[R-sig-Geo] Extract vs Zonal: Efficient method to extract and averaging values from large raster datasets using polygons

[ping yang]

Have you think about whether if the result of the extract function in the
raster library compare to the result from other GIS software?
I found some weird result when I compared it to the result from Zonal
Statistic from arcGIS and qGIS.
I was wondering there were some wrong with the raster::extract function.

On Mon, Oct 31, 2016 at 8:21 AM, Francisco Zambrano <frzambra at gmail.com>
wrote:

> Hi all,
>
> I need to know which is the quickest way to extract values from a large
> raster datasets (> 300 layers) using polygons (SpatialPolygonsDataFrame).
>
> Also, I need to know if worth it to use some parallelization, or just be
> enough to start the cluster with beginCluster(n) at the beginning, knowing
> that 'extract' use parallelization.
>
> Using "extract" function will be something like this:
>
> > library(raster)
> > library(maptools)
> > rasters <- stack(files_rasters)
> > polygons <- readShapePoly(file_shape)
>
> > beginCluster(8)
> > dataOut <- extract(rasters,polygons,fun='mean')
>  > endCluster()
>
> In the case of select the "zonal" function will be:
>
> > library(raster)
> > library(maptools)
> > rasters <- stack(files_rasters)
> > polygons <- readShapePoly(file_shape)
> > polygonsRaster <- rasterize(polygons, subset(rasters,1))
>
> > beginCluster(8)
> > dataOut <- zonal(rasters, polygonsRaster, 'mean')
> > endCluster()
>
> Which of those would have the quickest result?
> There is another quickest way to do it?
> If not, for those methods worth it to try some improvements such as
> parallelization?
>
> I've reviewed some discussion, but I think still there is not an answer to
> conclusive about it. Right now, I'm testing different approach, using
> smaller subset data but I still don't have a conclusion.
>
> Best to all,
>
> Francisco Zambrano
> Ph.D. Candidate from University of Concepcion, Chile.
> Visiting researcher at ITC, University of Twente, Netherlands.
>
>
> frzambra.github.io
> Agricultural Drought Webmapping <https://frzambra.shinyapps.io/shinyapp/>
>
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>
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