spNetwork (version 0.1.1)

cross_kfunctions.mc: Network cross k and g functions (multicore, experimental)

Description

Calculate the cross k and g functions for a set of points on a network with multicore support. (experimental)

Usage

cross_kfunctions.mc(
  lines,
  pointsA,
  pointsB,
  start,
  end,
  step,
  width,
  nsim,
  conf_int = 0.05,
  digits = 2,
  tol = 0.1,
  resolution = NULL,
  agg = NULL,
  verbose = TRUE
)

Arguments

lines

A SpatialLinesDataFrame with the sampling points. The geometries must be a SpatialLinesDataFrame (may crash if some geometries are invalid)

pointsA

A SpatialPointsDataFrame representing the points to which the distances are calculated.

pointsB

A SpatialPointsDataFrame representing the points from which the distances are calculated.

start

A double, the start value for evaluating the k and g functions

end

A double, the last value for evaluating the k and g functions

step

A double, the jump between two evaluations of the k and g function

width

The width of each donut for the g-function

nsim

An integer indicating the number of Monte Carlo simulations required

conf_int

A double indicating the width confidence interval (default = 0.05)

digits

An integer indicating the number of digits to retain for the spatial coordinates

tol

When adding the points to the network, specify the minimum distance between these points and the lines' extremities. When points are closer, they are added at the extremity of the lines.

resolution

When simulating random points on the network, selecting a resolution will reduce greatly the calculation time. When resolution is null the random points can occur everywhere on the graph. If a value is specified, the edges are split according to this value and the random points are selected vertices on the new network.

agg

A double indicating if the events must be aggregated within a distance. If NULL, the events are aggregated by rounding the coordinates.

verbose

A Boolean indicating if progress messages should be displayed

Value

A list with the following values :

• plotkA ggplot2 object representing the values of the cross k-function

• plotgA ggplot2 object representing the values of the cross g-function

• valuesA DataFrame with the values used to build the plots

Examples

# NOT RUN {
networkgpkg <- system.file("extdata", "networks.gpkg", package = "spNetwork", mustWork = TRUE)
eventsgpkg <- system.file("extdata", "events.gpkg", package = "spNetwork", mustWork = TRUE)
main_network_mtl <- rgdal::readOGR(networkgpkg,layer="main_network_mtl", verbose=FALSE)
mtl_libraries <- rgdal::readOGR(eventsgpkg,layer="mtl_libraries", verbose=FALSE)
mtl_theatres <- rgdal::readOGR(eventsgpkg,layer="mtl_theatres", verbose=FALSE)
future::plan(future::multisession(workers=2))
result <- cross_kfunctions.mc(main_network_mtl, mtl_libraries, mtl_theatres,
                           start = 0, end = 2500, step = 10, width = 250,
                           nsim = 50, conf_int = 0.05, digits = 2,
                           tol = 0.1, agg = NULL, verbose = TRUE)
## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)
# }