R/analysis/raster_tile.R

In Martin-Jung/Icarus: Spatial-Temporal analysis on PREDICTS data

# To parallelize spatial data, you often need to tile the data and process each tile separately. 
# Here is a function that will take a bounding box, tile size and generate a tiling system. 
# If given an overlap term, it will also add buffers to the tiles to reduce/eliminate edge effects, though this depends on what algorithm/model you are using.

tilebuilder=function(raster,size=10,overlap=NULL){
  ## get raster extents
  xmin=xmin(raster)
  xmax=xmax(raster)
  ymin=ymin(raster)
  ymax=ymax(raster)
  xmins=c(seq(xmin,xmax-size,by=size))
  ymins=c(seq(ymin,ymax-size,by=size))
  exts=expand.grid(xmin=xmins,ymin=ymins)
  exts$ymax=exts$ymin+size
  exts$xmax=exts$xmin+size
  if(!is.null(overlap)){
    #if overlapped tiles are requested, create new columns with buffered extents
    exts$yminb=exts$ymin
    exts$xminb=exts$xmin
    exts$ymaxb=exts$ymax
    exts$xmaxb=exts$xmax
    
    t1=(exts$ymin-overlap)>=ymin
    exts$yminb[t1]=exts$ymin[t1]-overlap
    t2=exts$xmin-overlap>=xmin
    exts$xminb[t2]=exts$xmin[t2]-overlap    
    t3=exts$ymax+overlap<=ymax
    exts$ymaxb[t3]=exts$ymax[t3]+overlap
    t4=exts$xmax+overlap<=xmax
    exts$xmaxb[t4]=exts$xmax[t4]+overlap  
  }
  exts$tile=1:nrow(exts)
  return(exts)
}