Create a filter of a track for "bad" points implying a speed of motion that is unrealistic.
speedfilter(x, max.speed = NULL, test = FALSE)trip object
speed in kilometres per hour
cut the algorithm short and just return first pass
Logical vector matching positions in the coordinate records that pass the filter.
This algorithm is destructive, and provides little information about location uncertainty. It is provided because it's commonly used and provides an illustrative benchmark for further work.
It is possible for the filter to become stuck in an infinite loop, depending on the function passed to the filter. Several minutes is probably too long for hundreds of points, test on smaller sections if unsure.
Using an algorithm (McConnnell et al., 1992), points are tested for speed between previous / next and 2nd previous / next points. Contiguous sections with an root mean square speed above a given maximum have their highest rms point removed, then rms is recalculated, until all points are below the maximum. By default an (internal) root mean square function is used, this can be specified by the user.
If the coordinates of the trip data are not projected, or NA the
distance calculation assumes longlat and kilometres (great circle). For
projected coordinates the speed must match the units of the coordinate
system. (The PROJ.4 argument "units=km" is suggested).
The algorithm comes from McConnell, B. J. and Chambers, C. and Fedak, M. A. (1992) Foraging ecology of southern elephant seals in relation to the bathymetry and productivity of the southern ocean. Antarctic Science 4 393-398