Evaluate target coverage

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

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

x has numeric planning units: The argument to 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 the argument to x. Additionally, any planning units missing cost (NA) values should also have missing (NA) values in the argument to solution.
x has matrix planning units: The argument to 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 the argument to x. Additionally, any planning units missing cost (NA) values for a particular zone should also have a missing (NA) values in the argument to solution.
x has Raster planning units: The argument to solution be a Raster object where different grid cells (pixels) 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 the argument to x. Additionally, any planning units missing cost (NA) values for a particular zone should also have missing (NA) values in the argument to solution.
x has data.frame planning units: The argument to 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 missing cost (NA) values for a particular zone should also have missing (NA) values in the argument to solution.
x has Spatial planning units: The argument to solution must be a Spatial 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 Spatial 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, the argument to solution must also have the same coordinate reference system as the planning unit data. Furthermore, any planning units missing cost (NA) values for a particular zone should also have missing (NA) values in the argument to solution.
x has sf::sf() planning units: The argument to 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, the argument to solution must also have the same coordinate reference system as the planning unit data. Furthermore, any planning units missing cost (NA) values for a particular zone should also have missing (NA) values in the argument to solution.

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 × 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
#> Bounding box:  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 × 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
#> Bounding box:  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                 0 POLYGON ((0 1, 0.1 1, 0.1 0...
#> 2                 0                 1 POLYGON ((0.1 1, 0.2 1, 0.2...
#> 3                 0                 0 POLYGON ((0.2 1, 0.3 1, 0.3...
#> 4                 0                 1 POLYGON ((0.3 1, 0.4 1, 0.4...
#> 5                 0                 1 POLYGON ((0.4 1, 0.5 1, 0.5...
#> 6                 0                 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 × 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          14.9 
#>  2 feature_2 <chr [1]> >=    TRUE          28.0            4.82          5.14
#>  3 feature_3 <chr [1]> >=    TRUE          65.0           12.8          12.8 
#>  4 feature_4 <chr [1]> >=    TRUE          38.0            5.58          6.64
#>  5 feature_5 <chr [1]> >=    TRUE          51.2            9.27          9.96
#>  6 feature_1 <chr [1]> >=    TRUE          75.1            9.06         15.0 
#>  7 feature_2 <chr [1]> >=    TRUE          28.0            4.23          5.58
#>  8 feature_3 <chr [1]> >=    TRUE          65.0           12.5          13.6 
#>  9 feature_4 <chr [1]> >=    TRUE          38.0            6.96          7.14
#> 10 feature_5 <chr [1]> >=    TRUE          51.2            8.76          9.96
#> 11 feature_1 <chr [1]> >=    TRUE          75.1            9.63         13.3 
#> 12 feature_2 <chr [1]> >=    TRUE          28.0            5.29          5.65
#> 13 feature_3 <chr [1]> >=    TRUE          65.0           11.5          11.8 
#> 14 feature_4 <chr [1]> >=    TRUE          38.0            4.43          7.71
#> 15 feature_5 <chr [1]> >=    TRUE          51.2            8.86          9.04
#>    absolute_shortfall relative_target relative_held relative_shortfall
#>                 <dbl>           <dbl>         <dbl>              <dbl>
#>  1                  0           0.183         0.198                  0
#>  2                  0           0.173         0.184                  0
#>  3                  0           0.198         0.198                  0
#>  4                  0           0.147         0.175                  0
#>  5                  0           0.181         0.195                  0
#>  6                  0           0.121         0.200                  0
#>  7                  0           0.151         0.200                  0
#>  8                  0           0.193         0.210                  0
#>  9                  0           0.183         0.188                  0
#> 10                  0           0.171         0.195                  0
#> 11                  0           0.128         0.177                  0
#> 12                  0           0.189         0.202                  0
#> 13                  0           0.176         0.181                  0
#> 14                  0           0.116         0.203                  0
#> 15                  0           0.173         0.177                  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 × 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          14.9 
#>  2 feature_2 <chr [1]> >=    TRUE          28.0            4.82          5.14
#>  3 feature_3 <chr [1]> >=    TRUE          65.0           12.8          12.8 
#>  4 feature_4 <chr [1]> >=    TRUE          38.0            5.58          6.64
#>  5 feature_5 <chr [1]> >=    TRUE          51.2            9.27          9.96
#>  6 feature_1 <chr [1]> >=    TRUE          75.1            9.06         15.0 
#>  7 feature_2 <chr [1]> >=    TRUE          28.0            4.23          5.58
#>  8 feature_3 <chr [1]> >=    TRUE          65.0           12.5          13.6 
#>  9 feature_4 <chr [1]> >=    TRUE          38.0            6.96          7.14
#> 10 feature_5 <chr [1]> >=    TRUE          51.2            8.76          9.96
#> 11 feature_1 <chr [1]> >=    TRUE          75.1            9.63         13.3 
#> 12 feature_2 <chr [1]> >=    TRUE          28.0            5.29          5.65
#> 13 feature_3 <chr [1]> >=    TRUE          65.0           11.5          11.8 
#> 14 feature_4 <chr [1]> >=    TRUE          38.0            4.43          7.71
#> 15 feature_5 <chr [1]> >=    TRUE          51.2            8.86          9.04
#>    absolute_shortfall relative_target relative_held relative_shortfall zone2 
#>                 <dbl>           <dbl>         <dbl>              <dbl> <chr> 
#>  1                  0           0.183         0.198                  0 zone_1
#>  2                  0           0.173         0.184                  0 zone_1
#>  3                  0           0.198         0.198                  0 zone_1
#>  4                  0           0.147         0.175                  0 zone_1
#>  5                  0           0.181         0.195                  0 zone_1
#>  6                  0           0.121         0.200                  0 zone_2
#>  7                  0           0.151         0.200                  0 zone_2
#>  8                  0           0.193         0.210                  0 zone_2
#>  9                  0           0.183         0.188                  0 zone_2
#> 10                  0           0.171         0.195                  0 zone_2
#> 11                  0           0.128         0.177                  0 zone_3
#> 12                  0           0.189         0.202                  0 zone_3
#> 13                  0           0.176         0.181                  0 zone_3
#> 14                  0           0.116         0.203                  0 zone_3
#> 15                  0           0.173         0.177                  0 zone_3
# }

Arguments

Value

Solution format

See also

Examples