spNetwork (version 0.4.4.6)

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

Description

Calculate the cross k and g functions for a set of points on a network. For more details, see the document of the function cross_kfunctions.

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,
  return_sims = FALSE,
  calc_g_func = TRUE,
  grid_shape = c(1, 1)
)

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

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.

calc_g_func

A Boolean indicating if the G function must also be calculated (TRUE by default). If FALSE, then only the K function is calculated

grid_shape

A vector of two values indicating how the study area must be split when performing the calculus. Default is c(1,1) (no split). A finer grid could reduce memory usage and increase speed when a large dataset is used. When using multiprocessing, the work in each grid is dispatched between the workers.

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_theatres, mtl_libraries,
                           start = 0, end = 2500, step = 10, width = 250,
                           nsim = 50, conf_int = 0.05, digits = 2,
                           tol = 0.1, agg = NULL, verbose = FALSE)
# }