Region labeling of samples based on their spatial connectivity.
labelSample(xy = xy, rad = rad, npt = NULL, npx = NULL, pxr = r)Object of class SpatialPoints of SpatialPointsDataFrame.
Minimum radius. Unit depends on the projection of the data.
Minimum pixel count per pixel.
Minimum number of pixels.
Pixel resolution os a valid raster layer.
A vector.
First, the samples are converted to pixel coordinates and then one of two occur: 1) if npt is set, the function removes pixels with a pixel count smaller than the one specified; 2) If npx is set, the connectivity between neighboring samples is evaluated and regions with a count small than the specified value are filtered. Only one option may be set at a time. Then, the remaining pixels are dilated and the samples are again labeled accounting for regions that are not connected but are near to each other. Regions within a given distance of each other (defined by rad) are aggregated. The grid used for this analysis is built from the spatial extent of xy and a given pixel resolution (pxr). If pxr is a raster, this will be used to define the dimensions of the grid. Doing so can be of use when the user has pre-select environmental predictors that will be used for modeling. Note that the finer the resolution the more independent regions are likely to be returned. The output is a vector with ID's assigning each sample to its region. Samples filtered by npt or npx will be returned as zeros.
# NOT RUN {
{
require(raster)
# read raster data
r <- raster(system.file('extdata', 'tcb_1.tif', package="rsMove"))
# read movement data
moveData <- read.csv(system.file('extdata', 'konstanz_20130804.csv', package="rsMove"))
moveData <- SpatialPointsDataFrame(moveData[,1:2], moveData, proj4string=crs(r))
# derive region labels
labels <- labelSample(xy=moveData, rad=90, npx=2, pxr=30)
}
# }
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