View source: R/border_correction_sf.R
correction_factor | R Documentation |
Function to calculate the border correction factor.
correction_factor(
study_area,
events,
lines,
method,
bws,
kernel_name,
tol,
digits,
max_depth,
sparse
)
study_area |
A feature collection of polygons or a polygon, the limit of the study area. |
events |
A feature collection of points representing the events on the network. |
lines |
The lines used to create the network |
method |
The method to use when calculating the NKDE, must be one of simple / discontinuous / continuous (see details for more information) |
bws |
The kernel bandwidth (in meters) for each event |
kernel_name |
The name of the kernel to use |
tol |
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. |
digits |
The number of digits to keep in the spatial coordinates. It ensures that topology is good when building the network. Default is 3 |
max_depth |
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. |
sparse |
A boolean indicating if sparse or regular matrix 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. |
A numeric vector with the correction factor values for each event
#no example provided, this is an internal function
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