Kernel Density Plots for Climate-Smart Spatial Plans

splnr_plot_climKernelDensity() generates kernel density plots for climate-smart spatial plans, offering two distinct plotting styles: "Normal" (for publication-quality comparison of multiple solutions) and "Basic" (for simplified visualization for stakeholders).

Usage

splnr_plot_climKernelDensity(
  soln,
  solution_names = "solution_1",
  climate_names = "metric",
  type = "Normal",
  colorMap = "C",
  legendTitle = expression(" °C y"^"-1" * ""),
  xAxisLab = expression("Climate warming ( °C y"^"-1" * ")")
)

Arguments

soln

For type = "Normal": A list of prioritizr solution objects (e.g., list(s1, s2)). Each solution must contain a metric column and a solution_1 column. For type = "Basic": A single prioritizr solution sf object.

solution_names

A character vector of names corresponding to each solution in soln when type = "Normal". Not used for type = "Basic". Defaults to NA.

climate_names

A character string of the name of the climate

type

A character string specifying the plotting style. Must be either "Normal" or "Basic". Defaults to "Normal".

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 "C".

legendTitle

A character string or expression for the title of the legend. Defaults to expression(" \u00B0C y"^"-1" * ""), representing "°C year⁻¹".

xAxisLab

A character string or expression for the x-axis label, depending on the climate metric input. Defaults to expression("Climate warming ( \u00B0C y"^"-1" * ")").

Value

A ggplot object representing the kernel density plot.

Details

This wrapper function intelligently dispatches to either splnr_plot_climKernelDensity_Fancy() (for type = "Normal") or splnr_plot_climKernelDensity_Basic() (for type = "Basic") based on the type parameter.

The "Normal" (Fancy) style is suitable for detailed comparisons, accommodating a list of solutions and custom axis labels, while the "Basic" style is streamlined for clarity and quick interpretation, ideal for stakeholder engagement.

Both underlying functions require a prioritizr solution containing a climate metric column with climate metric information and a prioritizr solution column indicating selected planning units.

Examples

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

# Prepare data for a climate-priority area approach (CPA)
target <- dat_species_bin %>%
  sf::st_drop_geometry() %>%
  colnames() %>%
  data.frame() %>%
  setNames(c("feature")) %>%
  dplyr::mutate(target = 0.3)

CPA <- splnr_climate_priorityAreaApproach(
  features = dat_species_bin,
  metric = dat_clim,
  targets = target,
  direction = -1,
  refugiaTarget = 1
)

# Join climate metric to features for the problem
out_sf <- CPA$Features %>%
  dplyr::mutate(Cost_None = rep(1, dim(.)[[1]])) %>% # Ensure enough costs for PUs
  sf::st_join(dat_clim, join = sf::st_equals)

# Define features for the prioritizr problem
usedFeatures <- out_sf %>%
  sf::st_drop_geometry() %>%
  dplyr::select(-tidyselect::starts_with("Cost_"), -"metric") %>%
  names()

# Create and solve a prioritizr problem
p1 <- prioritizr::problem(out_sf, usedFeatures, "Cost_None") %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(CPA$Targets$target) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_solnClim <- prioritizr::solve.ConservationProblem(p1)

# Example 1: Basic kernel density plot
plot_basic_kde <- splnr_plot_climKernelDensity(soln = dat_solnClim, type = "Basic")
print(plot_basic_kde)

# Example 2: Normal (Fancy) kernel density plot for a single solution
plot_normal_kde_single <- splnr_plot_climKernelDensity(
  soln = list(dat_solnClim),
  solution_names = c("Solution 1"),
  type = "Normal"
)
print(plot_normal_kde_single)

# Example 3: Normal (Fancy) plot comparing two solutions (create a dummy second solution)
# For demonstration, let's create another dummy solution
dat_solnClim_2 <- dat_solnClim %>%
  dplyr::mutate(solution_1 = sample(c(0, 1), n(), replace = TRUE)) # Randomize selection

plot_normal_kde_multi <- splnr_plot_climKernelDensity(
  soln = list(dat_solnClim, dat_solnClim_2),
  solution_names = c("Solution A", "Solution B"),
  climate_names = "metric",
  type = "Normal",
  colorMap = "plasma",
  legendTitle = "Climate Value",
  xAxisLab = "Climate Metric (units)"
)
print(plot_normal_kde_multi)
} # }