prioritizr (version 5.0.3)

add_binary_decisions: Add binary decisions

Description

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.

Usage

add_binary_decisions(x)

Arguments

x

problem() (i.e. '>ConservationProblem) object.

Value

Object (i.e. '>ConservationProblem) with the decisions added to it.

Details

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). If no decision is explicitly added to a problem, then the binary decision class will be used by default. 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 Also

decisions.

Examples

# NOT RUN {
# 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()
# }
# NOT RUN {
# solve problem
s1 <- solve(p1)

# plot solution
plot(s1, main = "solution")
# }
# NOT RUN {
# 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()
# }
# NOT RUN {
# solve the problem
s2 <- solve(p2)

# print solution
print(s2)

# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE, box = FALSE)
# }