entrogram: Entrogram for Spatial Data

In babaknaimi/elsa: Entropy-Based Local Indicator of Spatial Association

Entrogram for Spatial Data

Description

Compute sample (empirical) entrogram from spatial data. The function returns a binned entrogram and an entrogram cloud.

Usage

entrogram(x, width, cutoff,...) 

Arguments

x	a spatial object (RasterLayer or SpatialPoints or SpatialPolygons)
width	the lag size (width of subsequent distance intervals) into which cell pairs are grouped for ELSA estimates. If missing, the cell size (raster resolution) is assigned.
cutoff	spatial separation distance up to which cell pairs are included in ELSA 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

Entrogram is a variogram-like graph to explore spatial structure in a single variable. An entrogram summarizes the spatial relations in the data, and can be used to understand within what range (distance) the data is spatially autocorrelated.

Value

Entrogram

an object containing entrogram cloud and the entrogram 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')

en <- entrogram(r, width=2000)

plot(en)

babaknaimi/elsa documentation built on Jan. 28, 2024, 10 a.m.