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Add locked out constraints

Source: R/add_locked_out_constraints.R
add_locked_out_constraints.Rd

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_in_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 class 'ConservationProblem,numeric'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,logical'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,matrix'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,character'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,Spatial'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,sf'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,Raster'
add_locked_out_constraints(x, locked_out)

# S4 method for class 'ConservationProblem,SpatRaster'
add_locked_out_constraints(x, locked_out)

Arguments

x

problem() object.

locked_out

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

Value

An updated problem() object with the constraints added to it.

Data format

The following formats can be used to specify locked_out.

locked_out as a numeric vector

Here numeric values are used to specify which planning units should be locked for the solution. If x has data.frame planning units, then these values must refer to values in the id column of the planning unit data. Alternatively, if x has sf::st_sf() or matrix planning units, then these values must refer to the row numbers of the planning unit data. Additionally, if x has numeric vector planning units, then these values must refer to the element indices of the planning unit data. Finally, if x has terra::rast() planning units, then these values must refer to cell indices. Note that this format is only compatible if x has a single zone.

locked_out as a logical vector

Here TRUE/FALSE values are used to specify each if planning unit should be locked for the solution. Note that x should have a TRUE or FALSE value for planning unit in x. Note that this format is only compatible if x has a single zone.

locked_out as a matrix object

Here TRUE/FALSE values are used to specify each if each planning unit should be locked to a particular zone for 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.

locked_out as a character vector

Here column name(s) for the planning unit data in x are used to specify if planning units should be locked for the solution. This format is only compatible if x has planning units in sf::st_sf() or data.frame format. These columns must have logical (i.e., TRUE/FALSE) values indicating if planning units should be locked for the solution. If x has a single zone, locked_out must contain a single column name. Otherwise, if x has multiple zones, locked_out must contain a column name for each zone.

locked_out as a sf::sf() object

Here geometries of locked_out are used to specify which planning units should be locked for the solution. Specifically, planning units in x that spatially intersect with locked_out will be locked (per intersecting_units()). Note that this option is only compatible if x has a single zone.

locked_out as a terra::rast() object

Here the cells in locked_out are used to lock planning units for the solution. Specifically, planning units in x that intersect with cells in locked_out that have non-zero and non-missing (NA) values will be locked. If x has a single zone, then locked_out must have a single layer. Otherwise, if x has multiple zones, then locked_out must have a layer for each zone. Note that if locked_out has multiple layers, each cell must only contain a non-zero value in a single layer. Additionally, if the planning unit data in x is a terra::rast() object, we recommend standardizing missing (NA) values in locked_out with them to ensure that missing (NA) are consistent across both objects.

See also

Other functions for adding constraints: add_contiguity_constraints(), add_cost_constraints(), add_feature_contiguity_constraints(), add_linear_constraints(), add_locked_in_constraints(), add_mandatory_allocation_constraints(), add_manual_bounded_constraints(), add_manual_locked_constraints(), add_neighbor_constraints()

Examples

# set seed for reproducibility
set.seed(500)

# load data
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
sim_locked_out_raster <- get_sim_locked_out_raster()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_pu_polygons <- get_sim_zones_pu_polygons()
sim_zones_features <- get_sim_zones_features()

# 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 column 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)

# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)
s4 <- solve(p4)
s5 <- solve(p5)

# create single object with all solutions
s6 <- sf::st_sf(
  tibble::tibble(
    s1 = s1$solution_1,
    s2 = s2$solution_1,
    s3 = s3$solution_1,
    s4 = s4$solution_1,
    s5 = s5$solution_1
  ),
  geometry = sf::st_geometry(s1)
)

# plot solutions
plot(
  s6,
  main = c(
    "none locked out", "locked out (integer input)",
    "locked out (character input)", "locked out (raster input)",
    "locked out (polygon input)"
  )
)


# reset plot
par(mfrow = c(1, 1))

# create minimal multi-zone problem with spatial data
p7 <-
  problem(
    sim_zones_pu_polygons, sim_zones_features,
    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 out constraints using matrix data
locked_matrix <- as.matrix(sf::st_drop_geometry(
  sim_zones_pu_polygons[, c("locked_1", "locked_2", "locked_3")]
))

p8 <- p7 %>% add_locked_out_constraints(locked_matrix)

# solve problem
s8 <- solve(p8)

# create new column representing the zone id that each planning unit
# was allocated to in the solution
s8$solution <- category_vector(sf::st_drop_geometry(
  s8[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
))
s8$solution <- factor(s8$solution)

# plot solution
plot(s8[, "solution"], main = "solution", axes = FALSE)


# create multi-zone problem with locked out constraints using column names
p9 <-
  p7 %>%
  add_locked_out_constraints(c("locked_1", "locked_2", "locked_3"))

# solve problem
s9 <- solve(p9)

# create new column in s8 representing the zone id that each planning unit
# was allocated to in the solution
s9$solution <- category_vector(sf::st_drop_geometry(
  s9[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
))
s9$solution[s9$solution == 1 & s9$solution_1_zone_1 == 0] <- 0
s9$solution <- factor(s9$solution)

# plot solution
plot(s9[, "solution"], main = "solution", axes = FALSE)

# create multi-zone problem with raster planning units
p10 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  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-layer raster with locked out units
locked_out_raster <- sim_zones_pu_raster[[1]]
locked_out_raster[!is.na(locked_out_raster)] <- 0
locked_out_raster <- locked_out_raster[[c(1, 1, 1)]]
names(locked_out_raster) <- c("zones_1", "zones_2", "zones_3")
locked_out_raster[[1]][1] <- 1
locked_out_raster[[2]][2] <- 1
locked_out_raster[[3]][3] <- 1

# plot locked out raster
plot(locked_out_raster)


# add locked out raster units to problem
p10 <- p10 %>% add_locked_out_constraints(locked_out_raster)

# solve problem
s10 <- solve(p10)

# plot solution
plot(category_layer(s10), main = "solution", axes = FALSE)