Set the objective of a conservation planning problem()
to
minimize the cost of the solution whilst ensuring that all targets are met.
This objective is similar to that used in
Marxan and is detailed in Rodrigues et al. (2000).
add_min_set_objective(x)
x 


Object (i.e. ConservationProblem
) with the objective
added to it.
A problem objective is used to specify the overall goal of the conservation planning problem. Please note that all conservation planning problems formulated in the prioritizr package require the addition of objectivesfailing to do so will return an error message when attempting to solve problem.
In the context of systematic reserve design, the minimum set objective seeks to find the set of planning units that minimizes the overall cost of a reserve network, while meeting a set of representation targets for the conservation features. This objective is equivalent to a simplified Marxan reserve design problem with the Boundary Length Modifier (BLM) set to zero.
The minimum set objective for the reserve design problem can be expressed mathematically for a set of planning units (\(I\) indexed by \(i\)) and a set of features (\(J\) indexed by \(j\)) as:
$$\mathit{Minimize} \space \sum_{i = 1}^{I} x_i c_i \\ \mathit{subject \space to} \\ \sum_{i = 1}^{I} x_i r_{ij} \geq T_j \space \forall \space j \in J$$
Here, \(x_i\) is the decisions variable (e.g. specifying whether planning unit \(i\) has been selected (1) or not (0)), \(c_i\) is the cost of planning unit \(i\), \(r_{ij}\) is the amount of feature \(j\) in planning unit \(i\), and \(T_j\) is the target for feature \(j\). The first term is the objective function and the second is the set of constraints. In words this says find the set of planning units that meets all the representation targets while minimizing the overall cost.
Rodrigues AS, Cerdeira OJ, and Gaston KJ (2000) Flexibility, efficiency, and accountability: adapting reserve selection algorithms to more complex conservation problems. Ecography, 23: 565574.
# set seed for reproducibility
set.seed(500)
# load data
data(sim_pu_raster, sim_features, sim_pu_zones_stack, sim_features_zones)
# create minimal problem with minimum set objective
p1 < problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# \dontrun{
# solve problem
s1 < solve(p1)
# plot solution
plot(s1, main = "solution", axes = FALSE, box = FALSE)
# }
# create multizone problem with minimum set objective
targets_matrix < matrix(rpois(15, 1), nrow = 5, ncol = 3)
p2 < problem(sim_pu_zones_stack, sim_features_zones) %>%
add_min_set_objective() %>%
add_absolute_targets(targets_matrix) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# \dontrun{
# solve problem
s2 < solve(p2)
# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE, box = FALSE)
# }