Calculate how well feature representation targets are met by a solution to a conservation planning problem(). It is useful for understanding if features are adequately represented by a solution. Note that this function can only be used with problems that contain targets.

eval_target_coverage_summary(x, solution, include_zone, include_sense)

# S3 method for default
eval_target_coverage_summary(x, solution, include_zone, include_sense)

# S3 method for ConservationProblem
eval_target_coverage_summary(
  x,
  solution,
  include_zone = number_of_zones(x) > 1,
  include_sense = number_of_zones(x) > 1
)

Arguments

x

problem() (i.e. ConservationProblem) object.

solution

numeric, matrix, data.frame, Raster, Spatial, or sf::sf() object. The argument should be in the same format as the planning unit cost data in the argument to x. See the Solution format section for more information.

include_zone

logical include the zone column in the output? Defaults to TRUE for problems that contain multiple zones.

include_sense

logical include the sense column in the output? Defaults to TRUE for problems that contain multiple zones.

Value

tibble::tibble() object. Here, each row describes information for a different target. It contains the following columns:

feature

character name of the feature associated with each target.

zone

list of character zone names associated with each target. This column is in a list-column format because a single target can correspond to multiple zones (see add_manual_targets() for details and examples). For an example of converting the list-column format to a standard character column format, please see the Examples section. This column is only included if the argument to include_zones is TRUE.

sense

character sense associated with each target. Sense values specify the nature of the target. Typically (e.g. when using the add_absolute_targets() or add_relative_targets() functions), targets are specified using sense values indicating that the total amount of a feature held within a solution (ideally) be greater than or equal to a threshold amount (i.e. a sense value of ">="). Additionally, targets (i.e. using the add_manual_targets() function) can also be specified using sense values indicating that the total amount of a feature held within a solution must be equal to a threshold amount (i.e. a sense value of "=") or smaller than or equal to a threshold amount (i.e. a sense value of "<="). This column is only included if the argument to include_sense is TRUE.

total_amount

numeric total amount of the feature available across the entire conservation planning problem for meeting each target (not just planning units selected within the solution). For problems involving a single zone, this column is calculated as the sum of all of the values for a given feature (similar to values in the total_amount column produced by the eval_feature_representation_summary() function). For problems involving multiple zones, this column is calculated as the sum of the values for the feature associated with target (per the "feature" column), across the zones associated with the target (per the "zone" column).

absolute_target

numeric total threshold amount associated with each target.

absolute_held

numeric total amount held within the solution for the feature and (if relevant) zones associated with each target (per the "feature" and "zone" columns, respectively). This column 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).

absolute_shortfall

numeric total amount by which the solution fails to meet each target. This column is calculated as the difference between the total amount held within the solution for the feature and (if relevant) zones associated with the target (i.e. "absolute_held" column) and the target total threshold amount (i.e. "absolute_target" column), with values set to zero depending on the sense specified for the target (e.g. if the target sense is >= then the difference is set to zero if the value in the "absolute_held" is smaller than that in the "absolute_target" column).

relative_target

numeric proportion threshold amount associated with each target. This column is calculated by dividing the total threshold amount associated with each target (i.e. "absolute_target" column) by the total amount associated with each target (i.e. "total_amount" column).

relative_held

numeric proportion held within the solution for the feature and (if relevant) zones associated with each target (per the "feature" and "zone" columns, respectively). This column is calculated by dividing the total amount held for each target (i.e. "absolute_held" column) by the total amount for with each target (i.e. "total_amount" column).

relative_shortfall

numeric proportion by which the solution fails to meet each target. This column is calculated by dividing the total shortfall for each target (i.e. "absolute_shortfall" column) by the total amount for each target (i.e. "total_amount" column).

met

logical indicating if each target is met by the solution. This column is calculated by checking if the total shortfall associated with each target (i.e. "absolute_shortfall" column) is equal to zero.

