add_relative_targets | R Documentation |
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 values) using the feature_abundances()
function.
add_relative_targets(x, targets)
## S4 method for signature 'ConservationProblem,numeric'
add_relative_targets(x, targets)
## S4 method for signature 'ConservationProblem,matrix'
add_relative_targets(x, targets)
## S4 method for signature 'ConservationProblem,character'
add_relative_targets(x, targets)
x |
|
targets |
Object that specifies the targets for each feature. See the Targets format section for more information. |
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,
add_absolute_targets()
can
be used to set targets that each pertain to a single feature and a single
zone. To set targets that 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.
An updated problem()
object with the targets added to it.
The targets
for a problem can be specified using the following formats.
targets
as a numeric
vectorcontaining target values for each feature. Additionally, for convenience, this format can be a single value to assign the same target to each feature. Note that this format cannot be used to specify targets for problems with multiple zones.
targets
as a matrix
objectcontaining 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 to
x
, and each cell contains the target value for a given feature
that the solution needs to secure in a given zone.
targets
as a character
vectorcontaining the column name(s) in the
feature data associated with the argument to x
that
contain targets. This format can only be used when the
feature data associated with x
is a sf::st_sf()
or data.frame
.
For problems that contain a single zone, the argument to targets
must
contain a single column name. Otherwise, for problems that
contain multiple zones, the argument to targets
must
contain a column name for each zone.
See targets for an overview of all functions for adding targets.
Other targets:
add_absolute_targets()
,
add_loglinear_targets()
,
add_manual_targets()
## Not run:
# set seed for reproducibility
set.seed(500)
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_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)
# solve problem
s3 <- c(solve(p1), solve(p2))
names(s3) <- c("10% targets", "varying targets")
# plot solution
plot(s3, main = , axes = FALSE)
# create a problem with multiple management zones
p4 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
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_zones_features), zone_names(sim_zones_features)
)
)
print(p4_targets)
p5 <- p4 %>% add_relative_targets(p4_targets)
# solve problem
s5 <- solve(p5)
# plot solution (pixel values correspond to zone identifiers)
plot(category_layer(s5), main = "equal targets")
# create a problem with targets that require a varying amount of each
# feature to be represented in each zone
p6_targets <- matrix(
runif(15, 0.01, 0.2), nrow = 5, ncol = 3, dimnames = list(
feature_names(sim_zones_features), zone_names(sim_zones_features)
)
)
print(p6_targets)
p6 <- p4 %>% add_relative_targets(p6_targets)
# solve problem
s6 <- solve(p6)
# plot solution (pixel values correspond to zone identifiers)
plot(category_layer(s6), main = "varying targets")
## End(Not run)
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