Add locked in constraints
Add constraints to a conservation planning problem() to ensure
that specific planning units are selected (or allocated
to a specific zone) in the solution. For example, it may be desirable to
lock in planning units that are inside existing protected areas so that the
solution fills in the gaps in the existing reserve network. If specific
planning units should be locked out of a solution, use
add_locked_out_constraints(). For problems with non-binary
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_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,numeric' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,logical' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,matrix' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,character' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,Spatial' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,sf' add_locked_in_constraints(x, locked_in) ## S4 method for signature 'ConservationProblem,Raster' add_locked_in_constraints(x, locked_in)
x |
|
locked_in |
Object that determines which planning units that should be locked in. 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 cell—regardless of which planning unit cells contain
NA values.
integervector 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.
logicalvector 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.
matrixcontaining 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.
characterfield (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.
Rasterplanning units in x
that intersect with non-zero and non-NA 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 multi-band arguments, each pixel must
only contain a non-zero 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_in_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 in constraints using integers
p2 <- p1 %>% add_locked_in_constraints(which(sim_pu_polygons$locked_in))
# create problem with added locked in constraints using a field name
p3 <- p1 %>% add_locked_in_constraints("locked_in")
# create problem with added locked in constraints using raster data
p4 <- p1 %>% add_locked_in_constraints(sim_locked_in_raster)
# create problem with added locked in constraints using spatial polygon data
locked_in <- sim_pu_polygons[sim_pu_polygons$locked_in == 1, ]
p5 <- p1 %>% add_locked_in_constraints(locked_in)
## Not run:
# 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 in")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s2, main = "locked in (integer input)")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s3, main = "locked in (character input)")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s4, main = "locked in (raster input)")
plot(s4[s4$solution_1 == 1, ], col = "darkgreen", add = TRUE)
plot(s5, main = "locked in (polygon input)")
plot(s5[s5$solution_1 == 1, ], col = "darkgreen", add = TRUE)
# reset plot
par(mfrow = c(1, 1))
## End(Not run)
# create minimal multi-zone problem with spatial data
p6 <- problem(sim_pu_zones_polygons, sim_features_zones,
cost_column = c("cost_1", "cost_2", "cost_3")) %>%
add_min_set_objective() %>%
add_absolute_targets(matrix(rpois(15, 1), nrow = 5,
ncol = 3)) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create multi-zone problem with locked in constraints using matrix data
locked_matrix <- sim_pu_zones_polygons@data[, c("locked_1", "locked_2",
"locked_3")]
locked_matrix <- as.matrix(locked_matrix)
p7 <- p6 %>% add_locked_in_constraints(locked_matrix)
## Not run:
# solve problem
s6 <- solve(p6)
# create new column representing the zone id that each planning unit
# was allocated to in the solution
s6$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)
## End(Not run)
# create multi-zone problem with locked in constraints using field names
p8 <- p6 %>% add_locked_in_constraints(c("locked_1", "locked_2", "locked_3"))
## Not run:
# solve problem
s8 <- solve(p8)
# create new column 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)
## End(Not run)
# create multi-zone 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 in units
locked_in_stack <- sim_pu_zones_stack[[1]]
locked_in_stack[!is.na(locked_in_stack)] <- 0
locked_in_stack <- locked_in_stack[[c(1, 1, 1)]]
locked_in_stack[[1]][1] <- 1
locked_in_stack[[2]][2] <- 1
locked_in_stack[[3]][3] <- 1
# plot locked in stack
## Not run:
plot(locked_in_stack)
## End(Not run)
# add locked in raster units to problem
p9 <- p9 %>% add_locked_in_constraints(locked_in_stack)
## Not run:
# solve problem
s9 <- solve(p9)
# plot solution
plot(category_layer(s9), main = "solution", axes = FALSE, box = FALSE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.