`R/eval_ferrier_importance.R`

`eval_ferrier_importance.Rd`

Calculate importance scores for planning units selected in
a solution following Ferrier *et al.* (2000).

```
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,numeric
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,matrix
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,data.frame
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,Spatial
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,sf
eval_ferrier_importance(x, solution)
# S4 method for ConservationProblem,Raster
eval_ferrier_importance(x, solution)
```

- 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.

A `matrix`

, `tibble::tibble()`

,
`RasterLayer`

, or
`Spatial`

object containing the scores for each
planning unit selected in the solution.
Specifically, the returned object is in the
same format (except if the planning units are a `numeric`

vector) as the
planning unit data in the argument to `x`

.

Importance scores are reported separately for each feature within each planning unit. Additionally, a total importance score is also calculated as the sum of the scores for each feature. Note that this function only works for problems with a minimum set objective and a single zone. It will throw an error for problems that do not meet this criteria.

In previous versions, the documentation for this function had a warning indicating that the mathematical formulation for this function required verification. The mathematical formulation for this function has since been corrected and verified, so now this function is recommended for general use.

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 unitsThe 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 unitsThe 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 unitsThe 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 unitsThe 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 unitsThe 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 unitsThe 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`

.

Ferrier S, Pressey RL, and Barrett TW (2000) A new predictor of the
irreplaceability of areas for achieving a conservation goal, its application
to real-world planning, and a research agenda for further refinement.
*Biological Conservation*, 93: 303--325.

See importance for an overview of all functions for evaluating the importance of planning units selected in a solution.

Other importances:
`eval_rare_richness_importance()`

,
`eval_replacement_importance()`

```
# seed seed for reproducibility
set.seed(600)
# load data
data(sim_pu_raster, sim_features)
# create minimal problem with binary decisions
p1 <- problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(gap = 0, verbose = FALSE)
# \dontrun{
# solve 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 importance scores using Ferrier et al. 2000 method
fs1 <- eval_ferrier_importance(p1, s1)
# print importance scores,
# each planning unit has an importance score for each feature
# (as indicated by the column names) and each planning unit also
# has an overall total importance score (in the "total" column)
print(fs1)
#> class : RasterStack
#> dimensions : 10, 10, 100, 6 (nrow, ncol, ncell, nlayers)
#> resolution : 0.1, 0.1 (x, y)
#> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
#> crs : NA
#> names : layer.1, layer.2, layer.3, layer.4, layer.5, total
#> min values : 0, 0, 0, 0, 0, 0
#> max values : 0.003472042, 0.003596401, 0.003341572, 0.003768489, 0.003504223, 0.016417187
#>
# plot total importance scores
plot(fs1, main = "Ferrier scores", axes = FALSE, box = FALSE)
# create minimal problem with polygon (sf) planning units
p2 <- problem(sim_pu_sf, sim_features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.05) %>%
add_binary_decisions() %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s2 <- solve(p2)
# print solution
print(s2)
#> Simple feature collection with 90 features and 4 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: 0 xmax: 1 ymax: 1
#> CRS: NA
#> First 10 features:
#> 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...
#> 7 210.4612 FALSE FALSE 0 POLYGON ((0.6 1, 0.7 1, 0.7...
#> 8 211.0424 FALSE TRUE 0 POLYGON ((0.7 1, 0.8 1, 0.8...
#> 9 210.3878 FALSE FALSE 0 POLYGON ((0.8 1, 0.9 1, 0.9...
#> 10 204.3971 FALSE FALSE 0 POLYGON ((0.9 1, 1 1, 1 0.9...
# plot solution
plot(s2[, "solution_1"], main = "solution")
# calculate importance scores
fs2 <- eval_ferrier_importance(p2, s2[, "solution_1"])
# plot importance scores
plot(fs2, main = "Ferrier scores")
# }
```