Move-class) to calculate the utilization distribution, UD, of the given track. It uses the dynamic Brownian Bridge Movement Model (dBBMM) to do so. The dBBMM has the advantage over the other Brownian Bridge Movement Model that changes in behavior are accounted for. It does so by using the behavioral change point analysis in a sliding window. For details see references.brownian.bridge.dyn(object, raster, dimSize, location.error, margin=11, window.size=31, ext=.3, bbox=NA,...)raster is interpreted as the resolution of the square raster cells (in map units); the according raster will be calculated internally. If a RasterLayer is provided the brownian.bridge.dyn starts to calculate the UD based on that raster.raster is not set. dimSize is interpreted as the number of cells along the largest dimension of the track. The according raster will be calculated internally. Default is 10verbose=FALSE can be used to suppress printing messages about the computational sizeThe function prints an estimate of the size of the computational task ahead. This can give an indication of how long the computation is going to take. It should scale roughly linearly with the duration of the computations. In our experience 10e9 takes about a minute with an average laptop.
There is one further argument that can be given: time.step. It correspond to the size of the timer intervals taken for every integration step (in minutes). If left NULL 15 steps are taken in the shortest time interval.
## create a move object
data(leroy)
## change projection method to aeqd and center the coordinate system to the track
data2 <- spTransform(leroy[30:90,], CRSobj="+proj=aeqd +ellps=WGS84", center=TRUE)
## create a DBBMM object
dbbmm <- brownian.bridge.dyn(object=data2, location.error=12, dimSize=155, ext=.45,
time.step=19.1, margin=15)
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