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geocomplexity for spatial raster data based on spatial dependence

Source: R/geocd_raster.R
geocd_raster.Rd

This function calculates geocomplexity for spatial raster data based on spatial dependence.

Usage

geocd_raster(r, order = 1, normalize = TRUE, method = "moran")

Arguments

r

SpatRaster object or can be converted to SpatRaster by terra::rast().

order

(optional) The order of the adjacency object. Default is 1.

normalize

(optional) Whether to further normalizes the calculated geocomplexity. Default is TRUE.

method

(optional) In instances where the method is moran, geocomplexity is determined using local moran measure method. Conversely, when the method is spvar, the spatial variance of attribute data serves to characterize geocomplexity. For all other methods, the shannon information entropy of attribute data is employed to represent geocomplexity. The selection of the method can be made from any one of the three options: moran, spvar or entropy. Default is moran.

Value

A SpatRaster object

Note

In contrast to the geocd_vector() function, the geocd_raster() performs operations internally on raster data based on neighborhood operations(focal) without providing additional wt object.

References

Zehua Zhang, Yongze Song, Peng Luo & Peng Wu (2023) Geocomplexity explains spatial errors, International Journal of Geographical Information Science, 37:7, 1449-1469, DOI: 10.1080/13658816.2023.2203212

Anselin, L. (2019). A local indicator of multivariate spatial association: Extending geary’s c. Geographical Analysis, 51(2), 133–150. https://doi.org/10.1111/gean.12164

Examples

library(terra)
#> terra 1.7.83
m = matrix(c(3,3,3,3,1,3,
             3,3,3,2,1,2,
             3,3,3,1,2,1,
             1,3,2,2,2,2,
             2,2,2,1,1,2,
             1,2,1,1,1,1),
           nrow = 6,
           byrow = TRUE)
m = rast(m)
names(m) = 'sim'
plot(m, col = c("#d2eaac", "#a3dae1", "#8cc1e1"))

gc1 = geocd_raster(m,1)
gc2 = geocd_raster(m,2)
gc1
#> class       : SpatRaster 
#> dimensions  : 6, 6, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 6, 0, 6  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        : GC_sim 
#> min value   :      0 
#> max value   :      1 
plot(gc1)

gc2
#> class       : SpatRaster 
#> dimensions  : 6, 6, 1  (nrow, ncol, nlyr)
#> resolution  : 1, 1  (x, y)
#> extent      : 0, 6, 0, 6  (xmin, xmax, ymin, ymax)
#> coord. ref. :  
#> source(s)   : memory
#> name        : GC_sim 
#> min value   :      0 
#> max value   :      1 
plot(gc2)