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)
Arguments
| x |
|
|---|
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
Examples
# 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) # \dontrun{ # 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) # \dontrun{ # 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 : layer.1.1, layer.1.2, layer.1.3 #> min values : 0, 0, 0 #> max values : 1, 1, 1 #># }
