prioritizr (version 4.0.2)

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

x

ConservationProblem-class object.

locked_out

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

Examples

# 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
plot(locked_out_stack)

# 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)
# }