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Add binary decisions

Source: R/add_binary_decisions.R
add_binary_decisions.Rd

Add a binary decision to a conservation planning problem. This is the classic decision of either prioritizing or not prioritizing a planning unit. Typically, this decision has the assumed action of selecting planning units for protected area establishment.

Usage

add_binary_decisions(x)

Arguments

x

problem() object.

Value

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

Details

Conservation planning problems involve making decisions on planning units. These decisions are then associated with actions (e.g., turning a planning unit into a protected area). Only a single decision should be added to a problem() object. Note that if multiple decisions are added to an object, then the last one to be added will be used during optimization. Also, if no decision is added to a problem(), then this decision will be used by default.

See also

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

Other decisions: add_proportion_decisions(), add_semicontinuous_decisions()

Examples

# set seed for reproducibility
set.seed(500)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()

# create minimal problem with binary decisions
p1 <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.1) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s1 <- solve(p1)

# plot solution
plot(s1, main = "solution", axes = FALSE)


# create a matrix with targets for a multi-zone conservation problem
targs <- matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)

# build multi-zone conservation problem with binary decisions
p2 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(targs)  %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve the problem
s2 <- solve(p2)

# print solution
print(s2)
#> class       : SpatRaster
#> size        : 10, 10, 3  (nrow, ncol, nlyr)
#> resolution  : 0.1, 0.1  (x, y)
#> extent      : 0, 1, 0, 1  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> varnames    : sim_zones_pu_raster
#>               sim_zones_pu_raster
#>               sim_zones_pu_raster
#> names       : zone_1, zone_2, zone_3
#> min values  :      0,      0,      0
#> max values  :      1,      1,      1

# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE)