Network cross k and g functions (multicore, maturing) — cross

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

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,
  return_sims = FALSE
)

Arguments

lines: A feature collection of linestrings representing the underlying network. The geometries must be simple Linestrings (may crash if some geometries are invalid) without MultiLineSring
pointsA: A feature collection of points representing the points to which the distances are calculated.
pointsB: A feature collection of points representing the points from which the distances are calculated.
start: A double, the lowest distance used to evaluate the k and g functions
end: A double, the highest distance used to evaluate the k and g functions
step: A double, the step between two evaluations of the k and g function. start, end and step are used to create a vector of distances with the function seq
width: The width of each donut for the g-function. Half of the width is applied on both sides of the considered distance
nsim: An integer indicating the number of Monte Carlo simulations to perform for inference
conf_int: A double indicating the width confidence interval (default = 0.05) calculated on the Monte Carlo simulations
digits: An integer indicating the number of digits to retain from 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 can only be vertices on the new network
agg: A double indicating if the events must be aggregated within a distance. If NULL, the events are aggregated only by rounding the coordinates
verbose: A Boolean indicating if progress messages should be displayed
return_sims: a boolean indicating if the simulated k and g values must also be returned as matrices

Value

A list with the following values :

plotk: A ggplot2 object representing the values of the cross k-function
plotg: A ggplot2 object representing the values of the cross g-function
values: A DataFrame with the values used to build the plots

Examples

# \donttest{
data(main_network_mtl)
data(mtl_libraries)
data(mtl_theatres)
future::plan(future::multisession(workers=1))
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)
# }