summaries {prioritizr}

Evaluate solutions using summary statistics

Description

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.

Details

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 that 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 using symmetric data.

eval_asym_connectivity_summary()

Calculate the connectivity held within a solution using asymmetric data.

See Also

Other overviews: constraints, decisions, importance, objectives, penalties, portfolios, solvers, targets

Examples

## Not run: 
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_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)

# 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 symmetric 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 using symmetric data
eval_connectivity_summary(p, s, data = cm)

# create an asymmetric connectivity matrix to describe pair-wise
# connectivity values between combinations of planning units

# for brevity, we will just generate a matrix with random values
acm <- matrix(
  runif(ncell(sim_pu_raster) ^ 2),
  ncol = terra::ncell(sim_pu_raster)
)

# evaluate connectivity of solution using asymmetric data
eval_asym_connectivity_summary(p, s, data = acm)


## End(Not run)