(p4)
s5 <- solve(p5)
# create two new columns representing the zone id that each planning unit
# was allocated to in the two solutions
s4$solution <- category_vector(s4@data[, c("solution_1_zone_1",
"solution_1_zone_2",
"solution_1_zone_3")])
s4$solution <- factor(s4$solution)
s4$solution_locked <- category_vector(s5@data[, c("solution_1_zone_1",
"solution_1_zone_2",
"solution_1_zone_3")])
s4$solution_locked <- factor(s4$solution_locked)
# plot solutions
spplot(s4, zcol = c("solution", "solution_locked"), axes = FALSE,
box = FALSE)
# }