Hello,
I am trying to extract 300 meter raster cell values over Census block polygons
for each of the US states. Because 300m cells are much larger than typical
urban blocks, I had to specify "small" option (as shown below) so that no
polygon will remain "blank" without a corresponding raster value(s). Blocks
shapefile for District Columbia with about 6,500 blocks, for example, being the
smallest state, takes 15 minutes to complete, but New York with almost 350,000
block polygons would not complete even in 4 days. On a separate occasion I also
tried using parallel processing (foreach), but this only appears to slow down
the extract function, rather than speeding it up. I clipped the main raster to
the extent of each state, but this doesn't help (not much, anyway). In this
case the vector data are huge, so I would expect any improvement in efficiency
to come from reducing its burden on the process.
Please share any advice as to how I can make the extract function faster and
more efficient. Thank you very much!
Faster method: Without parallel processing:
BlockIndex <- extract(Raster_300m, Blocks, df=TRUE, small=TRUE)
Indefinitely slow: With parallel processing (8 registered cores):
BlockIndex <- foreach (i=1:length(Blocks$GEOID10), .combine='c',
.packages="raster") %dopar% {
extract(Raster_300m, Blocks[i], df=TRUE, small=TRUE)}
Regards,
Denys Dukhovnov
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