add_boundary_penalties: Add boundary penalties
In prioritizr/prioritizrutils: Utilities for prioritizr

Description Usage Arguments Value See Also Examples

Add penalties to a conservation problem to favor solutions that clump selected planning units together into contiguous reserves.

1	add_boundary_penalties(x, penalty, edge_factor, boundary_data = NULL)

`x`	`ConservationProblem-class` object.
`penalty`	`numeric` penalty for exposed edges. This is equivalent to the boundary length modifier (BLM) parameter in marxan.net.
`edge_factor`	`numeric` proportion to scale edges that do not have any neighboring planning units. For example, an edge factor of `0.5` is commonly used for planning units along the coast line.
`boundary_data`	`matrix`, `Matrix`, or `data.frame` object showing the shared boundary lengths between planning units. If `boundary_data` is a matrix then each row and column denote a planning unit and cell values represent their shared boundary. If `boundary_data` is `data.frame` then it must have the columns `"id1"`, `"id2"`, and `"boundary"`, where each row shows the shared boundary between two planning units (as per the standard Marxan input format). This argument is required if the planning units in `x` are stored in a `data.frame` object. Otherwise, it is optional and the boundary data will be automatically calculated. Note that the `boundary_data` must be denote symmetric relationships between planning units. If assymetric relaionships are required, use the `add_connectivity_penalties` function.

ConservationProblem-class object.

constraints, penalties.

# 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 low boundary penalties
p2 <- p1 %>% add_boundary_penalties(5, 1)

# create problem with high boundary penalties
# receive half the penalty as inner edges
p3 <- p1 %>% add_boundary_penalties(50, 1)


# solve problems
s <- stack(solve(p1), solve(p2), solve(p3))

# plot solutions
plot(s, main = c("basic solution", "small penalties", "high penalties"))