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Plot Importance Score of Planning Units

Source: R/splnr_plotting.R
splnr_plot_importanceScore.Rd

The splnr_plot_importanceScore() function visualizes the importance scores (irreplaceability) of Planning Units from a prioritizr conservation problem using ggplot2. It supports different methods for calculating importance scores.

Usage

splnr_plot_importanceScore(
  soln,
  pDat,
  method = "Ferrier",
  plotTitle = "",
  colorMap = "A",
  decimals = 4,
  legendTitle = "Importance Score"
)

Arguments

soln

The prioritizr solution object, expected as an sf object. It should contain a solution_1 column.

pDat

The prioritizr problem object that was solved to generate soln.

method

A character string specifying the method for calculating importance scores. Must be one of "Ferrier", "RWR", or "RC". Defaults to "Ferrier".

plotTitle

A character string for the title of the plot. Defaults to "".

colorMap

A character string indicating the viridis color map to use (e.g., "A", "B", "C", "D", "E"). See https://ggplot2.tidyverse.org/reference/scale_viridis.html for all options. Defaults to "A".

decimals

The number of decimal places to display for the importance scores in the legend. Ferrier Score often benefits from a higher number of decimals (>4). Defaults to 4.

legendTitle

A character string for the title of the legend. Defaults to "Importance Score".

Value

A ggplot object representing the plot of importance scores.

Details

Importance scores quantify the irreplaceability of a Planning Unit in a conservation solution. This function leverages the prioritizr package to calculate and plot three different types of importance scores:

  • "Ferrier": The Ferrier Score, which is applicable only with the minimum set objective function. It often requires a higher number of decimals (e.g., >4) for accurate representation.

  • "RWR": Rarity Weighted Richness Score.

  • "RC": Replacement Cost. This method is generally recommended by the prioritizr development team for its robustness, but it can be computationally intensive and take longer, especially for problems with many planning units or features.

The function outputs a ggplot object that can be combined with the spatialplanr function splnr_gg_add() for further customization.

Examples

if (FALSE) { # \dontrun{
# Assuming 'dat_species_bin' and 'dat_PUs' are existing sf objects in your package.

# Create a dummy prioritizr problem and solve it for demonstration.
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()

# Plot importance score using the "Ferrier" method.
plot_ferrier_importance <- splnr_plot_importanceScore(
  soln = dat_soln,
  pDat = dat_problem,
  method = "Ferrier",
  decimals = 4,
  plotTitle = "Ferrier Importance Score"
)
print(plot_ferrier_importance)

# Plot importance score using the "RWR" (Rarity Weighted Richness) method.
plot_rwr_importance <- splnr_plot_importanceScore(
  soln = dat_soln,
  pDat = dat_problem,
  method = "RWR",
  decimals = 2,
  plotTitle = "Rarity Weighted Richness"
)
print(plot_rwr_importance)
} # }