autocor: Global Spatial Autocorrelation Statistics

In elsa: Entropy-Based Local Indicator of Spatial Association

Description

Functions to calculate Moran's I and Geary's c statistics.

Usage

moran(x,d1,d2,...) 
geary(x,d1,d2,...) 

Arguments

x	a raster object (RasterLayer or SpatialPointsDataFrame or SpatialPolygonsDataFrame
d1	lower bound local distance, or an object of class neighbours created by dneigh when x is SpatialPoints or SpatialPolygons
d2	upper bound local distance
...	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

Details

moran and geary are two functions to measure global spatial autocorrelation within the range of distance specified through d1 and d2. It returns a single numeric value than can show the degree of spatial autocorrelation in the whole dataset.

Value

A numeric value.

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)

moran(r, d1=0, d2=2000)

geary(r, d1=0, d2=2000)

elsa documentation built on March 19, 2020, 5:12 p.m.