correlogram {elsa}

Correlogram of Spatial Data

Description

Compute Correlogram of spatial data. The function returns a binned correlogram by calculating Moran's I (spatial autocorrelation) in different range of distances.

Usage

correlogram(x, width, cutoff,...) 

Arguments

x

a spatial object (RasterLayer or SpatialPointsDataFrame or SpatialPolygonsDataFrame)

width

the lag size (width of subsequent distance intervals) into which cell pairs are grouped for semivariance estimates. If missing, the cell size (raster resolution) is assigned.

cutoff

spatial separation distance up to which cell pairs are included in semivariance estimates; as a default, the length of the diagonal of the box spanning the data is divided by three.

...

Additional arguments including zcol (when x is Spatial* object, specifies the name of the variable in the dataset; longlat (when x is Spatial* object, spacifies whether the dataset has a geographic coordinate system); s (only when x is a Raster object, it would be useful when the dataset is big, so then by specifying s, the calculation would be based on a sample with size s drawn from the dataset, default is NULL means all cells should be contributed in the calculations)

Details

Correlogram is a graph to explore spatial structure in a single variable. A correlogram summarizes the spatial relations in the data, and can be used to understand within what range (distance) the data is spatially autocorrelated.

Value

Correlogram

an object containing Moran's I values within each distance interval

Author(s)

Babak Naimi [e-mail]

http://r-gis.net

References

Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019). ELSA: Entropy-based local indicator of spatial association. Spatial statistics, 29, 66-88.

Examples

file <- system.file('external/dem_example.grd',package='elsa')
r <- raster(file)
plot(r,main='a continuous raster map')

co <- correlogram(r, width=2000,cutoff=30000)

plot(co)