Set targets as a proportion (between 0 and 1) of the maximum level of
representation of features in the study area. Please note that proportions
are scaled according to the features' total abundances in the study area
(including any locked out planning units, or planning units with NA
cost data) using the feature_abundances() function.
add_relative_targets(x, targets) # S4 method for ConservationProblem,numeric add_relative_targets(x, targets) # S4 method for ConservationProblem,matrix add_relative_targets(x, targets) # S4 method for ConservationProblem,character add_relative_targets(x, targets)
Arguments
| x |
|
|---|---|
| targets | Object that specifies the targets for each feature. See the Targets data format section for more information. |
Value
Object (i.e. ConservationProblem) with the targets added
to it.
Details
Targets are used to specify the minimum amount or proportion of a
feature's distribution that needs to be protected. Most conservation
planning problems require targets with the exception of the maximum cover
(see add_max_cover_objective()) and maximum utility
(see add_max_utility_objective()) problems. Attempting to solve
problems with objectives that require targets without specifying targets
will throw an error.
For problems associated with multiple management zones, this function can
be used to set targets that each pertain to a single feature and a single
zone. To set targets which can be met through allocating different
planning units to multiple zones, see the add_manual_targets()
function. An example of a target that could be met through allocations
to multiple zones might be where each management zone is expected to
result in a different amount of a feature and the target requires that
the total amount of the feature in all zones must exceed a certain
threshold. In other words, the target does not require that any single
zone secure a specific amount of the feature, but the total amount held
in all zones must secure a specific amount. Thus the target could,
potentially, be met through allocating all planning units to any specific
management zone, or through allocating the planning units to different
combinations of management zones.
Targets data format
The targets for a problem can be specified using the following formats.
numericvectorof target values for each feature. Additionally, for convenience, this type of argument can be a single value to assign the same target to each feature. Note that this type of argument cannot be used to specify targets for problems with multiple zones.matrixcontaining a target for each feature in each zone. Here, each row corresponds to a different feature in argument to
x, each column corresponds to a different zone in argument tox, and each cell contains the target value for a given feature that the solution needs to secure in a given zone.charactercontaining the names of fields (columns) in the feature data associated with the argument to
xthat contain targets. This type of argument can only be used when the feature data associated withxis adata.frame. This argument must contain a field (column) name for each zone.
See also
Examples
# set seed for reproducibility set.seed(500) # load data data(sim_pu_raster, sim_features) # create base problem p <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # create problem with 10% targets p1 <- p %>% add_relative_targets(0.1) # create problem with varying targets for each feature targets <- c(0.1, 0.2, 0.3, 0.4, 0.5) p2 <- p %>% add_relative_targets(targets) # \dontrun{ # solve problem s <- stack(solve(p1), solve(p2))#> $LogToConsole #> [1] 0 #> #> $LogFile #> [1] "" #> #> $Presolve #> [1] 2 #> #> $MIPGap #> [1] 0.1 #> #> $TimeLimit #> [1] 2147483647 #> #> $Threads #> [1] 1 #> #> $NumericFocus #> [1] 0 #> #> $LogToConsole #> [1] 0 #> #> $LogFile #> [1] "" #> #> $Presolve #> [1] 2 #> #> $MIPGap #> [1] 0.1 #> #> $TimeLimit #> [1] 2147483647 #> #> $Threads #> [1] 1 #> #> $NumericFocus #> [1] 0 #># } # create a problem with multiple management zones p3 <- problem(sim_pu_zones_stack, sim_features_zones) %>% add_min_set_objective() %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # create a problem with targets that specify an equal amount of each feature # to be represented in each zone p4_targets <- matrix(0.1, nrow = 5, ncol = 3, dimnames = list(feature_names(sim_features_zones), zone_names(sim_features_zones))) print(p4_targets)#> zone_1 zone_2 zone_3 #> feature_1 0.1 0.1 0.1 #> feature_2 0.1 0.1 0.1 #> feature_3 0.1 0.1 0.1 #> feature_4 0.1 0.1 0.1 #> feature_5 0.1 0.1 0.1p4 <- p3 %>% add_relative_targets(p4_targets) # solve problem # \dontrun{ # solve problem s4 <- solve(p4)#> $LogToConsole #> [1] 0 #> #> $LogFile #> [1] "" #> #> $Presolve #> [1] 2 #> #> $MIPGap #> [1] 0.1 #> #> $TimeLimit #> [1] 2147483647 #> #> $Threads #> [1] 1 #> #> $NumericFocus #> [1] 0 #># plot solution (pixel values correspond to zone identifiers) plot(category_layer(s4), main = c("equal targets"))# } # create a problem with targets that require a varying amount of each # feature to be represented in each zone p5_targets <- matrix(runif(15, 0.01, 0.2), nrow = 5, ncol = 3, dimnames = list(feature_names(sim_features_zones), zone_names(sim_features_zones))) print(p5_targets)#> zone_1 zone_2 zone_3 #> feature_1 0.16838399 0.04908221 0.06359158 #> feature_2 0.14775224 0.10731456 0.17964011 #> feature_3 0.19530969 0.18583855 0.15529418 #> feature_4 0.09884473 0.16747797 0.04122594 #> feature_5 0.16433284 0.14519964 0.14909414p5 <- p3 %>% add_relative_targets(p4_targets) # solve problem # \dontrun{ # solve problem s5 <- solve(p5)#> $LogToConsole #> [1] 0 #> #> $LogFile #> [1] "" #> #> $Presolve #> [1] 2 #> #> $MIPGap #> [1] 0.1 #> #> $TimeLimit #> [1] 2147483647 #> #> $Threads #> [1] 1 #> #> $NumericFocus #> [1] 0 #># plot solution (pixel values correspond to zone identifiers) plot(category_layer(s5), main = c("varying targets"))# }
