Plot selected planning units in space

Plot the spatial distribution of selected planning units from a solutionset-class object returned by solve.

This function maps the planning-unit selection summary returned by get_pu onto the planning-unit geometry stored in the associated Problem object.

Usage

plot_spatial_pu(
  x,
  runs = NULL,
  ...,
  base_alpha = 0.1,
  selected_alpha = 0.9,
  base_fill = "grey92",
  base_color = NA,
  selected_color = NA,
  draw_borders = FALSE,
  show_base = TRUE
)

Arguments

x

A solutionset-class object returned by solve.

runs

Optional integer vector of run ids. If NULL, the first available run is plotted by default.

...

Reserved for future extensions.

base_alpha

Numeric value in \([0,1]\) giving the alpha of the base planning-unit layer.

selected_alpha

Numeric value in \([0,1]\) giving the alpha of the selected planning-unit layer.

base_fill

Fill colour for the base planning-unit layer.

base_color

Border colour for the base planning-unit layer.

selected_color

Border colour for selected planning units.

draw_borders

Logical. If FALSE, borders are not drawn.

show_base

Logical. If TRUE, draw the base planning-unit layer underneath the selected units.

Value

Invisibly returns a ggplot object.

Details

Let \(w_i \in \{0,1\}\) denote the planning-unit selection variable for planning unit \(i\). This function plots the user-facing selected == 1 representation of \(w_i\).

If several runs are requested, the output is faceted by run_id.

Planning-unit geometry must be available in the associated problem object.

See also

get_pu, plot_spatial, plot_spatial_actions, plot_spatial_features

Examples

if (
  requireNamespace("sf", quietly = TRUE) &&
  requireNamespace("ggplot2", quietly = TRUE) &&
  requireNamespace("rcbc", quietly = TRUE)
) {
  data("sim_pu_sf", package = "multiscape")

  pu <- sim_pu_sf[
    seq_len(min(4L, nrow(sim_pu_sf))),
  ]

  pu$id <- seq_len(nrow(pu))
  pu$cost <- seq_len(nrow(pu))

  features <- data.frame(
    id = 1L,
    name = "feature_1"
  )

  dist_features <- data.frame(
    pu = pu$id,
    feature = 1L,
    amount = rep(1, nrow(pu))
  )

  problem <- create_problem(
    pu = pu,
    features = features,
    dist_features = dist_features,
    cost = "cost"
  ) |>
    add_constraint_targets_relative(0.25) |>
    add_objective_min_cost(alias = "cost") |>
    set_solver_cbc(verbose = FALSE)

  solutions <- solve(problem)

  plot_spatial_pu(solutions)
}