Jorge,
You could probably test using a subset of your raster. E.g. do:
plot(raster_map)
e = drawExtent() # now draw a smallish rectangle on the map
raster_map <- crop(raster_map, e)
etc.
I would use polygonValues if there were relatively few, or only very
small, polygons. From your description it appears that your polygons
cover a large part of the extent of your raster and I would go with
polygonsToRaster followed by zonal. Perhaps something along these
lines:
library(raster)
# first setting up example polygons and raster
nc <- readShapePoly(system.file("shapes/sids.shp",
package="maptools")[1], proj4string=CRS("+proj=longlat +datum=NAD27"))
r = raster(nc)
res(r) = 0.1
v <- runif(ncell(r))
v[v<0.2] <- NA
r[] <- round(v)
p <- polygonsToRaster(nc, r)
# table with counts of raster values for all polygons (but not for NA cells)
a = crosstab(p, r)
# counts of cells for all polygons
b = freq(p)
An example of the performance of the polygonsToRaster vs. polygonValues
> nc <- readShapePoly(system.file("shapes/sids.shp", package="maptools")[1],
> proj4string=CRS("+proj=longlat +datum=NAD27"))
> r = raster(nc)
> res(r) = 0.1
> r[] = round(runif(ncell(r)))
> system.time( polygonsToRaster(nc, r) )
Found 100 region(s) and 108 polygon(s)
user system elapsed
4.91 0.00 4.92
> system.time( polygonValues(nc, r) )
user system elapsed
40.69 0.09 40.78
>
Robert
On Wed, Sep 1, 2010 at 3:03 PM, Jorge Fernando Saraiva de Menezes
<jorgefernandosara...@gmail.com> wrote:
> Dear list,
>
> I have a raster, with a grid resolution of 250m2 and values of 0,1 or NA,
> and a shapefile with about 5000 polygons of areas varying from 221m2 to
> 323,629,571,955m2, which means that a polygon might have 1 billion cells
> (fortunaly, these are very few)
>
> I would like to obtain for each polygon the percentage of 0, 1 and NAs cells
> per island... I thought the following could work:
>
> polygonValues method
>
> poly_map=readShapePoly("shapefile path")
> raster_map=raster("raster path")
> objective=polygonValues(poly_map,srtm)
>
> zonal method
>
> poly_map=readShapePoly("shapefile path")
> raster_map=raster("raster path")
> rasterofpoly=polygonsToRaster(poly_map)
> objective=zonal(raster_map,rasterofpoly,stat="sum",na.rm=T)
>
> polygonstoRaster method
>
> poly_map=readShapePoly("shapefile path")
> raster_map=raster("raster path")
> objective=polygonsToRaster(poly_map,raster_map, mask=T,filename="path to new
> raster")
>
>
> but all of those take a long time to work... and i don't know a quick way of
> testing it's efficiency, so I would like to know if anyone knows which of
> those would perform the calculation faster in this situation, and if any of
> these methods are viable (i.e. would not take months or years)
>
> I have acess to a workstation with 4 processors (1 GHz each , i thnk) and
> 8Gb of memory (not sure about this configuration), using ubuntu
>
> Thanks in advance,
> Jorge Menezes
>
> [[alternative HTML version deleted]]
>
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