prioritizr (version 3.0.4)

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. total solution cost given add_min_set_objective).

Details

Both penalties and constraints can be used as mechanisms to increase solution connectivity, but instead of penalizing solutions with low connectivity, a constraint acts as a cutoff to make certain solutions invalid. Applying a constraint does not necessarily affect solution cost, while applying a penalty does. Below are the penalties that can be added to a ConservationProblem object.

add_boundary_penalties

Add penalties to a conservation problem to favor solutions that clump selected planning units together into contiguous reserves. Uses shared boundary length as a measure of connectivity, equivalent to the boundary length modifier (BLM) in Marxan. Boundary data is calculated automatically unless the planning units in x are stored in a data.frame, in which case boundary data must be added as a matrix or data.frame. This function can only be used for symmetric relationships between planning units; for asymmetric relationships use add_connectivity_penalties.

add_connectivity_penalties

Add constraints to a conservation problem to favor solutions that select planning units with high connectivity between them. Uses connectivity data in the form of a matrix or data.frame object, where strength of connectivity is a value such as the inverse distance between consecutive planning units. This function can be used for symmetric or asymmetric relationships between planning units.

See Also

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

Examples

# NOT RUN {
## load data
data(sim_pu_points, 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 problem with connectivity penalties
# create connectivity matrix based on spatial proximity
sc_matrix <- as.data.frame(sim_pu_raster, xy = TRUE)
sc_matrix <- sc_matrix[is.finite(values(sim_pu_raster)), 1:2]
sc_matrix <- 1 / (as.matrix(dist(sc_matrix)) + 1)

# remove connections between planning units with little connectivity
sc_matrix[sc_matrix < 0.85] <- 0

# create problem
p3 <- p1 %>% add_connectivity_penalties(25, sc_matrix)
# }
# NOT RUN {
## solve problems
s <- stack(solve(p1), solve(p2), solve(p3))

# plot solutions
plot(s, axes = FALSE, box = FALSE,
     main = c("basic solution", "boundary penalties",
              "connectivity penalties"))
# }
# NOT RUN {
# }