Measure spatial autocorrelation in the predictor raster files
This function provides a quantitative basis for choosing block size. The spatial autocorrelation in all continuous predictor variables available as raster layers is assessed and reported. The function estimates spatial autocorrelation ranges of all input raster layers. This is the range over which observations are independent and is determined by constructing the empirical variogram, a fundamental geostatistical tool for measuring spatial autocorrelation. The empirical variogram models the structure of spatial autocorrelation by measuring variability between all possible pairs of points (O'Sullivan and Unwin, 2010). Results are plotted. See the details section for further information.
spatialAutoRange( rasterLayer, sampleNumber = 5000L, border = NULL, speciesData = NULL, doParallel = FALSE, nCores = NULL, showPlots = TRUE, degMetre = 111325, maxpixels = 1e+05, plotVariograms = FALSE, progress = TRUE )
rasterLayer |
A raster object of covariates to find spatial autocorrelation range. |
sampleNumber |
Integer. The number of sample points of each raster layer to fit variogram models. It is 5000 by default, however it can be increased by user to represent their region well (relevant to the extent and resolution of rasters). |
border |
A sf or SpatialPolygons object to clip the block based on it (optional). |
speciesData |
A spatial or sf object (optional). If provided, the |
doParallel |
Logical. Run in parallel when more than one raster layer is available. Given multiple CPU cores, it is
recommended to set it to |
nCores |
Integer. Number of CPU cores to run in parallel. If |
showPlots |
Logical. Show final plot of spatial blocks and autocorrelation ranges. |
degMetre |
Numeric. The conversion rate of metres to degree. This is for constructing spatial blocks for visualisation. When the input map is in geographic coordinate system (decimal degrees), the block size is calculated based on deviding the calculated range by this value to convert to the input map's unit (by default 111325; the standard distance of a degree in metres, on the Equator). |
maxpixels |
Number of random pixels to select the blocks over the study area. |
plotVariograms |
Logical. Plot fitted variograms. This can also be done after the analysis.
It is |
progress |
Logical. Shows progress bar. It works only when |
The input raster layers should be continuous for computing the variograms and estimating the range of spatial autocorrelation. The input rasters should also have a specified coordinate reference system. However, if the reference system is not specified, the function attempts to guess it based on the extent of the map. It assumes an unprojected reference system for layers with extent lying between -180 and 180, and a projected reference system otherwise.
Variograms are calculated based on the distances between pairs of points, so unprojected rasters (in degrees) will
not give an accurate result (especially over large latitudinal extents). For unprojected rasters, the great circle distance (rather
than Euclidian distance) is used to calculate the spatial distances between pairs of points. To
enable more accurate estimate, it is recommended to transform unprojected maps (geographic coordinate
system / latitude-longitude) to a projected metric reference system (e.g. UTM or Lambert) where it is possible.
See autofitVariogram from automap and variogram from gstat packages
for further information.
An object of class S3. A list object including:
range - the suggested range, which is the median of all calculated ranges
rangeTable - a table of input covariates names and their autocorrelation range
plots - the output plot (the plot is shown by default)
sampleNumber
variograms - fitted variograms for all layers
O'Sullivan, D., Unwin, D.J., (2010). Geographic Information Analysis, 2nd ed. John Wiley & Sons.
Roberts et al., (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography. 40: 913-929.
# load the example raster data
awt <- raster::brick(system.file("extdata", "awt.grd", package = "blockCV"))
# run the model in parallel
range1 <- spatialAutoRange(rasterLayer = awt,
sampleNumber = 5000, # number of cells to be used
doParallel = TRUE,
nCores = 2, # if NULL, it uses half of the CPU cores
plotVariograms = FALSE,
showPlots = TRUE)
# run the model with no parallel
range3 <- spatialAutoRange(rasterLayer = awt,
sampleNumber = 5000,
doParallel = FALSE,
progress = TRUE)
# show the result
summary(range1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.