The worker function for tnkde and tnkde.mc
tnkde_worker(
lines,
events_loc,
events,
samples_loc,
samples_time,
kernel_name,
bw_net,
bw_time,
bws_net,
bws_time,
method,
div,
digits,
tol,
sparse,
max_depth,
verbose = FALSE
)A numeric matrix with the nkde values
A feature collection of linestrings with the sampling points. The geometries must be simple Linestrings (may crash if some geometries are invalid)
A feature collection of points representing the aggergated events on the network. The points will be snapped on the network.
A feature collection of points representing the base events on the network
A feature collection of points representing the locations for which the densities will be estimated.
A numeric vector representing when each density will be estimated
The name of the kernel to use
The global network kernel bandwidth
The global time kernel bandwidth
The network kernel bandwidth (in meters) for each event
The time bandwidth for each event
The method to use when calculating the NKDE, must be one of simple / discontinuous / continuous (see details for more information)
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).
The number of digits to keep in the spatial coordinates. It ensures that topology is good when building the network. Default is 3
When adding the events and the sampling points to the network, the minimum distance between these points and the lines extremities. When points are closer, they are added at the extremity of the lines.
A Boolean indicating if sparse or regular matrices should be used by the Rcpp functions. Regular matrices are faster, but require more memory and could lead to error, in particular with multiprocessing. Sparse matrices are slower, but require much less memory.
When using the continuous and discontinuous methods, the calculation time and memory use can go wild if the network has a lot of small edges (area with a lot of intersections and a lot of events). To avoid it, it is possible to set here a maximum depth. Considering that the kernel is divided at intersections, a value of 8 should yield good estimates. A larger value can be used without problem for the discontinuous method. For the continuous method, a larger value will strongly impact calculation speed.
A Boolean, indicating if the function should print messages about the process.
#This is an internal function, no example provided
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