prioritizr (version 5.0.3)

penalties: Conservation problem penalties

Description

A penalty can be applied to a conservation planning problem() to penalize solutions according to a specific metric. Penalties---unlike constraints---act as an explicit trade-off with the objective being minimized or maximized (e.g. solution cost when used with add_min_set_objective()).

Details

Both penalties and constraints can be used to modify a problem and identify solutions that exhibit specific characteristics. Constraints work by invalidating solutions that do not exhibit specific characteristics. On the other hand, penalties work by specifying trade-offs against the main problem objective and are mediated by a penalty factor.

The following penalties can be added to a conservation planning problem():

add_boundary_penalties()

Add penalties to a conservation problem to favor solutions that have planning units clumped together into contiguous areas.

add_connectivity_penalties()

Add penalties to a conservation problem to favor solutions that select planning units with high connectivity between them.

add_linear_penalties()

Add penalties to a conservation problem to favor solutions that avoid selecting planning units based on a certain variable (e.g. anthropogenic pressure).

See Also

constraints, decisions, objectives portfolios, problem(), solvers, targets.

Examples

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

# create basic problem
p1 <- problem(sim_pu_raster, sim_features) %>%
      add_min_set_objective() %>%
      add_relative_targets(0.2) %>%
      add_default_solver()

# create problem with boundary penalties
p2 <- p1 %>% add_boundary_penalties(5, 1)

# create connectivity matrix based on spatial proximity
 scm <- as.data.frame(sim_pu_raster, xy = TRUE, na.rm = FALSE)
 scm <- 1 / (as.matrix(dist(scm)) + 1)

# remove weak and moderate connections between planning units to reduce
# run time
scm[scm < 0.85] <- 0

# create problem with connectivity penalties
p3 <- p1 %>% add_connectivity_penalties(25, data = scm)

# create problem with linear penalties,
# here the penalties will be based on random numbers to keep it simple

# simulate penalty data
sim_penalty_raster <- simulate_cost(sim_pu_raster)

# plot penalty data
plot(sim_penalty_raster, main = "penalty data", axes = FALSE, box = FALSE)

# create problem with linear penalties, with a penalty scaling factor of 100
p4 <- p1 %>% add_linear_penalties(100, data = sim_penalty_raster)

# }
# NOT RUN {
# solve problems
s <- stack(solve(p1), solve(p2), solve(p3), solve(p4))

# plot solutions
plot(s, axes = FALSE, box = FALSE,
     main = c("basic solution", "boundary penalties",
              "connectivity penalties", "linear penalties"))
 
# }