Betweenness Centrality metrics using Conefor command line

Use this function to calculate the BC, BCIIC and BCPC indexes under one or several distance thresholds.

MK_BCentrality(
  nodes,
  id,
  attribute = NULL,
  area_unit = "ha",
  distance = list(type = "centroid", resistance = NULL),
  metric = c("BC", "BCIIC", "BCPC"),
  distance_thresholds = NULL,
  probability = NULL,
  LA = NULL,
  coneforpath = NULL,
  dA = FALSE,
  dvars = FALSE,
  parallel = FALSE,
  rasterparallel = FALSE,
  write = NULL
)

Arguments

nodes	Object of class sf, SpatialPolygonsDataFrame or raster. The shapefile must be in a projected coordinate system.
id	character. If nodes is a shapefile then you can specify the column name with the node ID. If NULL, then a new id will be generated.
attribute	character or vector. If nodes is a shappefile then you must specify the column name with the attribute in the data table selected for the nodes. If nodes is a raster layer then it must be a numeric vector with the node's attribute. The length of the vector must be equal to the number of nodes. The numeric vector is multiplied by the area of each node to obtain a weighted habitat index. If NULL the node area will be used as a node attribute, the unit area can be selected using the "area_unit" argument.
area_unit	character. If attribute is NULL you can set an area unit, ?unit_covert compatible unit ("m2", "Dam2, "km2", "ha", "inch2", "foot2", "yard2", "mile2"). Default equal to "m2".
distance	list. Distance parameters. For example: type, resistance,or keep. For "type" choose one of the distances: "centroid" (faster), "edge", "hausdorff-edge", "least-cost" or "commute-time". If the type is equal to "least-cost" or "commute-time", then you have to use the "resistance" argument. To see more arguments see the ?distancefile.
metric	character. Choose a Betweenness Centrality Metric: "BC" or "BCIIC" considering topologycal distances or "BCPC" considering maximum product probabilities.
distance_thresholds	numeric. Distance or distances thresholds (meters) to establish connections. For example, one distance: distance_threshold = 30000; two or more specific distances: distance_thresholds = c(30000, 50000); sequence distances: distance_thresholds = seq(10000,100000, 10000).
probability	numeric. Connection probability to the selected distance threshold, e.g., 0.5 that is 50 percentage of probability connection. Use in case of selecting the "BPC" metric.
LA	numeric. Maximum landscape attribute (attribute unit, if attribute is NULL then unit is equal to ha).
coneforpath	character. Path to Conefor 2.6 with command line interface (http://www.conefor.org/coneforsensinode.html). Example, "C:/Users/coneforWin64.exe".
dA	logical. If TRUE, then the delta attribute will be added to the node's importance result.
dvars	logical. If TRUE, then the absolute variation will be added to the node's importance result.
parallel	numeric. Specify the number of cores to use for parallel processing, default = NULL. Parallelize the function using furrr package and multiprocess plan.
rasterparallel	logical. If parallel is FALSE and nodes is a raster then you can use this argument to assign the metrics values to the nodes raster. It is useful when raster resolution is less than 100 m2.
write	character. Write output shapefile, example, "C:/ejemplo.shp".

References

Saura, S. and Torne, J. (2012). Conefor 2.6. Universidad Politecnica de Madrid. Available at www.conefor.org.
Freeman L.C. (1977). Set of Measures of Centrality Based on Betweenness. Sociometry 40: 35-41.
Bodin, O. and Saura, S. (2010). Ranking individual habitat patches as connectivity providers: integrating network analysis and patch removal experiments. Ecological Modelling 221: 2393-2405.

Examples

if (FALSE) {
library(Makurhini)
data("vegetation_patches", package = "Makurhini")
nrow(vegetation_patches) #Number of patches

#Two distance thresholds
BCIIC <- MK_BCentrality(nodes = vegetation_patches, id = "id",
            coneforpath = "C:/Users/coneforWin64.exe",
            distance = list(type = "centroid"),
            metric = "BCIIC", LA = NULL,
            distance_thresholds = c(10000, 30000)) #10 and 30 km

#Using raster
data("raster_vegetation_patches", package = "Makurhini")
BCPC <- MK_BCentrality(nodes = raster_vegetation_patches,
           coneforpath = "C:/Users/coneforWin64.exe",
           attribute = NULL,
           distance = list(type = "centroid"),
           metric = "BCPC", probability = 0.5,
           LA = NULL,
           distance_thresholds = 40000) #40 km

BCPC_parallel <- MK_BCentrality(nodes = raster_vegetation_patches,
                    coneforpath = "C:/Users/coneforWin64.exe",
                    id = "id", attribute = NULL,
                    distance = list(type = "centroid"),
                    metric = "BCPC", LA = NULL, probability = 0.5,
                    distance_thresholds = c(40000, 60000),
                    parallel = 4) #40 and 60 km
}