eval_asym_connectivity_summary | R Documentation |
Calculate the connectivity held within a solution to a conservation planning problem. This summary statistic evaluates the connectivity of a solution using pair-wise connectivity values between combinations of planning units. It is specifically designed for asymmetric connectivity data.
## S4 method for signature 'ConservationProblem,ANY,ANY,matrix'
eval_asym_connectivity_summary(x, solution, zones, data)
## S4 method for signature 'ConservationProblem,ANY,ANY,Matrix'
eval_asym_connectivity_summary(x, solution, zones, data)
## S4 method for signature 'ConservationProblem,ANY,ANY,data.frame'
eval_asym_connectivity_summary(x, solution, zones, data)
## S4 method for signature 'ConservationProblem,ANY,ANY,dgCMatrix'
eval_asym_connectivity_summary(x, solution, zones, data)
## S4 method for signature 'ConservationProblem,ANY,ANY,array'
eval_asym_connectivity_summary(x, solution, zones, data)
x |
|
solution |
|
zones |
|
data |
|
This summary statistic is comparable to the Connectivity
metric
reported by the
Marxan software (Ball et al. 2009).
It is calculated using the same equations used to penalize solutions
with asymmetric connectivity data
(i.e., add_asym_connectivity_penalties()
).
Specifically, it is calculated as the sum of the connectivity
values (in the argument to data
) that correspond pairs of planning
units, wherein one planning unit is selected by the solution
and the other planning unit is not selected by solution.
A tibble::tibble()
object describing the connectivity of the
solution.
It contains the following columns:
character
description of the summary statistic.
The statistic associated with the "overall"
value
in this column is calculated using the entire solution
(including all management zones if there are multiple zones).
If multiple management zones are present, then summary statistics
are also provided for each zone separately
(indicated using zone names).
numeric
connectivity value.
Greater values correspond to solutions associated with greater
connectivity.
Thus conservation planning exercises typically prefer solutions
with greater values.
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 terra::rast()
planning unitsThe argument to solution
be a terra::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 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 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
.
The argument to data
can be specified using several different formats.
data
as a matrix
/Matrix
objectwhere rows and columns represent
different planning units and the value of each cell represents the
strength of connectivity between two different planning units. Cells
that occur along the matrix diagonal are treated as weights which
indicate that planning units are more desirable in the solution.
The argument to zones
can be used to control
the strength of connectivity between planning units in different zones.
The default argument for zones
is to treat planning units
allocated to different zones as having zero connectivity.
data
as a data.frame
objectcontaining columns that are named
"id1"
, "id2"
, and "boundary"
. Here, each row
denotes the connectivity between a pair of planning units
(per values in the "id1"
and "id2"
columns) following the
Marxan format.
If the argument to x
contains multiple zones, then the
"zone1"
and "zone2"
columns can optionally be provided to manually
specify the connectivity values between planning units when they are
allocated to specific zones. If the "zone1"
and
"zone2"
columns are present, then the argument to zones
must be
NULL
.
data
as an array
objectcontaining four-dimensions where cell values
indicate the strength of connectivity between planning units
when they are assigned to specific management zones. The first two
dimensions (i.e., rows and columns) indicate the strength of
connectivity between different planning units and the second two
dimensions indicate the different management zones. Thus
the data[1, 2, 3, 4]
indicates the strength of
connectivity between planning unit 1 and planning unit 2 when planning
unit 1 is assigned to zone 3 and planning unit 2 is assigned to zone 4.
Ball IR, Possingham HP, and Watts M (2009) Marxan and relatives: Software for spatial conservation prioritisation in Spatial conservation prioritisation: Quantitative methods and computational tools. Eds Moilanen A, Wilson KA, and Possingham HP. Oxford University Press, Oxford, UK.
See summaries for an overview of all functions for summarizing solutions.
Also, see add_asym_connectivity_penalties()
to penalize solutions with low
asymmetric connectivity.
Other summaries:
eval_boundary_summary()
,
eval_connectivity_summary()
,
eval_cost_summary()
,
eval_feature_representation_summary()
,
eval_n_summary()
,
eval_target_coverage_summary()
## Not run:
# set seed for reproducibility
set.seed(500)
# load data
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
sim_zones_pu_polygons <- get_sim_zones_pu_polygons()
sim_zones_features <- get_sim_zones_features()
# build minimal conservation problem with polygon data
p1 <-
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 the problem
s1 <- solve(p1)
# print solution
print(s1)
# plot solution
plot(s1[, "solution_1"])
# simulate connectivity matrix
# here, we will generate connectivity values randomly
# between all pairs of planning units
acm1 <- matrix(
runif(nrow(sim_pu_polygons) ^ 2),
nrow = nrow(sim_pu_polygons)
)
# calculate connectivity associated with the solution
r1 <- eval_asym_connectivity_summary(p1, s1[, "solution_1"], data = acm1)
print(r1)
# build multi-zone conservation problem with polygon data
p2 <-
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 the problem
s2 <- solve(p2)
# print solution
print(s2)
# create new column representing the zone id that each planning unit
# was allocated to in the solution
s2$solution <- category_vector(
s2[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")]
)
s2$solution <- factor(s2$solution)
# plot solution
plot(s2[, "solution"])
# simulate asymmetric connectivity matrix
acm2 <- matrix(
runif(nrow(sim_zones_pu_polygons) ^ 2),
nrow = nrow(sim_zones_pu_polygons)
)
# calculate connectivity associated with the solution
r2 <- eval_asym_connectivity_summary(
p2,
s2[, c("solution_1_zone_1", "solution_1_zone_2", "solution_1_zone_3")],
data = acm2
)
print(r2)
## End(Not run)
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