eval_asym_connectivity_summary: Evaluate asymmetric connectivity of solution

In prioritizr: Systematic Conservation Prioritization in R

Evaluate asymmetric connectivity of solution

Description

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.

Usage

## S4 method for signature 'GenericConservationProblem,ANY,ANY,matrix'
eval_asym_connectivity_summary(x, solution, zones, data)

## S4 method for signature 'GenericConservationProblem,ANY,ANY,Matrix'
eval_asym_connectivity_summary(x, solution, zones, data)

## S4 method for signature 'GenericConservationProblem,ANY,ANY,data.frame'
eval_asym_connectivity_summary(x, solution, zones, data)

## S4 method for signature 'GenericConservationProblem,ANY,ANY,dgCMatrix'
eval_asym_connectivity_summary(x, solution, zones, data)

## S4 method for signature 'GenericConservationProblem,ANY,ANY,array'
eval_asym_connectivity_summary(x, solution, zones, data)

Arguments

x	problem() object.
solution	numeric, matrix, data.frame, terra::rast(), or sf::sf() object. Note that solution must have the same format as the planning unit data in x. See the Solution format section for more information.
zones	matrix or Matrix object describing the level of connectivity between different zones. Each row and column corresponds to a different zone in x, and cell values indicate the level of connectivity between each combination of zones. Cell values along the diagonal of the matrix represent the level of connectivity between planning units allocated to the same zone. Cell values must range between 1 and -1, where negative values favor solutions with weak connectivity. Defaults to an identity matrix (i.e., a matrix with ones along the matrix diagonal and zeros elsewhere), so that planning units are only considered to be connected when they are allocated to the same zone. Note that zones is only required when working with multiple zones and data is a matrix or Matrix object. If data is an array or data.frame with data for multiple zones (e.g., using the "zone1" and "zone2" column names), then zones must be NULL.
data	matrix, Matrix, data.frame, or array object containing connectivity data. The connectivity values correspond to the strength of connectivity between different planning units. Thus connections between planning units that are associated with higher values are more favorable in the solution. See the Data format section for more information.

Details

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 (per 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.

Value

A tibble::tibble() object describing the connectivity of the solution. It contains the following columns.

summary

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 x has multiple zones). If x has multiple management zones, then summary statistics are also provided for each zone separately (indicated using zone names).

asym_connectivity

numeric connectivity value. Greater values correspond to solutions associated with greater connectivity. Thus conservation planning exercises typically prefer solutions with greater values.

Solution format

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

x has numeric planning units

Here 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 x. Additionally, any planning units with missing cost (NA) values should also have missing (NA) values in the solution.

x has matrix planning units

Here 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 x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have a missing (NA) values in solution.

x has terra::rast() planning units

Here solution be a terra::rast() object where different cells 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 x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

x has data.frame planning units

Here 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 with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

x has sf::sf() planning units

Here 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, solution must also have the same coordinate reference system as the planning unit data. Furthermore, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

Data format

The following formats can be used to specify data.

data as a matrix/Matrix object

Here rows and columns correspond to different planning units and cell values denote the strength of connectivity between two planning units. Cells that occur along the matrix diagonal are treated as weights which indicate that planning units are more desirable in the solution. With this format, zones can be used to control the strength of connectivity between planning units in different zones. Note that the default for zones is to treat planning units allocated to different zones as having zero connectivity.

data as a data.frame object

Here rows correspond to a pair of planning units and columns provide information about each pair of planning units. In particular, data must have the columns: "id1", "id2", and "boundary". The "id1" and "id2" columns contain identifiers (indices) for a pair of planning units, and the "boundary" column contains the strength of connectivity between them (following the Marxan format). If x has 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 particular zones. Note that if the "zone1" and "zone2" columns are present, then zones must be NULL.

data as an array object

Here a four-dimension array is used to specify connectivity data, 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.

References

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 Also

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 functions for summarizing solutions: eval_boundary_summary(), eval_connectivity_summary(), eval_cost_summary(), eval_feature_representation_summary(), eval_n_summary(), eval_objective_summary(), eval_target_coverage_summary()

Examples

# 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)

prioritizr documentation built on Sept. 24, 2026, 5:07 p.m.