prioritizr (version 3.0.4)

add_connectivity_penalties: Add connectivity penalties

Description

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

Usage

add_connectivity_penalties(x, penalty, connectivity_data)

Arguments

x

ConservationProblem-class object.

penalty

numeric penalty for missing connections between planning units. This is equivalent to the connectivity strength modifier (CSM; Beger et al. 2010). Defaults to one so that penalties are the same as the values in the data.

connectivity_data

A data.frame or matrix object that shows the strength of connectivity between consecutive planning units. If connectivity_data is a matrix, then rows and columns represent each planing unit and the cell values represent the connectivity between them. If connectivity_data is a data.frame the column names must be "id1", "id2", "boundary", where each row shows the connectivity between two planning units (following the Marxan format). The data can be used to denote symmetric or asymmetric relationships between planning units.

Value

ConservationProblem-class object.

Details

This function 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 and is inspired by Beger et al. (2010).

References

Beger M, Linke S, Watts M, Game E, Treml E, Ball I, and Possingham, HP (2010) Incorporating asymmetric connectivity into spatial decision making for conservation, Conservation Letters, 3: 359--368.

See Also

penalties.

Examples

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

# create a symmetric connectivity matrix where the connectivity between
# two planning units is the inverse distance between them
sc_matrix <- (1 / (as.matrix(dist(sim_pu_points@coords)) + 1))

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

# create basic problem
p1 <- problem(sim_pu_points, sim_features, "cost") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.2)

# create problem with low connectivity penalties
p2 <- p1 %>% add_connectivity_penalties(25, sc_matrix)

# create problem with higher connectivity penalties
p3 <- p1 %>% add_connectivity_penalties(50, sc_matrix)

# create an asymmetric connectivity matrix where links from even numered
# units to odd numbered units have half the connectivity as from odd
# numbered units to even units
even_units <- seq(2, length(sim_pu_points), 2)
odd_units <- seq(1, length(sim_pu_points), 2)
ac_matrix <- sc_matrix
ac_matrix[even_units, odd_units] <- 0.5 * ac_matrix[even_units, odd_units]

# create problem with asymmetric connectivity and high penalties
p4 <- p1 %>% add_connectivity_penalties(50, ac_matrix)

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

# plot solutions
par(mfrow = c(2,2), mar = c(0, 0, 4.1, 0))

plot(s[[1]], pch = 19, main = "basic solution", cex = 1.5)
points(s[[1]][s[[1]]$solution_1 == 1, ], col = "darkgreen", pch = 19,
       cex = 1.5)

plot(s[[2]], pch = 19, main = "small penalties", cex = 1.5)
points(s[[2]][s[[2]]$solution_1 == 1, ], col = "darkgreen", pch = 19,
       cex = 1.5)

plot(s[[3]], pch = 19, main = "high penalties", cex = 1.5)
points(s[[3]][s[[3]]$solution_1 == 1, ], col = "darkgreen", pch = 19,
       cex = 1.5)

plot(s[[4]], pch = 19, main = "asymmetric connectivity", cex = 1.5)
points(s[[4]][s[[4]]$solution_1 == 1, ], col = "darkgreen", pch=19,
       cex = 1.5)
# }
# NOT RUN {
# }