eval_rare_richness_importance {prioritizr} | R Documentation |
Evaluate solution importance using rarity weighted richness scores
Description
Calculate importance scores for planning units selected in a solution using rarity weighted richness scores (based on Williams et al. 1996). This method is only recommended for large-scaled conservation planning exercises (i.e., more than 100,000 planning units) where importance scores cannot be calculated using other methods in a feasible period of time. This is because rarity weighted richness scores cannot (i) account for the cost of different planning units, (ii) account for multiple management zones, and (iii) identify truly irreplaceable planning units — unlike the replacement cost metric which does not suffer any of these limitations.
Usage
eval_rare_richness_importance(x, solution, ...)
## S4 method for signature 'ConservationProblem,numeric'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,matrix'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,data.frame'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,Spatial'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,sf'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,Raster'
eval_rare_richness_importance(x, solution, rescale, ...)
## S4 method for signature 'ConservationProblem,SpatRaster'
eval_rare_richness_importance(x, solution, rescale, ...)
Arguments
x |
|
solution |
|
... |
not used. |
rescale |
|
Details
Rarity weighted richness scores are calculated using the following
terms. Let I
denote the set of planning units (indexed by
i
), let J
denote the set of conservation features (indexed by
j
), let r_{ij}
denote the amount of feature j
associated with planning unit i
, and let m_j
denote the
maximum value of feature j
in r_{ij}
in all planning units
i \in I
. To calculate the rarity weighted richness (RWR) for
planning unit k
:
\mathit{RWR}_{k} = \sum_{j}^{J} \frac{ \frac{r_{ik}}{m_j} }{
\sum_{i}^{I}r_{ij}}
Value
A numeric
, matrix
, data.frame
,
terra::rast()
, or sf::sf()
object
containing the importance scores for each planning
unit in the solution. Specifically, the returned object is in the
same format as the planning unit data in the argument to x
.
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
hasnumeric
planning unitsThe argument to
solution
must be anumeric
vector with each element corresponding to a different planning unit. It should have the same number of planning units as those in the argument tox
. Additionally, any planning units missing cost (NA
) values should also have missing (NA
) values in the argument tosolution
.x
hasmatrix
planning unitsThe argument to
solution
must be amatrix
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 tox
. Additionally, any planning units missing cost (NA
) values for a particular zone should also have a missing (NA
) values in the argument tosolution
.x
hasterra::rast()
planning unitsThe argument to
solution
be aterra::rast()
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 tox
. Additionally, any planning units missing cost (NA
) values for a particular zone should also have missing (NA
) values in the argument tosolution
.x
hasdata.frame
planning unitsThe argument to
solution
must be adata.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 adata.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 withsf::sf()
data). Additionally, any planning units missing cost (NA
) values for a particular zone should also have missing (NA
) values in the argument tosolution
.x
hassf::sf()
planning unitsThe argument to
solution
must be asf::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 thesf::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 tosolution
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 tosolution
.
References
Williams P, Gibbons D, Margules C, Rebelo A, Humphries C, and Pressey RL (1996) A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds. Conservation Biology, 10: 155–174.
See Also
See importance for an overview of all functions for evaluating the importance of planning units selected in a solution.
Other importances:
eval_ferrier_importance()
,
eval_replacement_importance()
Examples
## Not run:
# seed seed for reproducibility
set.seed(600)
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
# create minimal problem with raster planning units
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)
# solve problem
s1 <- solve(p1)
# print solution
print(s1)
# plot solution
plot(s1, main = "solution", axes = FALSE)
# calculate importance scores
rwr1 <- eval_rare_richness_importance(p1, s1)
# print importance scores
print(rwr1)
# plot importance scores
plot(rwr1, main = "rarity weighted richness", axes = FALSE)
# create minimal problem with polygon planning units
p2 <-
problem(sim_pu_polygons, 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)
# plot solution
plot(s2[, "solution_1"], main = "solution")
# calculate importance scores
rwr2 <- eval_rare_richness_importance(p2, s2[, "solution_1"])
# plot importance scores
plot(rwr2, main = "rarity weighted richness")
## End(Not run)