Description Usage Arguments Details Value Author(s) References
move.forud
function that creates step utilization distribution contours based on the brownian
.bridge.dyn
from package move and exports the created contour lines as a SpatialPolygonsDataFrame
to a #'set of shapefiles for further whatever in ArcMap (or GIS program of choice).
1 | move.forud(x, range.subset, ts, ras, le, lev, crs, path, name, ID)
|
x, |
dbbmm object created using |
range.subset, |
range of model step segments that the user wants to create the UD contours for. Has to be greater than the margin based on the dbbmm object as estimates of variance for the first few steps are not estimable depending on how big of a margin is specified. Easiest way to define #' this range is to use the row number from the input file. |
ts, |
time step for integration of |
ras, |
raster background size for |
le, |
location error value for |
lev, |
level of the UD contour as a vector, c(50, 95) that the user is interested in. Will work with #' multiple values (e.g., c(50, 95)) and will label those values in the resultant shapefile. Note that you don't have to put these values in as percentages (e.g., 0.50 for #' 50 percent). |
crs, |
coordinate reference system for identifying where the polygons are located. Uses standard |
path, |
file path (e.g., "C:/") using standard R path nomenclature specifying the location that the #'output shapefiles are to be written. Note that the call to |
name, |
file name, in quotation's, specifying the output files name which will be written to the #' |
ID, |
Unique identifier for each individual that is added to each created shapefile for linking to other #'data structures |
This function is a different approach than move.contour
(which will be deprecated in 2016)
that creates the same output (shapefiles of utilization distribution polygons) based on the dbbmm object.
The primary difference between move.contour
and move.forud
is that move.forud
uses a for
loop to loop through the range of line segments for which it creates the UD's and then
exporting the output as shapefiles one at time to the user-defined path. The time it takes to do each method
seems to be the same as the slowness of the process is due to the calculations being done by
brownian.bridge.dyn()
of the UD over the raster extent. move.forud
does not use all the memory in the system #'as did move.contour
which saved the objects for one batch export of the shapefiles. move.forud
#'is not particularly any more efficient than move.contour
, but if you have a large raster extent and #'many individual time step utilization distributions that are being estimated, move.forud
does not stack all the raster files in memory, causing issues.
I thought that vectorisation would speed this up, but the vast majority of the time is spend in the call to #'brownian.bridge.dyn()
and not the rest of the for loop. So, I am working on some ways to reduce the #'background raster on which the contour estimates are created as there are alot of them that are way outside #'the range of useful at the individual time-step level, but when you look at the entire period which brow#'nian.bridge.dyn
does, are actually useful. Think of it as pulling one hour worth of movements out of a year #'worth of GPS locations, it is likely that the 50m in that hour is a small segment of the overall range and #'hence background raster size.
As an aside, I do know that it is better to access values in slots using slot(object, 'name') versus the '@' #'sign, but, the routine I used to pull the requisite values from the DDBMvar object would not work as the data #'necessary does not have slot values assigned to it at this time.
Output is a set of shapefiles, written to the specified path
Bret A. Collier <bret@lsu.edu>
Kranstauber, D., R. Kays, S. D. Lapoint, M. Wikelski, and K. Safi. 2012. A dynamic Brownian #bridge movement model to estimate utlization distributions for heterogenous animal movement. Journal of Animal Ecology 81: 738-746. DOI: 10 .1111/j.1365-2656.2012.01955.x.
Byrne, M. E., J. C. McCoy, J. Hinton, M. J. Chamberlain, and B. A. Collier. 2014. Using dynamic #'brownian bridge movement modeling to measure temporal patterns of habitat selection. Journal of Animal Ecology, 83: 1234-1243. DOI: 10.1111/1365-2656.12205
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