add_manual_locked_constraints: Add manually specified locked constraints

In prioritizr: Systematic Conservation Prioritization in R

Add manually specified locked constraints

Description

Add constraints to a conservation planning problem to ensure that solutions allocate (or do not allocate) specific planning units to specific management zones. This function offers more fine-grained control than the add_locked_in_constraints() and add_locked_out_constraints() functions.

Usage

add_manual_locked_constraints(x, data)

## S4 method for signature 'ConservationProblem,data.frame'
add_manual_locked_constraints(x, data)

## S4 method for signature 'ConservationProblem,tbl_df'
add_manual_locked_constraints(x, data)

Arguments

x	problem() object.
data	data.frame or tibble::tibble() object. See the Data format section for more information.

Value

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

Data format

The argument to data should be a data.frame with the following columns:

pu

integer planning unit identifier.

zone

character names of zones. Note that this argument is optional for arguments to x that contain a single zone.

status

numeric values indicating how much of each planning unit should be allocated to each zone in the solution. For example, the numeric values could be binary values (i.e., zero or one) for problems containing binary-type decision variables (using the add_binary_decisions() function). Alternatively, the numeric values could be proportions (e.g., 0.5) for problems containing proportion-type decision variables (using the add_proportion_decisions()).

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_in_constraints(), add_locked_out_constraints(), add_mandatory_allocation_constraints(), add_manual_bounded_constraints(), add_neighbor_constraints()

Examples

## Not run: 
# set seed for reproducibility
set.seed(500)

# load data
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
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 locked in constraints using add_locked_constraints
p2 <- p1 %>% add_locked_in_constraints("locked_in")

# create identical problem using add_manual_locked_constraints
locked_data <- data.frame(
  pu = which(sim_pu_polygons$locked_in),
  status = 1
)

p3 <- p1 %>% add_manual_locked_constraints(locked_data)

# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)

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

# plot solutions
## s1 = none locked in
## s2 = locked in constraints
## s3 = manual locked constraints
plot(s4)

# create minimal problem with multiple zones
p5 <-
  problem(
    sim_zones_pu_polygons, sim_zones_features,
    c("cost_1", "cost_2", "cost_3")
  ) %>%
  add_min_set_objective() %>%
  add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# create data.frame with the following constraints:
# planning units 1, 2, and 3 must be allocated to zone 1 in the solution
# planning units 4, and 5 must be allocated to zone 2 in the solution
# planning units 8 and 9 must not be allocated to zone 3 in the solution
locked_data2 <- data.frame(
  pu = c(1, 2, 3, 4, 5, 8, 9),
  zone = c(rep("zone_1", 3), rep("zone_2", 2),rep("zone_3", 2)),
  status = c(rep(1, 5), rep(0, 2))
)

# print locked constraint data
print(locked_data2)

# create problem with added constraints
p6 <- p5 %>% add_manual_locked_constraints(locked_data2)

# solve problem
s5 <- solve(p5)
s6 <- solve(p6)

# create two new columns representing the zone id that each planning unit
# was allocated to in the two solutions
s5$solution <- category_vector(sf::st_drop_geometry(
  s5[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
))
s5$solution <- factor(s5$solution)

s5$solution_locked <- category_vector(sf::st_drop_geometry(
  s6[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
))
s5$solution_locked <- factor(s5$solution_locked)

# plot solutions
plot(s5[, c("solution", "solution_locked")], axes = FALSE)

## End(Not run)

prioritizr documentation built on Aug. 9, 2023, 1:06 a.m.