Add constraints to a conservation planning problem()
to ensure
that specific planning units are not selected
(or allocated to a specific zone) in the solution. For example, it may be
useful to lock out planning units that have been degraded and are not
suitable for conserving species. If specific planning units should be locked
in to the solution, use add_locked_out_constraints()
. For
problems with nonbinary planning unit allocations (e.g. proportions), the
add_manual_locked_constraints()
function can be used to lock
planning unit allocations to a specific value.
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,numeric
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,logical
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,matrix
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,character
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,Spatial
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,sf
add_locked_out_constraints(x, locked_out)
# S4 method for ConservationProblem,Raster
add_locked_out_constraints(x, locked_out)
x 


locked_out  Object that determines which planning units that should be locked out. See the Data format section for more information. 
Object (i.e. ConservationProblem
) with the constraints
added to it.
The locked planning units can be specified using the following formats.
Generally, the locked data should correspond to the planning units
in the argument to x.
To help make working with
Raster
planning unit data easier,
the locked data should correspond to cell indices in the
Raster
data. For example, integer
arguments
should correspond to cell indices and logical
arguments should have
a value for each cellregardless of which planning unit cells contain
NA
values.
integer
vector
of indices pertaining to which
planning units should be locked for the solution. This argument is only
compatible with problems that contain a single zone.
logical
vector
containing TRUE
and/or
FALSE
values that indicate which planning units should be locked
in the solution. This argument is only compatible with problems that
contain a single zone.
matrix
containing logical
TRUE
and/or
FALSE
values which indicate if certain planning units are
should be locked to a specific zone in the solution. Each row
corresponds to a planning unit, each column corresponds to a zone, and
each cell indicates if the planning unit should be locked to a given
zone. Thus each row should only contain at most a single TRUE
value.
character
field (column) name(s) that indicate if planning
units should be locked for the solution. This type of argument is only
compatible if the planning units in the argument to x
are a
Spatial
, sf::sf()
, or
data.frame
object. The fields
(columns) must have logical
(i.e. TRUE
or FALSE
)
values indicating if the planning unit is to be locked for the solution.
For problems containing multiple zones, this argument should contain
a field (column) name for each management zone.
Spatial
or sf::sf()
planning units in x
that spatially intersect with the
argument to y
(according to intersecting_units()
are locked for to the solution. Note that this option is only available
for problems that contain a single management zone.
Raster
planning units in x
that intersect with nonzero and nonNA
raster cells are locked
for the solution. For problems that contain multiple zones, the
Raster
object must contain a layer
for each zone. Note that for multiband arguments, each pixel must
only contain a nonzero value in a single band. Additionally, if the
cost data in x
is a Raster
object, we
recommend standardizing NA
values in this dataset with the cost
data. In other words, the pixels in x
that have NA
values
should also have NA
values in the locked data.
# set seed for reproducibility
set.seed(500)
# load data
data(sim_pu_polygons, sim_features, sim_locked_out_raster)
# create minimal problem
p1 < problem(sim_pu_polygons, sim_features, "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.2) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create problem with added locked out constraints using integers
p2 < p1 %>% add_locked_out_constraints(which(sim_pu_polygons$locked_out))
# create problem with added locked out constraints using a field name
p3 < p1 %>% add_locked_out_constraints("locked_out")
# create problem with added locked out constraints using raster data
p4 < p1 %>% add_locked_out_constraints(sim_locked_out_raster)
# create problem with added locked out constraints using spatial polygon data
locked_out < sim_pu_polygons[sim_pu_polygons$locked_out == 1, ]
p5 < p1 %>% add_locked_out_constraints(locked_out)
# \dontrun{
# solve problems
s1 < solve(p1)
s2 < solve(p2)
s3 < solve(p3)
s4 < solve(p4)
s5 < solve(p5)
# plot solutions
par(mfrow = c(3,2), mar = c(0, 0, 4.1, 0))
plot(s1, main = "none locked out")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s2, main = "locked out (integer input)")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s3, main = "locked out (character input)")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s4, main = "locked out (raster input)")
plot(s4[s4$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s5, main = "locked out (polygon input)")
plot(s5[s5$solution_1 == 1, ], col = "darkgreen", add = TRUE)
# reset plot
par(mfrow = c(1, 1))
# }
# create minimal multizone problem with spatial data
p6 < problem(sim_pu_zones_polygons, sim_features_zones,
cost_column = c("cost_1", "cost_2", "cost_3")) %>%
add_min_set_objective() %>%
add_absolute_targets(matrix(rpois(15, 1), nrow = 5, ncol = 3)) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create multizone problem with locked out constraints using matrix data
locked_matrix < sim_pu_zones_polygons@data[, c("locked_1", "locked_2",
"locked_3")]
locked_matrix < as.matrix(locked_matrix)
p7 < p6 %>% add_locked_out_constraints(locked_matrix)
# \dontrun{
# solve problem
s6 < solve(p6)
# create new column representing the zone id that each planning unit
# was allocated to in the solution
s6$solution < category_vector(s6@data[, c("solution_1_zone_1",
"solution_1_zone_2",
"solution_1_zone_3")])
s6$solution < factor(s6$solution)
# plot solution
spplot(s6, zcol = "solution", main = "solution", axes = FALSE, box = FALSE)
# }
# create multizone problem with locked out constraints using field names
p8 < p6 %>% add_locked_out_constraints(c("locked_1", "locked_2",
"locked_3"))
# \dontrun{
# solve problem
s8 < solve(p8)
# create new column in s8 representing the zone id that each planning unit
# was allocated to in the solution
s8$solution < category_vector(s8@data[, c("solution_1_zone_1",
"solution_1_zone_2",
"solution_1_zone_3")])
s8$solution[s8$solution == 1 & s8$solution_1_zone_1 == 0] < 0
s8$solution < factor(s8$solution)
# plot solution
spplot(s8, zcol = "solution", main = "solution", axes = FALSE, box = FALSE)
# }
# create multizone problem with raster planning units
p9 < problem(sim_pu_zones_stack, sim_features_zones) %>%
add_min_set_objective() %>%
add_absolute_targets(matrix(rpois(15, 1), nrow = 5, ncol = 3)) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create raster stack with locked out units
locked_out_stack < sim_pu_zones_stack[[1]]
locked_out_stack[!is.na(locked_out_stack)] < 0
locked_out_stack < locked_out_stack[[c(1, 1, 1)]]
locked_out_stack[[1]][1] < 1
locked_out_stack[[2]][2] < 1
locked_out_stack[[3]][3] < 1
# plot locked out stack
# \dontrun{
plot(locked_out_stack)
# }
# add locked out raster units to problem
p9 < p9 %>% add_locked_out_constraints(locked_out_stack)
# \dontrun{
# solve problem
s9 < solve(p9)
# plot solution
plot(category_layer(s9), main = "solution", axes = FALSE, box = FALSE)
# }