eval_feature_representation_summary: Evaluate feature representation by solution
In prioritizr: Systematic Conservation Prioritization in R

View source: R/eval_feature_representation_summary.R

eval_feature_representation_summary

R Documentation

Evaluate feature representation by solution

Description

Calculate how well features are represented by a solution to a conservation planning problem. These summary statistics are reported for each and every feature, and each and every zone, within a conservation planning problem.

Usage

eval_feature_representation_summary(x, solution)

## S3 method for class 'ConservationProblem'
eval_feature_representation_summary(x, solution)

## S3 method for class 'MultiConservationProblem'
eval_feature_representation_summary(x, solution)

Arguments

`x`	`problem()` or `multi_problem()` object.
`solution`	`numeric`, `matrix`, `data.frame`, `terra::rast()`, or `sf::sf()` object. Note that `solution` must have the same format as the planning unit data in `x`. See the Solution format section for more information.

Value

A tibble::tibble() object describing feature representation by the solution. Here, each row describes a specific summary statistic (e.g., different management zone) for a specific feature. It contains the following columns.

problem: character name of problem. Note that this column is only present if x is a multi_problem() object.
summary: character description of the summary statistic. The statistics associated with the "overall" value in this column are calculated using all planning unit values. If x has multiple management zones, this means that all calculations are completed by summing together all planning unit values across all zones. For example, if there are two zones, a single planning unit, and a feature has a value of one in the single planning unit for both zones, then total_amount will contain a value of two (even though it would not be possible to to achieve a value of two because the planning unit could not simultaneously be allocated to both zones). Additionally, if x has multiple management zones, then summary statistics are also provided for each zone separately (indicated using zone names).
feature: character name of the feature.
total_amount: numeric total amount of each feature available in the entire conservation planning problem (not just planning units selected within the solution). It is calculated as the sum of the feature data, supplied when creating a problem() object (e.g., presence/absence values).
absolute_held: numeric total amount of each feature secured within the solution. It is calculated as the sum of the feature data, supplied when creating a problem() object (e.g., presence/absence values), weighted by the status of each planning unit in the solution (e.g., selected or not for prioritization).
relative_held: numeric proportion of each feature secured within the solution. It is calculated by dividing values in the "absolute_held" column by those in the "total_amount" column.

Solution format

Broadly speaking, solution must be in the same format as the planning unit data in x. Further details on the correct format are listed separately for each of the different planning unit data formats.

x has numeric planning units: Here solution must be a numeric vector with each element corresponding to a different planning unit. It should have the same number of planning units as those in x. Additionally, any planning units with missing cost (NA) values should also have missing (NA) values in the solution.
x has matrix planning units: Here solution must be a matrix vector with each row corresponding to a different planning unit, and each column correspond to a different management zone. It should have the same number of planning units and zones as those in x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have a missing (NA) values in solution.
x has terra::rast() planning units: Here solution be a terra::rast() object where different cells correspond to different planning units and layers correspond to a different management zones. It should have the same dimensionality (rows, columns, layers), resolution, extent, and coordinate reference system as the planning units in x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.
x has data.frame planning units: Here solution must be a data.frame with each column corresponding to a different zone, each row corresponding to a different planning unit, and cell values corresponding to the solution value. This means that if a data.frame object containing the solution also contains additional columns, then these columns will need to be subsetted prior to using this function (see below for example with sf::sf() data). Additionally, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.
x has sf::sf() planning units: Here solution must be a sf::sf() object with each column corresponding to a different zone, each row corresponding to a different planning unit, and cell values corresponding to the solution value. This means that if the sf::sf() object containing the solution also contains additional columns, then these columns will need to be subsetted prior to using this function (see below for example). Additionally, solution must also have the same coordinate reference system as the planning unit data. Furthermore, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

Examples


# set seed for reproducibility
set.seed(500)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_pu_polygons <- get_sim_zones_pu_polygons()
sim_zones_features <- get_sim_zones_features()

