priorCON (version 0.1.7)

connectivity_scenario: Connectivity scenario problem

Description

Solve a prioritizr prioritization problem, by incorporating graph connectivity of the features.

Usage

connectivity_scenario(cost_raster, features_rasters = NULL, budget_perc,
                      pre_graphs, locked_in = NULL, locked_out = NULL)

Value

A list containing input for get_outputs. Additionally, the connectivity SpatRaster objects that are used in prioritization are returned as well, before and after min-max scaling (original_connectivity_rast and normalized_connectivity_rast

objects correspondingly).

Arguments

cost_raster

SpatRaster object used as cost for prioritization. Its coordinates must correspond to the input given at preprocess_graphs.

features_rasters

features SpatRaster object used for prioritization. Its coordinates must correspond to the input given at preprocess_graphs.

budget_perc

numeric value \([0,1]\). It represents the budget percentage of the cost to be used for prioritization.

pre_graphs

output of get_metrics function.

locked_in

SpatRaster object used as locked in constraints, where these planning units are selected in the solution, e.g. current protected areas. For details, see
prioritizr::add_locked_in_constraints.

locked_out

SpatRaster object used as locked out constraints, where these planning units are excluded from the solution, e.g. not appropriate for protected areas. For details, see prioritizr::add_locked_out_constraints.

Details

A connectivity prioritization problem is created and solved using prioritizr package. The solver used for solving the problems is the best available on the computer, following the solver hierarchy of prioritizr. By default, the highs package using the HiGHS solver is downloaded during package installation.

Both features and connectivity rasters are min-max scaled before solving the prioritization problem.

References

Hanson, Jeffrey O, Richard Schuster, Nina Morrell, Matthew Strimas-Mackey, Brandon P M Edwards, Matthew E Watts, Peter Arcese, Joseph Bennett, and Hugh P Possingham. 2025. prioritizr: Systematic Conservation Prioritization in R. https://CRAN.R-project.org/package=prioritizr.

Hanson JO, Schuster R, Strimas‐Mackey M, Morrell N, Edwards BPM, Arcese P, Bennett JR, and Possingham HP. 2025. Systematic conservation prioritization with the prioritizr R package. Conservation Biology, 39: e14376. tools:::Rd_expr_doi("10.1111/cobi.14376")

Huangfu, Qi, and JA Julian Hall. 2018. Parallelizing the Dual Revised Simplex Method. Mathematical Programming Computation 10 (1): 119–42. tools:::Rd_expr_doi("10.1007/s12532-017-0130-5")

See Also

preprocess_graphs, get_metrics

Examples

# Read connectivity files from folder and combine them
combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"),
                                        header = FALSE, sep =";")

# Set seed for reproducibility
set.seed(42)

# Detect graph communities using the s-core algorithm
pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core")

cost_raster <- get_cost_raster()
features_rasters <- get_features_raster()

# Solve a prioritizr prioritization problem,
# by incorporating graph connectivity of the features
connectivity_solution <- connectivity_scenario(cost_raster=cost_raster,
features_rasters=features_rasters, budget_perc=0.1, pre_graphs=pre_graphs)

# Plot solution raster
terra::plot(connectivity_solution$solution, main="Connectivity Solution")

# Plot normalized connectivity raster
terra::plot(connectivity_solution$normalized_connectivity_rast,
main="Normalized S-Core Connectivity raster")