nkde.mc: Network Kernel density estimate (multicore)

In spNetwork: Spatial Analysis on Network

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 feature collection of linestrings representing the underlying network. The geometries must be simple Linestrings (may crash if some geometries are invalid) without MultiLineSring.
events	events A feature collection of points 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 feature collection of points 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), can be a single float or a numeric vector if a different bandwidth must be used for each event.
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 bandwidth) "none" (the simple sum).
diggle_correction	A Boolean indicating if the correction factor for edge effect must be used.
study_area	A feature collection of polygons 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.

Details

For more details, see help(nkde)

Value

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

Examples

data(mtl_network)
data(bike_accidents)
future::plan(future::multisession(workers=1))
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=TRUE)
## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)

spNetwork documentation built on June 22, 2024, 9:40 a.m.