Hello all!
I need to spatially calculate 30-meter grid raster mean over US Census blocks
in North East region (approx. 1.9 mln street blocks/polygons). I came up with
the script that runs in parallel, but it still takes days to complete, even
when the i7 CPU is used up to the max. My question: is there any other way to
improve the processing speed given the set-up below (I included the backbone of
the script to save space). I am new to R, so any help will be much appreciated.
Thanks a lot!
Regards,
Denys
____________________________
library(sp)
library(rgdal)
library(raster)
library(foreach)
library(spatial.tools)
Grid30m <- raster("raster.tif") # loading main raster into R
NEfips <- c(09, 10, 11, ....) # list of state FIPS codes, whose street
block polygons are to be processed
ShapePath <- "T:\\..." # path to block shapefiles
sfQuickInit(cpus=8) # setting up and registering parallel
processing back-end
for (x in NEfips) {
State <- paste("block_", x , sep="")
Blocks <- readOGR(dsn=ShapePath, layer=State[1], stringsAsFactors=F) #
loading the block polygons
BlocksReproject <- spTransform(Blocks, crs(Grid30m))
GridMean <- foreach (i=1:length(BlocksReproject$GEOID10), .combine='c',
.packages="raster") %dopar% { # Parallel processing loop for extracting
mean raster value per street block polygon
extract(Grid30m, BlocksReproject[i], fun=mean)
}
write.table(...) # Generating output containing the mean raster
statistics for each block polygon.
}
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