spNetwork (version 0.2.1)

network_listw.mc: Network distance listw (multicore)

Description

Generate listw object (spdep like) based on network distances with multicore support.

Usage

network_listw.mc(
  origins,
  lines,
  maxdistance,
  method = "centroid",
  point_dist = NULL,
  snap_dist = Inf,
  line_weight = "length",
  mindist = 10,
  direction = NULL,
  dist_func = "inverse",
  matrice_type = "B",
  grid_shape = c(1, 1),
  verbose = FALSE,
  digits = 3,
  tol = 0.1
)

Arguments

origins

A SpatialLinesDataFrame, SpatialPointsDataFrame or SpatialPolygonsDataFrame for which the spatial neighbouring list will be built

lines

A SpatialLinesDataFrame representing the network

maxdistance

The maximum distance between two observations to consider them as neighbours.

method

A string indicating how the starting points will be built. If 'centroid' is used, then the centre of lines or polygons is used. If 'pointsalong' is used, then points will be placed along polygons' borders or along lines as starting and end points. If 'ends' is used (only for lines) the first and last vertices of lines are used as starting and ending points.

point_dist

A float, defining the distance between points when the method pointsalong is selected.

snap_dist

the maximum distance to snap the start and end points on the network.

line_weight

The weights to use for lines. Default is "length" (the geographical length), but can be the name of a column. The value is considered proportional with the geographical length of the lines.

mindist

The minimum distance between two different observations. It is important for it to be different from 0 when a W style is used.

direction

Indicates a field giving information about authorized travelling direction on lines. if NULL, then all lines can be used in both directions. Must be the name of a column otherwise. The values of the column must be "FT" (From - To), "TF" (To - From) or "Both".

dist_func

Indicates the function to use to convert the distance between observation in spatial weights. Can be 'identity', 'inverse', 'squared inverse' or a function with one parameter x that will be vectorized internally

matrice_type

The type of the weighting scheme. Can be 'B' for Binary, 'W' for row weighted, see the documentation of spdep::nb2listw for details

grid_shape

A vector of length 2 indicating the shape of the grid to use for splitting the dataset. Default is c(1,1), so all the calculation is done in one go. It might be necessary to split it if the dataset is large.

verbose

A Boolean indicating if the function should print its progress

digits

The number of digits to retain in the spatial coordinates ( simplification used to reduce risk of topological error)

tol

A float indicating the spatial tolerance when points are added as vertices to lines.

Value

A listw object (spdep like)

Examples

# NOT RUN {
networkgpkg <- system.file("extdata", "networks.gpkg", package = "spNetwork", mustWork = TRUE)
mtl_network <- rgdal::readOGR(networkgpkg,layer="mtl_network", verbose=FALSE)
future::plan(future::multisession(workers=2))
listw <- network_listw.mc(mtl_network,mtl_network,maxdistance=500,
        method = "centroid", line_weight = "length",
        dist_func = 'squared inverse', matrice_type='B', grid_shape = c(2,2))
## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)
# }