autocor: Global Spatial Autocorrelation Statistics In babaknaimi/elsa: Entropy-Based Local Indicator of Spatial Association

 moran R Documentation

Global Spatial Autocorrelation Statistics

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.

A numeric value.

Author(s)

Babak Naimi naimi.b@gmail.com

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)
```

babaknaimi/elsa documentation built on Nov. 21, 2022, 12:33 a.m.