Solution format

The argument to solution must be in the same format as the planning unit data in the argument to x (e.g. in terms of data representation, dimensionality, and spatial attributes). For example, if the planning unit data in x is a numeric vector, then the argument to solution must be a numeric vector with the same number of elements. Similarly, if the planning units in x are a data.frame, then the argument to solution must also 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. Additionally, if the planning unit data in x is a Raster object, then the argument to solution must also be a Raster object with the same dimensionality (rows and columns), resolution, extent, and coordinate reference system. Furthermore, if the planning unit data in x is a Spatial or sf::sf() object then the argument to solution must also be a Spatial or sf::sf() object (respectively) with the same spatial information (e.g. polygons and coordinate reference system), and contain columns corresponding to different zones, and cell values corresponding to the solution values.

The argument to solution must also have missing (NA) values for planning units that have missing (NA) cost values. In other words, the solution must have missing (NA) values in the same elements, cells, or pixels (depending on the cost data format) as the planning unit cost data. For example, if the planning unit data are a Raster object, then the argument to solution must have missing (NA) values in the same pixels as the planning unit cost data. Similarly, if the planning unit data are a Spatial, sf::sf(), or data.frame object, then the solution must have missing (NA) values in the same cells as the planning unit cost data columns. If an argument is supplied to solution where the missing (NA) values in the argument to solution do not match those in the planning unit cost data, then an error will be thrown.

