spNetwork (version 0.4.3)

bw_tnkde_cv_likelihood_calc.mc: Bandwidth selection by likelihood cross validation for temporal NKDE (multicore)

Description

Calculate for multiple network and time bandwidths the cross validation likelihood to select an appropriate bandwidth in a data-driven approach with multicore support

Usage

bw_tnkde_cv_likelihood_calc.mc(
  bw_net_range,
  bw_net_step,
  bw_time_range,
  bw_time_step,
  lines,
  events,
  time_field,
  w,
  kernel_name,
  method,
  diggle_correction = FALSE,
  study_area = NULL,
  max_depth = 15,
  digits = 5,
  tol = 0.1,
  agg = NULL,
  sparse = TRUE,
  zero_strat = "min_double",
  grid_shape = c(1, 1),
  sub_sample = 1,
  verbose = TRUE,
  check = TRUE
)

Arguments

bw_net_range

The range for the network bandwidth to consider, given as a numeric vector of two values: c(bandwidth_min, bandwidth_max)

bw_net_step

The step between each network bandwidth to calculate, given as a float

bw_time_range

The range for the time bandwidth to consider, given as a numeric vector of two values: c(bandwidth_min, bandwidth_max)

bw_time_step

The step between each time bandwidth to calculate, given as a float

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.

time_field

The name of the field in events indicating when the events occurred. It must be a numeric field

w

A vector representing the weight of each event

kernel_name

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

method

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

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.

zero_strat

A string indicating what to do when density is 0 when calculating LOO density estimate for an isolated event. "min_double" (default) replace the 0 value by the minimum double possible on the machine. "remove" will remove them from the final score. The first approach penalizes more strongly the small bandwidths.

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.

sub_sample

A float between 0 and 1 indicating the percentage of quadra to keep in the calculus. For large datasets, it may be useful to limit the bandwidth evaluation and thus reduce calculation time.

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 matrix with the cross validation score. Each row corresponds to a network bandwidth and each column to a time bandwidth (the higher the better).

Details

See the function bws_tnkde_cv_likelihood_calc for more details. Note that the calculation is split according to the grid_shape argument. If the grid_shape is c(1,1) then only one process can be used.

Examples

# NOT RUN {
# loading the data
networkgpkg <- system.file("extdata", "networks.gpkg", package = "spNetwork", mustWork = TRUE)
eventsgpkg <- system.file("extdata", "events.gpkg", package = "spNetwork", mustWork = TRUE)
mtl_network <- sf::st_read(networkgpkg,layer="mtl_network")
bike_accidents <- sf::st_read(eventsgpkg,layer="bike_accidents")

# converting the Date field to a numeric field (counting days)
bike_accidents$Time <- as.POSIXct(bike_accidents$Date, format = "%Y/%m/%d")
bike_accidents$Time <- difftime(bike_accidents$Time, min(bike_accidents$Time), units = "days")
bike_accidents$Time <- as.numeric(bike_accidents$Time)
bike_accidents <- subset(bike_accidents, bike_accidents$Time>=89)

future::plan(future::multisession(workers=2))

# calculating the cross validation values
cv_scores <- bw_tnkde_cv_likelihood_calc.mc(
  bw_net_range = c(100,1000),
  bw_net_step = 100,
  bw_time_range = c(10,60),
  bw_time_step = 5,
  lines = mtl_network,
  events = bike_accidents,
  time_field = "Time",
  w = rep(1, nrow(bike_accidents)),
  kernel_name = "quartic",
  method = "discontinuous",
  diggle_correction = FALSE,
  study_area = NULL,
  max_depth = 10,
  digits = 2,
  tol = 0.1,
  agg = 15,
  sparse=TRUE,
  grid_shape=c(1,1),
  sub_sample=1,
  verbose = FALSE,
  check = TRUE)

## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)
# }