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Plot selected planning units in space

Source: R/plot.R
plot_spatial_planning_units.Rd

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_planning_units onto the planning-unit geometry stored in the associated Problem object.

Usage

plot_spatial_planning_units(
  x,
  solutions = 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.

solutions

Optional integer vector of solution ids. If NULL, the first available solution 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_planning_units, plot_spatial_actions, plot_spatial_features, plot_spatial_planning_units

Examples

if (
  requireNamespace("sf", quietly = TRUE) &&
  requireNamespace("ggplot2", quietly = TRUE) &&
  requireNamespace("rcbc", quietly = TRUE)
) {
  # Load a complete simulated planning problem.
  example_data <- load_sim_multiaction()

  problem <- create_problem(
    pu = example_data$planning_units,
    features = example_data$features,
    dist_features = example_data$dist_features,
    cost = "cost"
  ) |>
    add_actions(
      example_data$actions,
      cost = example_data$action_costs
    ) |>
    add_effects(
      example_data$effects,
      effect_type = "delta"
    ) |>
    add_constraint_targets_relative(0.05) |>
    add_objective_min_cost(alias = "cost", include_pu_cost = FALSE) |>
    add_objective_max_benefit(alias = "benefit") |>
    set_method_weighted_sum(
      aliases = c("cost", "benefit"),
      runs = set_runs_grid(n = 3),
      normalize_weights = TRUE
    ) |>
    set_solver_cbc(verbose = FALSE)

  solutions <- solve(problem)

  plot_spatial_planning_units(solutions)
}