Add a binary decision to a conservation planning problem()
.
This is the classic decision of either prioritizing or not prioritizing a
planning unit. Typically, this decision has the assumed action of buying
the planning unit to include in a protected area network. If no decision is
added to a problem then this decision class will be used by default.
add_binary_decisions(x)
problem()
(i.e., ConservationProblem
) object.
Object (i.e., ConservationProblem
) with the decisions added
to it.
Conservation planning problems involve making decisions on planning
units. These decisions are then associated with actions (e.g., turning a
planning unit into a protected area). Only a
single decision should be added to a ConservationProblem
object.
Note that if multiple decisions are added to a problem object, then the
last one to be added will be used.
See decisions for an overview of all functions for adding decisions.
Other decisions:
add_default_decisions()
,
add_proportion_decisions()
,
add_semicontinuous_decisions()
# \dontrun{
# set seed for reproducibility
set.seed(500)
# load data
data(sim_pu_raster, sim_features, sim_pu_zones_stack, sim_features_zones)
# create minimal problem with binary decisions
p1 <- problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# solve problem
s1 <- solve(p1)
# plot solution
plot(s1, main = "solution")
# build multi-zone conservation problem with binary decisions
p2 <- problem(sim_pu_zones_stack, sim_features_zones) %>%
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)
# solve the problem
s2 <- solve(p2)
# print solution
print(s2)
#> class : RasterStack
#> dimensions : 10, 10, 100, 3 (nrow, ncol, ncell, nlayers)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
#> crs : NA
#> names : zone_1, zone_2, zone_3
#> min values : 0, 0, 0
#> max values : 1, 1, 1
#>
# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE, box = FALSE)
# }