add_locked_out_constraints: Add locked out constraints

Description

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 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.

Usage

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,Raster
add_locked_out_constraints(x, locked_out)

Arguments

ConservationProblem-class object.

locked_out

Object that determines which planning units that should be locked out. See the Details section for more information.

Value

ConservationProblem-class object with the constraints added to it.

Details

The locked planning units can be specified in several different ways. Generally, the locked data should correspond to the planning units in the argument to x. To help make working with Raster-class planning unit data easier, the locked data should correspond to cell indices in the Raster-class 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.

integer: vector of indices pertaining to which planning units should be locked in 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 in the solution. This type of argument is only compatible if the planning units in the argument to x are a Spatial-class 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 in the solution. For problems containing multiple zones, this argument should contain a field (column) name for each management zone.
Raster-class: planning units in x that intersect with non-zero and non-NA raster cells are locked in the solution. For problems that contain multiple zones, the Raster-class 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-class 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.

Examples

Run this code

# NOT RUN {
# 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()

# 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)
# }
# 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 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)
# }
# 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()

# create multi-zone 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)
# }
# 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)
# }
# NOT RUN {
# create multi-zone problem with locked out constraints using field names
p8 <- p6 %>% add_locked_out_constraints(c("locked_1", "locked_2",
                                          "locked_3"))
# }
# NOT RUN {
# 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)
# }
# 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()

# 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
# }
# NOT RUN {
plot(locked_out_stack)
# }
# NOT RUN {
# add locked out raster units to problem
p9 <- p9 %>% add_locked_out_constraints(locked_out_stack)

# }
# NOT RUN {
# solve problem
s9 <- solve(p9)

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

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning