prioritizr (version 3.0.4)

constraints: Conservation problem constraints

Description

A constraint can be added to a conservation planning problem as a way to make certain solutions invalid, given a cutoff criteria. Used when specific planning units or configurations of planning units are undesirable or inefficient.

Details

Like a penalty, a constraint can be used as a mechanism to increase connectivity between planning units, but works by eliminating all solutions without a certain degree of connectivity, rather than penalizing poorly connected solutions. The solution cost will likely be less affected by applying a constraint than a penalty. Use the add_connected_constraints, add_corridor_constraints, or add_neighbor_constraints functions for connectivity considerations, or see penalties.

Constraints can also be used to lock in or lock out certain planning units from the solution, such as protected areas or degraded land. Use the add_locked_in_constraints and add_locked_out_constraints functions to do this.

Below are the constraints that can be added to a ConservationProblem object.

add_connected_constraints

Add constraints to a conservation problem to ensure that all selected planning units are spatially connected to each other.

add_corridor_constraints

Adds constraints to ensure that all planning units used to represent features form a contiguous reserve.

add_locked_in_constraints

Add constraints to ensure that certain planning units are present in the solution.

add_locked_out_constraints

Add constraints to ensure that certain planning units are absent from the solution.

add_neighbor_constraints

Add constraints to a conservation problem to ensure that all selected planning units have at least a certain number of neighbors.

See Also

decisions, objectives, penalties, codeportfolios, problem, solvers, targets.

Examples

# NOT RUN {
# load data
data(sim_pu_raster, sim_features)

# create base problem with no additional constraints
p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.1) %>%
      add_binary_decisions()

# create problem with locked in constraints
p2 <- p1 %>% add_locked_in_constraints("locked_in")

# create problem with locked in constraints
p3 <- p1 %>% add_locked_out_constraints("locked_out")

# create problem with neighbor constraints
p4 <- p1 %>% add_neighbor_constraints(2)

# create problem with connected constraints
p5 <- p1 %>% add_connected_constraints()
# }
# 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 = "basic")
plot(s1[s1$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s2, main="locked in")
plot(s2[s2$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s3, main="locked out")
plot(s3[s3$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s4, main = "neighbor")
plot(s4[s4$solution_1 == 1, ], col = "darkgreen", add = TRUE)

plot(s5, main = "connected")
plot(s5[s5$solution_1 == 1, ], col = "darkgreen", add = TRUE)
# }
# NOT RUN {
# }