See also

Examples

# \dontrun{ # set seed for reproducibility set.seed(500) # load data data(sim_pu_raster, sim_pu_sf, sim_features, sim_pu_zones_sf, sim_features_zones) # build minimal conservation problem with raster data p1 <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # solve the problem s1 <- solve(p1) # print solution print(s1)
#> class : RasterLayer #> dimensions : 10, 10, 100 (nrow, ncol, ncell) #> resolution : 0.1, 0.1 (x, y) #> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax) #> crs : NA #> source : memory #> names : layer #> values : 0, 1 (min, max) #>
# plot solution plot(s1, main = "solution", axes = FALSE, box = FALSE)
# calculate target coverage by the solution r1 <- eval_target_coverage_summary(p1, s1) print(r1, width = Inf) # note: `width = Inf` tells R to print all columns
#> # A tibble: 5 x 9 #> feature met total_amount absolute_target absolute_held absolute_shortfall #> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> #> 1 layer.1 TRUE 83.3 8.33 8.91 0 #> 2 layer.2 TRUE 31.2 3.12 3.13 0 #> 3 layer.3 TRUE 72.0 7.20 7.34 0 #> 4 layer.4 TRUE 42.7 4.27 4.35 0 #> 5 layer.5 TRUE 56.7 5.67 6.01 0 #> relative_target relative_held relative_shortfall #> <dbl> <dbl> <dbl> #> 1 0.1 0.107 0 #> 2 0.1 0.100 0 #> 3 0.1 0.102 0 #> 4 0.1 0.102 0 #> 5 0.1 0.106 0
# build minimal conservation problem with polygon (sf) data p2 <- problem(sim_pu_sf, sim_features, cost_column = "cost") %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # solve the problem s2 <- solve(p2) # print first six rows of the attribute table print(head(s2))
#> Simple feature collection with 6 features and 4 fields #> geometry type: POLYGON #> dimension: XY #> bbox: xmin: 0 ymin: 0.9 xmax: 0.6 ymax: 1 #> CRS: NA #> cost locked_in locked_out solution_1 geometry #> 1 215.8638 FALSE FALSE 0 POLYGON ((0 1, 0.1 1, 0.1 0... #> 2 212.7823 FALSE FALSE 0 POLYGON ((0.1 1, 0.2 1, 0.2... #> 3 207.4962 FALSE FALSE 0 POLYGON ((0.2 1, 0.3 1, 0.3... #> 4 208.9322 FALSE TRUE 0 POLYGON ((0.3 1, 0.4 1, 0.4... #> 5 214.0419 FALSE FALSE 0 POLYGON ((0.4 1, 0.5 1, 0.5... #> 6 213.7636 FALSE FALSE 0 POLYGON ((0.5 1, 0.6 1, 0.6...
# plot solution plot(s2[, "solution_1"])
# calculate target coverage by the solution r2 <- eval_target_coverage_summary(p2, s2[, "solution_1"]) print(r2, width = Inf)
#> # A tibble: 5 x 9 #> feature met total_amount absolute_target absolute_held absolute_shortfall #> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> #> 1 layer.1 TRUE 74.5 7.45 8.05 0 #> 2 layer.2 TRUE 28.1 2.81 2.83 0 #> 3 layer.3 TRUE 64.9 6.49 6.65 0 #> 4 layer.4 TRUE 38.2 3.82 3.87 0 #> 5 layer.5 TRUE 50.7 5.07 5.41 0 #> relative_target relative_held relative_shortfall #> <dbl> <dbl> <dbl> #> 1 0.1 0.108 0 #> 2 0.1 0.101 0 #> 3 0.1 0.103 0 #> 4 0.1 0.101 0 #> 5 0.1 0.107 0
# build multi-zone conservation problem with polygon (sf) data p3 <- problem(sim_pu_zones_sf, sim_features_zones, 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 the problem s3 <- solve(p3) # print first six rows of the attribute table print(head(s3))
#> Simple feature collection with 6 features and 9 fields #> geometry type: POLYGON #> dimension: XY #> bbox: xmin: 0 ymin: 0.9 xmax: 0.6 ymax: 1 #> CRS: NA #> cost_1 cost_2 cost_3 locked_1 locked_2 locked_3 solution_1_zone_1 #> 1 215.8638 183.3344 205.4113 FALSE FALSE FALSE 0 #> 2 212.7823 189.4978 209.6404 FALSE FALSE FALSE 0 #> 3 207.4962 193.6007 215.4212 TRUE FALSE FALSE 0 #> 4 208.9322 197.5897 218.5241 FALSE FALSE FALSE 0 #> 5 214.0419 199.8033 220.7100 FALSE FALSE FALSE 0 #> 6 213.7636 203.1867 224.6809 FALSE FALSE FALSE 0 #> solution_1_zone_2 solution_1_zone_3 geometry #> 1 0 1 POLYGON ((0 1, 0.1 1, 0.1 0... #> 2 0 0 POLYGON ((0.1 1, 0.2 1, 0.2... #> 3 0 0 POLYGON ((0.2 1, 0.3 1, 0.3... #> 4 0 0 POLYGON ((0.3 1, 0.4 1, 0.4... #> 5 0 0 POLYGON ((0.4 1, 0.5 1, 0.5... #> 6 1 0 POLYGON ((0.5 1, 0.6 1, 0.6...
