Class comparison between two nominal rasters

Source: R/cross.tab.R

Creates a labeled cross tabulation between two nominal rasters

cross.tab(x, y, values = NULL, labs = NULL, pct = FALSE, ...)

Arguments

x

rasterLayer class object
y

rasterLayer class object to compare to x
values

Expected values in both rasters
labs

Labels associated with values argument
pct

(TRUE/FALSE) return proportions rather than counts
...

Additional arguments

Value

a table with the cross tabulated counts

Note

This function returns a cross tabulation between two nominal rasters. Arguments allow for labeling the results and returning proportions rather than counts. It also accounts for asymmetrical classes between the two rasters

References

Pontius Jr, R.G., Shusas, E., McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment 101(2):251-268.

See also

raster::crosstab

Author

Jeffrey S. Evans <jeffrey_evans@tnc.org>

Examples

 library(sp)
 library(raster)
   data(meuse.grid)
 
 r1 <- sp::SpatialPixelsDataFrame(points = meuse.grid[c("x", "y")], 
                                  data = meuse.grid)
 lulc2010 <- raster(r1)
   na.idx <- which(!is.na(lulc2010[]))
     lulc2010[na.idx] <- sample(1:5, length(na.idx), replace=TRUE)
  
 lulc2020 <- raster(lulc2010)
   lulc2020[na.idx] <- sample(1:5, length(na.idx), replace=TRUE)
 
 ( v = sort(unique(c(lulc2010[], lulc2020[]))) )

#> [1] 1 2 3 4 5
 l = c("water","urban","forest",
       "ag","barren")

 cross.tab(lulc2010, lulc2020) 

#>         lulc2010
#> lulc2020   1   2   3   4   5
#>        1 109 123 126 132 123
#>        2 123 129 136 110 117
#>        3 114 129 122 139 129
#>        4 125 124 117 129 118
#>        5 132 103 124 139 131
 cross.tab(lulc2010, lulc2020, values = v, labs = l)

#>         lulc2010
#> lulc2020 water urban forest  ag barren
#>   water    109   123    126 132    123
#>   urban    123   129    136 110    117
#>   forest   114   129    122 139    129
#>   ag       125   124    117 129    118
#>   barren   132   103    124 139    131
 cross.tab(lulc2010, lulc2020, values = v, labs = l, pct=TRUE)

#>         lulc2010
#> lulc2020   water   urban  forest      ag  barren
#>   water  0.17781 0.20000 0.18009 0.20392 0.20986
#>   urban  0.20065 0.20976 0.20379 0.20228 0.16375
#>   forest 0.20555 0.22114 0.19273 0.19086 0.19714
#>   ag     0.21533 0.17886 0.21959 0.21044 0.22099
#>   barren 0.20065 0.19024 0.20379 0.19250 0.20827

# Create asymmetrical classes 
lulc2020[na.idx] <- sample(c(1,2,4,5), length(na.idx), replace=TRUE)

cross.tab(lulc2010, lulc2020, values = v, labs = l, pct=TRUE)

#>         lulc2010
#> lulc2020   water   urban  forest      ag  barren
#>   water  0.26427 0.23252 0.25592 0.24144 0.26391
#>   urban  0.28385 0.23902 0.27804 0.27080 0.24801
#>   ag     0.23817 0.25203 0.22433 0.24959 0.24642
#>   barren 0.21370 0.27642 0.24171 0.23817 0.24165