Add locked in constraints

Source: R/add_locked_in_constraints.R

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 ConservationProblem,numeric
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,logical
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,matrix
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,character
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,Spatial
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,sf
add_locked_in_constraints(x, locked_in)

# S4 method for ConservationProblem,Raster
add_locked_in_constraints(x, locked_in)

Arguments

x

problem() (i.e. ConservationProblem) object.
locked_in

Object that determines which planning units that should be locked in. See the Data format section for more information.

Value

Object (i.e. ConservationProblem) with the constraints added to it.

Data format

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.

data as an integer vector

containing indices that indicate which planning units should be locked for the solution. This argument is only compatible with problems that contain a single zone.

data as a 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.

data as a matrix object

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.

data as a character vector

containing field (column) name(s) that indicate if planning units should be locked for the solution. This format 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 that contain a single zone, the argument to data must contain a single field name. Otherwise, for problems that contain multiple zones, the argument to data must contain a field name for each zone.

data as a Spatial or sf::sf() object

containing geometries that will be used to lock planning units for the solution. Specifically, planning units in x that spatially intersect with y will be locked (per intersecting_units()). Note that this option is only available for problems that contain a single management zone.

data as a Raster object

containing cells used to lock planning units for the solution. Specifically, planning units in x that intersect with cells that have non-zero and non-NA values are locked. 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.

See also

See constraints for an overview of all functions for adding constraints.

Other constraints: add_contiguity_constraints(), add_feature_contiguity_constraints(), add_linear_constraints(), add_locked_out_constraints(), add_mandatory_allocation_constraints,ConservationProblem-method, add_manual_bounded_constraints(), add_manual_locked_constraints()

Examples

# 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)
# \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 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))

# }

# 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)
# \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 multi-zone problem with locked in constraints using field names
p8 <- p6 %>% add_locked_in_constraints(c("locked_1", "locked_2", "locked_3"))
# \dontrun{
# 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)

# }
# 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
# \dontrun{
plot(locked_in_stack)

# }
# add locked in raster units to problem
p9 <- p9 %>% add_locked_in_constraints(locked_in_stack)

# \dontrun{
# solve problem
s9 <- solve(p9)

# plot solution
plot(category_layer(s9), main = "solution", axes = FALSE, box = FALSE)

# }