# create new column representing the zone id that each planning unit # was allocated to in the solution s3$solution <- category_vector( s3[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]) s3$solution <- factor(s3$solution) # plot solution plot(s3[, "solution"])
# calculate target coverage by the solution r3 <- eval_target_coverage_summary( p3, s3[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]) print(r3, width = Inf)
#> # A tibble: 15 x 11 #> feature zone sense met total_amount absolute_target absolute_held #> <chr> <list> <chr> <lgl> <dbl> <dbl> <dbl> #> 1 feature_1 <chr [1]> >= TRUE 75.1 13.8 15.2 #> 2 feature_2 <chr [1]> >= TRUE 28.0 4.82 5.18 #> 3 feature_3 <chr [1]> >= TRUE 65.0 12.8 12.8 #> 4 feature_4 <chr [1]> >= TRUE 38.0 5.58 6.56 #> 5 feature_5 <chr [1]> >= TRUE 51.2 9.27 10.2 #> 6 feature_1 <chr [1]> >= TRUE 75.1 9.06 14.2 #> 7 feature_2 <chr [1]> >= TRUE 28.0 4.23 5.43 #> 8 feature_3 <chr [1]> >= TRUE 65.0 12.5 13.0 #> 9 feature_4 <chr [1]> >= TRUE 38.0 6.96 6.98 #> 10 feature_5 <chr [1]> >= TRUE 51.2 8.76 9.23 #> 11 feature_1 <chr [1]> >= TRUE 75.1 9.63 13.2 #> 12 feature_2 <chr [1]> >= TRUE 28.0 5.29 5.30 #> 13 feature_3 <chr [1]> >= TRUE 65.0 11.5 11.5 #> 14 feature_4 <chr [1]> >= TRUE 38.0 4.43 6.76 #> 15 feature_5 <chr [1]> >= TRUE 51.2 8.86 8.88 #> absolute_shortfall relative_target relative_held relative_shortfall #> <dbl> <dbl> <dbl> <dbl> #> 1 0 0.183 0.202 0 #> 2 0 0.173 0.185 0 #> 3 0 0.198 0.198 0 #> 4 0 0.147 0.173 0 #> 5 0 0.181 0.200 0 #> 6 0 0.121 0.189 0 #> 7 0 0.151 0.194 0 #> 8 0 0.193 0.201 0 #> 9 0 0.183 0.183 0 #> 10 0 0.171 0.180 0 #> 11 0 0.128 0.175 0 #> 12 0 0.189 0.189 0 #> 13 0 0.176 0.177 0 #> 14 0 0.116 0.178 0 #> 15 0 0.173 0.173 0
# create a new column with character values containing the zone names, # by extracting these data out of the zone column # (which is in list-column format) r3$zone2 <- vapply(r3$zone, FUN.VALUE = character(1), paste, sep = " & ") # print r3 again to show the new column print(r3, width = Inf)
#> # A tibble: 15 x 12 #> feature zone sense met total_amount absolute_target absolute_held #> <chr> <list> <chr> <lgl> <dbl> <dbl> <dbl> #> 1 feature_1 <chr [1]> >= TRUE 75.1 13.8 15.2 #> 2 feature_2 <chr [1]> >= TRUE 28.0 4.82 5.18 #> 3 feature_3 <chr [1]> >= TRUE 65.0 12.8 12.8 #> 4 feature_4 <chr [1]> >= TRUE 38.0 5.58 6.56 #> 5 feature_5 <chr [1]> >= TRUE 51.2 9.27 10.2 #> 6 feature_1 <chr [1]> >= TRUE 75.1 9.06 14.2 #> 7 feature_2 <chr [1]> >= TRUE 28.0 4.23 5.43 #> 8 feature_3 <chr [1]> >= TRUE 65.0 12.5 13.0 #> 9 feature_4 <chr [1]> >= TRUE 38.0 6.96 6.98 #> 10 feature_5 <chr [1]> >= TRUE 51.2 8.76 9.23 #> 11 feature_1 <chr [1]> >= TRUE 75.1 9.63 13.2 #> 12 feature_2 <chr [1]> >= TRUE 28.0 5.29 5.30 #> 13 feature_3 <chr [1]> >= TRUE 65.0 11.5 11.5 #> 14 feature_4 <chr [1]> >= TRUE 38.0 4.43 6.76 #> 15 feature_5 <chr [1]> >= TRUE 51.2 8.86 8.88 #> absolute_shortfall relative_target relative_held relative_shortfall zone2 #> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 0 0.183 0.202 0 zone_1 #> 2 0 0.173 0.185 0 zone_1 #> 3 0 0.198 0.198 0 zone_1 #> 4 0 0.147 0.173 0 zone_1 #> 5 0 0.181 0.200 0 zone_1 #> 6 0 0.121 0.189 0 zone_2 #> 7 0 0.151 0.194 0 zone_2 #> 8 0 0.193 0.201 0 zone_2 #> 9 0 0.183 0.183 0 zone_2 #> 10 0 0.171 0.180 0 zone_2 #> 11 0 0.128 0.175 0 zone_3 #> 12 0 0.189 0.189 0 zone_3 #> 13 0 0.176 0.177 0 zone_3 #> 14 0 0.116 0.178 0 zone_3 #> 15 0 0.173 0.173 0 zone_3
# }