This is the main function that applies the MinPatch algorithm to a prioritizr solution to ensure all protected areas meet minimum size thresholds. The function uses prioritizr summary functions where possible to reduce code duplication and ensure consistency with prioritizr calculations.
Usage
run_minpatch(
prioritizr_problem,
prioritizr_solution,
min_patch_size,
patch_radius,
boundary_penalty = 0,
remove_small_patches = TRUE,
add_patches = TRUE,
whittle_patches = TRUE,
solution_column = "solution_1",
verbose = TRUE
)Arguments
- prioritizr_problem
A prioritizr problem object
- prioritizr_solution
A solved prioritizr solution object
- min_patch_size
Minimum patch size threshold
- patch_radius
Radius for adding new patches
- boundary_penalty
Boundary penalty value (default = 0)
- remove_small_patches
Logical, whether to remove small patches (Stage 1, default = TRUE)
- add_patches
Logical, whether to add new patches to meet targets (Stage 2, default = TRUE)
- whittle_patches
Logical, whether to remove unnecessary units (Stage 3, default = TRUE)
- solution_column
Name of solution column (default = "solution_1")
- verbose
Logical, whether to print progress (default = TRUE)
Details
The MinPatch algorithm consists of three stages:
Remove small patches: Removes patches smaller than min_patch_size
Add new patches: Adds patches to meet conservation targets
Whittle patches: Removes unnecessary planning units
**Important**: If you set remove_small_patches = TRUE but
add_patches = FALSE, the algorithm may remove patches without
compensating, potentially violating conservation targets. In such cases,
a warning will be issued. Consider using add_patches = TRUE or
a smaller min_patch_size to maintain target achievement.
Examples
library(prioritizr)
library(sf)
library(terra)
# Get example data from prioritizr
dat <- c(get_sim_pu_raster(), get_sim_features()) %>%
as.polygons(dissolve = FALSE, values = TRUE) %>%
sf::st_as_sf() %>%
dplyr::rename(cost = layer)
st_crs(dat) <- NA
features = colnames(dat) %>%
stringr::str_subset("feature_")
# Create prioritizr problem
p <- problem(dat, features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.17) %>% # 17% of each feature
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# Solve problem
s <- solve(p)
# Apply MinPatch with all stages
result <- run_minpatch(
prioritizr_problem = p,
prioritizr_solution = s,
min_patch_size = 0.05,
patch_radius = 0.3,
)
#> Validating inputs...
#> Initializing data structures...
#> Calculating boundary matrix (this may take a while)...
#> Creating patch radius dictionary...
#> Calculating initial patch statistics...
#> Stage 1: Removing small patches...
#> Stage 2: Adding new patches...
#> Initial unmet targets: 5
#> Unmet feature IDs: 1, 2, 3, 4, 5
#> Iteration 1 - Unmet targets: 5
#> Found 85 potential patches with scores
#> Best score: 0.002500108 for unit 90
#> Added patch centered on unit 90
#> Iteration 2 - Unmet targets: 4
#> Found 76 potential patches with scores
#> Best score: 0.002060928 for unit 78
#> Added patch centered on unit 78
#> All conservation targets are now met!
#> Stage 3: Removing unnecessary planning units...
#> Edge units found: 21
#> Keystone units: 0
#> New keystone units: 0
#> Scoreable units: 21
#> Unit 90 cannot be removed - adding to keystone set
#> Edge units found: 20
#> Keystone units: 1
#> New keystone units: 0
#> Scoreable units: 20
#> Unit 81 cannot be removed - adding to keystone set
#> Edge units found: 19
#> Keystone units: 2
#> New keystone units: 0
#> Scoreable units: 19
#> Unit 80 cannot be removed - adding to keystone set
#> Edge units found: 18
#> Keystone units: 3
#> New keystone units: 0
#> Scoreable units: 18
#> Unit 89 cannot be removed - adding to keystone set
#> Edge units found: 17
#> Keystone units: 4
#> New keystone units: 0
#> Scoreable units: 17
#> Unit 73 cannot be removed - adding to keystone set
#> Unit 79 cannot be removed - adding to keystone set
#> Unit 72 cannot be removed - adding to keystone set
#> Unit 88 cannot be removed - adding to keystone set
#> Unit 87 cannot be removed - adding to keystone set
#> Unit 64 cannot be removed - adding to keystone set
#> No more edge units to consider - terminating
#> Calculating final statistics...
#> MinPatch processing complete!
# Apply MinPatch with only Stage 1 and 3 (skip adding patches)
result2 <- run_minpatch(
prioritizr_problem = p,
prioritizr_solution = s,
min_patch_size = 0.05,
patch_radius = 0.3,
add_patches = FALSE
)
#> Validating inputs...
#> Initializing data structures...
#> Calculating boundary matrix (this may take a while)...
#> Creating patch radius dictionary...
#> Calculating initial patch statistics...
#> Stage 1: Removing small patches...
#> Warning: After removing small patches, 5 conservation targets are no longer met. Consider setting add_patches = TRUE to automatically add patches to meet targets, or use a smaller min_patch_size.
#> Warning: 5 targets are no longer met after removing small patches
#> Unmet feature IDs: 1, 2, 3, 4, 5
#> Stage 2: Skipping addition of new patches...
#> Stage 3: Removing unnecessary planning units...
#> Edge units found: 5
#> Keystone units: 0
#> New keystone units: 5
#> Scoreable units: 0
#> No units can be removed - all are keystone - terminating
#> Calculating final statistics...
#> MinPatch processing complete!
print_minpatch_summary(result)
#> === MinPatch Processing Summary ===
#>
#> Patch Statistics:
#> Initial patches: 7 (valid: 1)
#> Final patches: 4 (valid: 2)
#> Area change: 0.05 (31.2%)
#>
#> Cost Breakdown:
#> Planning unit cost: 4137.80
#> Boundary cost: 0.00
#> Total cost: 4137.80
#> Selected units: 21
#>
#> Feature Representation:
#> Total features: 5
#> Targets met: 5
#> Targets unmet: 0
#> Mean proportion: 0.236
#> Total shortfall: 0.00
#>
#>
#> === End Summary ===