Use this function to calculate centrality measures. under one or several distance thresholds.
MK_RMCentrality( nodes, distance = list(type = "centroid"), distance_thresholds = NULL, binary = TRUE, probability = NULL, rasterparallel = FALSE, write = NULL, intern = TRUE )
| nodes | Object of class sf, SpatialPolygonsDataFrame or raster. It must be in a projected coordinate system. If nodes is a raster layer then raster values (Integer) will be taken as "id". |
|---|---|
| distance | list. Distance parameters. For example: type, resistance,or keep. For "type" choose one of the distances: "centroid" (faster), "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. |
| distance_thresholds | numeric. Distance or distances thresholds to establish connections (in meters). 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). |
| binary | logical. Binary metrics, it only considers the distance thresholds to establish if a pair of nodes is (1) or not connected (0). Probability argument is not necessary. |
| 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 "PC" metric. If probability = NULL, then it will be the inverse of the mean dispersal distance for the species (1/α; Hanski and Ovaskainen 2000). |
| rasterparallel | logical. If nodes is "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. It is necessary to specify the "Folder direction" + "Initial prefix", for example, "C:/ejemplo". |
| intern | logical. Show the progress of the process, default = TRUE. Sometimes the advance process does not reach 100 percent when operations are carried out very quickly. |
This function implements Patch-Scale Connectivity or Centrality Measures. Radial measures: degree, strength (using probability argument, for weighted graphs), eigenvector centrality (eigen), and closeness centrality (close). Medial measures: betweenness centrality (BWC), node memberships (cluster), and modularity (modules, using probability argument). The function builds on functions out of Csardi’s ’igraph’ package.
Borgatti, S. P., & Everett, M. G. (2006). A Graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484. https://doi.org/10.1016/j.socnet.2005.11.005 Hanski, I. and Ovaskainen, O. 2000. The metapopulation capacity of a fragmented landscape. Nature 404: 755–758.
if (FALSE) { library(Makurhini) library(sf) data("vegetation_patches", package = "Makurhini") nrow(vegetation_patches) # Number of patches #Two distance threshold, centrality_test <- MK_RMCentrality(nodes = vegetation_patches, distance = list(type = "centroid"), distance_thresholds = c(10000, 100000), probability = 0.05, write = NULL) centrality_test plot(centrality_test$d10000["degree"], breaks = "jenks") plot(centrality_test$d10000["BWC"], breaks = "jenks") plot(centrality_test$d10000["cluster"], breaks = "jenks") plot(centrality_test$d10000["modules"], breaks = "jenks") }