Introduction
Here I demonstrate how to generate k-means output from stacked raster data in R. For the .Rmd behind this output, right click, copy the link and paste in your browser my gitHub account.
Getting the data sets
I will make use of worldclim data and global administrative units data of Kenya.
my_rasters <- getData('worldclim', res = 10, var = 'tmin')
my_polygon <- getData('GADM', country = 'KEN', level = 0)
masked_rasters <- mask(crop(my_rasters, my_polygon), my_polygon)
plot(masked_rasters[[2]])
Stacking the rasters
stacked_rasters <- raster::stack(masked_rasters) Running k-means
Running k-means on raster data requires use of raster.kmeans function in ecbtools package. To install the ecbtools package use:
remotes::install_github(“ozjimbob/ecbtools”)
You may need to install remotes package first to have the install_github function.
kmeans_layer <- ecbtools::raster.kmeans(x = stacked_rasters, # The stack
k = 4,# Number of clusters
nstart = 3, # random sets
geo = TRUE, # weighting by location (x,y coords)
geo.weight = 1) # wweight to location
raster::plot(kmeans_layer)
Saving the raster output
Saving the raster into the current working directory for future reference or subsequent analysis/sharing as need may be.
raster::writeRaster(kmeans_layer, 'output_clusters.tif', overwrite = TRUE) References
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