Evaluate solutions using summary statistics
After generating a solution to a conservation planning problem(),
it can be useful to evaluate how well it performs. These functions
can be used to evaluate a solution according to
various different summary statistics.
The following functions can be used to summarize the performance
of a solution to a conservation planning problem():
eval_n_summary()Calculate the number of planning units selected within a solution.
eval_cost_summary()Calculate the total cost of a solution.
eval_feature_representation_summary()Calculate how well features are represented by a solution. This function can be used for all problems.
eval_target_coverage_summary()Calculate how well feature representation targets are met by a solution. This function can only be used with problems contain targets.
eval_boundary_summary()Calculate the exposed boundary length (perimeter) associated with a solution.
eval_connectivity_summary()Calculate the connectivity held within a solution.
# load data
data(sim_pu_raster, sim_features)
# create a minimal problem
p <- problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
## Not run:
# solve problem
s <- solve(p)
# evaluate number of selected planning units in solution
eval_n_summary(p, s)
# evaluate solution cost
eval_cost_summary(p, s)
# evaluate feature representation by solution
eval_feature_representation_summary(p, s)
# evaluate target coverage by solution
eval_target_coverage_summary(p, s)
# evaluate exposed boundary (perimeter) length by solution
eval_boundary_summary(p, s)
# create a connectivity matrix to describe pair-wise connectivity
# values between combinations of planning units,
# see ?connectivity_matrix for more information
# for brevity, we will do this using the cost data
# cost valuers have high connectivity between them
cm <- connectivity_matrix(sim_pu_raster, sim_pu_raster)
# evaluate connectivity of solution
eval_connectivity_summary(p, s, data = cm)
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