spNetwork (version 0.2.1)

nkde.mc: Network Kernel density estimate (multicore)

Description

Calculate the Network Kernel Density Estimate based on a network of lines, sampling points, and events with multicore support.

Usage

nkde.mc(
  lines,
  events,
  w,
  samples,
  kernel_name,
  bw,
  adaptive = FALSE,
  trim_bw = NULL,
  method,
  div = "bw",
  diggle_correction = FALSE,
  study_area = NULL,
  max_depth = 15,
  digits = 5,
  tol = 0.1,
  agg = NULL,
  sparse = TRUE,
  grid_shape = c(1, 1),
  verbose = TRUE,
  check = TRUE
)

Arguments

lines

A SpatialLinesDataFrame representing the underlying network. The geometries must be a SpatialLinesDataFrame (may crash if some geometries are invalid) without MultiLineSring.

events

events A SpatialPointsDataFrame representing the events on the network. The points will be snapped on the network to their closest line.

w

A vector representing the weight of each event

samples

A SpatialPointsDataFrame representing the locations for which the densities will be estimated.

kernel_name

The name of the kernel to use. Must be one of triangle, gaussian, tricube, cosine ,triweight, quartic, epanechnikov or uniform.

bw

The kernel bandwidth (using the scale of the lines)

adaptive

A Boolean, indicating if an adaptive bandwidth must be used

trim_bw

A float, indicating the maximum value for the adaptive bandwidth

method

The method to use when calculating the NKDE, must be one of simple / discontinuous / continuous (see nkde details for more information)

div

The divisor to use for the kernel. Must be "n" (the number of events within the radius around each sampling point), "bw" (the bandwith) "none" (the simple sum).

diggle_correction

A Boolean indicating if the correction factor for edge effect must be used.

study_area

A SpatialPolygonsDataFrame or a SpatialPolygon representing the limits of the study area.

max_depth

when using the continuous and discontinuous methods, the calculation time and memory use can go wild if the network has many small edges (area with many of intersections and many events). To avoid it, it is possible to set here a maximum depth. Considering that the kernel is divided at intersections, a value of 10 should yield good estimates in most cases. A larger value can be used without a problem for the discontinuous method. For the continuous method, a larger value will strongly impact calculation speed.

digits

The number of digits to retain from the spatial coordinates. It ensures that topology is good when building the network. Default is 3. Too high a precision (high number of digits) might break some connections

tol

A float indicating the minimum distance between the events and the lines' extremities when adding the point to the network. When points are closer, they are added at the extremity of the lines.

agg

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

sparse

A Boolean indicating if sparse or regular matrices should be used by the Rcpp functions. These matrices are used to store edge indices between two nodes in a graph. Regular matrices are faster, but require more memory, in particular with multiprocessing. Sparse matrices are slower (a bit), but require much less memory.

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.

verbose

A Boolean, indicating if the function should print messages about the process.

check

A Boolean indicating if the geometry checks must be run before the operation. This might take some times, but it will ensure that the CRS of the provided objects are valid and identical, and that geometries are valid.

Value

A vector of values, they are the density estimates at sampling points

Details

For more details, see help(nkde)

Examples

# NOT RUN {
networkgpkg <- system.file("extdata", "networks.gpkg", package = "spNetwork", mustWork = TRUE)
eventsgpkg <- system.file("extdata", "events.gpkg", package = "spNetwork", mustWork = TRUE)
mtl_network <- rgdal::readOGR(networkgpkg,layer="mtl_network", verbose=FALSE)
bike_accidents <- rgdal::readOGR(eventsgpkg,layer="bike_accidents", verbose=FALSE)
future::plan(future::multisession(workers=2))
lixels <- lixelize_lines(mtl_network,200,mindist = 50)
samples <- lines_center(lixels)
densities <- nkde.mc(mtl_network,
                  events = bike_accidents,
                  w = rep(1,nrow(bike_accidents)),
                  samples = samples,
                  kernel_name = "quartic",
                  bw = 300, div= "bw",
                  adaptive = FALSE, agg = 15,
                  method = "discontinuous", digits = 1, tol = 1,
                  grid_shape = c(3,3),
                  verbose=FALSE)
## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)
# }