Add binary decisions

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	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

# 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)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 

# 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)
#> $LogToConsole
#> [1] 0
#> 
#> $LogFile
#> [1] ""
#> 
#> $Presolve
#> [1] 2
#> 
#> $MIPGap
#> [1] 0.1
#> 
#> $TimeLimit
#> [1] 2147483647
#> 
#> $Threads
#> [1] 1
#> 
#> $NumericFocus
#> [1] 0
#> 

# 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)
# }