Package index • multiscape

Create a planning problem

Create the core planning problem from planning units, features, costs, and optional spatial data.

create_problem(): Create a planning problem input object

Management actions and outcomes

Define feasible management actions and their ecological and economic consequences.

add_actions(): Add management actions to a planning problem
add_effects(): Add action effects to a planning problem
add_benefits(): Add benefits
add_losses(): Add losses
add_profit(): Add profit to a planning problem

Targets and constraints

Specify representation targets, resource limits, and fixed planning-unit or action decisions.

add_constraint_targets_absolute(): Add absolute targets
add_constraint_targets_relative(): Add relative targets
add_constraint_area(): Add area constraint
add_constraint_budget(): Add budget constraint
add_constraint_locked_actions(): Add locked action decisions to a planning problem
add_constraint_locked_planning_units(): Add locked planning units to a problem

Spatial relationships

Define and store neighborhood, boundary, distance, and user-supplied relationships among planning units.

add_spatial_relations(): Add spatial relations
add_spatial_boundary(): Add spatial boundary-length relations
add_spatial_rook(): Add rook adjacency from polygons
add_spatial_queen(): Add queen adjacency from polygons
add_spatial_knn(): Add k-nearest-neighbours spatial relations
add_spatial_distance(): Add distance-threshold spatial relations

Atomic objectives

Add ecological, economic, intervention, and spatial objectives to the planning problem.

add_objective_min_cost(): Add objective: minimize cost
add_objective_max_benefit(): Add objective: maximize benefit
add_objective_min_loss(): Add objective: minimize loss
add_objective_max_profit(): Add objective: maximize profit
add_objective_max_net_profit(): Add objective: maximize net profit
add_objective_min_intervention_impact(): Add objective: minimize intervention impact
add_objective_min_fragmentation_planning_units(): Add objective: minimize planning-unit fragmentation
add_objective_min_fragmentation_action(): Add objective: minimize action fragmentation

Multi-objective methods and run designs

Choose how multiple objectives are explored and define the combinations of weights or constraints to evaluate.

set_method_weighted_sum(): Set the weighted-sum multi-objective method
set_method_epsilon_constraint(): Set the epsilon-constraint multi-objective method
set_method_augmecon(): Set the AUGMECON multi-objective method
set_runs_grid(): Define an automatic multi-objective run grid
set_runs_manual(): Define a manual multi-objective run design
set_runs_control(): Control multi-objective run behavior

Solvers and optimization

Select and configure a mixed-integer programming solver, then solve the planning problem.

set_solver(): Configure solver settings
set_solver_gurobi(): Configure Gurobi solver settings
set_solver_cplex(): Configure CPLEX solver settings
set_solver_cbc(): Configure CBC solver settings
set_solver_symphony(): Configure SYMPHONY solver settings
solve(): Solve a planning problem

Inspect solutions

Extract run metadata, objective values, spatial decisions, features, and target outcomes from stored solutions.

get_runs(): Get run-level metadata from a solution set
get_objectives(): Get objective values from a solution set
get_planning_units(): Get planning-unit results from a solution set
get_actions(): Get action results from a solution set
get_solution_states(): Get planning-unit states from stored solutions
get_features(): Get feature summary from a solution set
get_targets(): Get target achievement summary from a solution set

Manage solution sets

Filter, combine, and remove duplicate alternatives before analysis or reporting.

solution_filter(): Filter solutions in a solution set
solution_append(): Append solutions from another solution set
solution_unique(): Keep unique solutions in a solution set

Objective-space analysis

Characterize performance trade-offs, empirical extremes, distances, knees, and neighboring alternatives in objective space.

frontier_extremes(): Find objective-wise extreme solutions
frontier_distances(): Compute distances to observed ideal or nadir points
frontier_knee(): Identify knee solutions on an observed Pareto frontier
frontier_neighbors(): Identify neighboring solutions in objective space

Decision-space analysis

Quantify recurrence, similarity, and consistency in spatial decisions across alternative solutions.

selection_frequency(): Calculate selection frequency across solutions
selection_similarity(): Calculate structural similarity among solutions
selection_consistency(): Summarize consistency of planning-unit states

Objective-decision linkage

Relate changes in objective performance to changes in spatial prescriptions and identify informative solution contrasts.

linkage_distances(): Compare objective and decision distances
linkage_transition(): Describe the transition between two solutions
linkage_turnover(): Measure decision turnover along an objective-space neighborhood
linkage_contrasts(): Select informative solution contrasts

Visualize results

Plot objective trade-offs, planning-unit selections, management actions, and spatial feature distributions.

plot_tradeoff(): Plot trade-offs from a solution set
plot_spatial_planning_units(): Plot selected planning units in space
plot_spatial_actions(): Plot selected actions in space
plot_spatial_features(): Plot spatial feature values from a solution set

Core objects

Classes used to represent planning problems and their resulting solution sets.

problem-class Problem: Problem class
solutionset-class SolutionSet: SolutionSet class

Advanced model inspection

Compile and inspect the underlying optimization model.

compile_model(): Compile the optimization model stored in a Problem

Example data

Simulated datasets and data loaders used in package examples and documentation.

sim_dist_features: Simulated feature distribution
sim_features: Simulated features
sim_multiaction: Simulated spatial multi-action planning inputs
sim_pu: Simulated planning units
sim_pu_sf: Simulated planning units
load_sim_features_raster(): Example feature raster
load_sim_multiaction(): Load the simulated spatial multi-action example

Deprecated functions

Functions retained temporarily for backward compatibility.

add_constraint_locked_pu() obsoleta: Add locked planning units to a problem
add_objective_min_fragmentation_pu() obsoleta: Add objective: minimize planning-unit fragmentation
get_pu() obsoleta: Get planning-unit results from a solution set
plot_spatial_pu() obsoleta: Plot selected planning units in space
run_grid() obsoleta: Define an automatic multi-objective run grid
run_manual() obsoleta: Define a manual multi-objective run design
mo_control() obsoleta: Control multi-objective run behavior