# create a simple conservation planning dataset so we can see exactly
# how feature representation is calculated
pu <- data.frame(
  id = seq_len(10),
  cost = c(0.2, NA, runif(8)),
  spp1 = runif(10),
  spp2 = c(rpois(9, 4), NA)
)

# create problem
p1 <-
  problem(pu, c("spp1", "spp2"), cost_column = "cost") %>%
  add_min_set_objective() %>%
  add_relative_targets(0.1) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# create a solution
# specifically, a data.frame with a single column that contains
# binary values indicating if each planning units was selected or not
s1 <- data.frame(s = c(1, NA, rep(c(1, 0), 4)))
print(s1)

# calculate feature representation
r1 <- eval_feature_representation_summary(p1, s1)
print(r1)

# let's verify that feature representation calculations are correct
# by manually performing the calculations and compare the results with r1
## calculate total amount for each feature
print(
  setNames(
    c(sum(pu$spp1, na.rm = TRUE), sum(pu$spp2, na.rm = TRUE)),
    c("spp1", "spp2")
  )
)

## calculate absolute amount held for each feature
print(
  setNames(
    c(sum(pu$spp1 * s1$s, na.rm = TRUE), sum(pu$spp2 * s1$s, na.rm = TRUE)),
    c("spp1", "spp2")
  )
)

## calculate relative amount held for each feature
print(
  setNames(
    c(
      sum(pu$spp1 * s1$s, na.rm = TRUE) / sum(pu$spp1, na.rm = TRUE),
      sum(pu$spp2 * s1$s, na.rm = TRUE) / sum(pu$spp2, na.rm = TRUE)
    ),
    c("spp1", "spp2")
  )
)

# solve problem using an exact algorithm solver
s1_2 <- solve(p1)
print(s1_2)

# calculate feature representation in this solution
r1_2 <- eval_feature_representation_summary(
  p1, s1_2[, "solution_1", drop = FALSE]
)
print(r1_2)

# build minimal conservation problem with raster data
p2 <-
  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
s2 <- solve(p2)

# print solution
print(s2)

# calculate feature representation in the solution
r2 <- eval_feature_representation_summary(p2, s2)
print(r2)

# plot solution
plot(s2, main = "solution", axes = FALSE)

# build minimal conservation problem with polygon data
p3 <-
  problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
  add_min_set_objective() %>%
  add_relative_targets(0.1) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s3 <- solve(p3)

# print first six rows of the attribute table
print(head(s3))

# calculate feature representation in the solution
r3 <- eval_feature_representation_summary(p3, s3[, "solution_1"])
print(r3)

# plot solution
plot(s3[, "solution_1"], main = "solution", axes = FALSE)

# build multi-zone conservation problem with raster data
p4 <-
  problem(sim_zones_pu_raster, sim_zones_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s4 <- solve(p4)

# print solution
print(s4)

# calculate feature representation in the solution
r4 <- eval_feature_representation_summary(p4, s4)
print(r4)

# plot solution
plot(category_layer(s4), main = "solution", axes = FALSE)

# build multi-zone conservation problem with polygon data
p5 <-
  problem(
    sim_zones_pu_polygons, sim_zones_features,
    cost_column = c("cost_1", "cost_2", "cost_3")
  ) %>%
  add_min_set_objective() %>%
  add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5, ncol = 3)) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s5 <- solve(p5)

# print first six rows of the attribute table
print(head(s5))

# calculate feature representation in the solution
r5 <- eval_feature_representation_summary(
  p5, s5[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
)
print(r5)

# create new column representing the zone id that each planning unit
# was allocated to in the solution
s5$solution <- category_vector(
  s5[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
)
s5$solution <- factor(s5$solution)

# plot solution
plot(s5[, "solution"])

prioritizr documentation built on Sept. 24, 2026, 5:07 p.m.