Skip to contents

Plot Solution Comparison

Source: R/splnr_plotting.R
splnr_plot_comparison.Rd

The splnr_plot_comparison() function spatially visualizes the differences between two prioritizr conservation solutions. This helps in understanding which Planning Units are common, added, or removed between two scenarios.

Usage

splnr_plot_comparison(
  soln1,
  soln2,
  legendTitle = "Scenario 2 compared to Scenario 1:",
  base_size = 14
)

Arguments

soln1

The first prioritizr solution, expected as an sf object with a solution_1 column. This serves as the baseline for comparison.

soln2

The second prioritizr solution, expected as an sf object with a solution_1 column. This is the solution being compared against soln1.

legendTitle

A character string for the title of the legend. Defaults to "Scenario 2 compared to Scenario 1:".

base_size

A numeric value for the base font size (in points) passed to ggplot2::theme_bw(). All text elements scale proportionally from this value. Defaults to 14.

Value

A ggplot object representing the spatial comparison of the two solutions.

Details

Conservation planning often involves comparing outputs from different conservation problems or scenarios. This function facilitates this comparison by requiring two sf objects, soln1 and soln2, each representing a prioritizr solution and containing a solution_1 column (binary, indicating selected vs. not selected).

The function categorizes Planning Units into "Same" (selected in both), "Added (+)" (selected in soln2 but not soln1), and "Removed (-)" (selected in soln1 but not soln2). It then plots these categories with distinct colors for clear visualization. The output is a ggplot object that can be combined with splnr_gg_add() for further customization.

Examples

if (FALSE) { # \dontrun{
# Assuming 'dat_species_bin' is an existing sf object in your package.

# Create Problem 1 with 30% target and solve it.
dat_problem <- prioritizr::problem(
  dat_species_bin %>% dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.3) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_soln <- dat_problem %>%
  prioritizr::solve.ConservationProblem()

# Create Problem 2 with 50% target and solve it.
dat_problem2 <- prioritizr::problem(
  dat_species_bin %>%
    dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.5) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_soln2 <- dat_problem2 %>%
  prioritizr::solve.ConservationProblem()

# Plot the comparison between the two solutions.
plot_comparison <- splnr_plot_comparison(dat_soln, dat_soln2)
print(plot_comparison)
} # }