Yes, right. Once not using weights, keep using the vector is not that much advantage. Except that with the vector I can discard the cells for which the original raster has no data. But this is not going to compensate. I'll try both and let you know.
Thanks a lot for your help. Agus 2010/6/30 Robert J. Hijmans <r.hijm...@gmail.com>: > Agus, You should be able to use raster::resample to align the grids; > particularly if you are coarsening the data anyway. Treating a raster > as polygons is asking for trouble; even with big machines. Robert > > > > On Wed, Jun 30, 2010 at 11:10 AM, Agustin Lobo <alobolis...@gmail.com> wrote: >> Yes, you are both right. Actually, shame to me: the better the >> machine, the more careless the user! >> >> 1. Weights are not really needed, as the polygons are much larger than >> the pixels. Ignoring those pixels not >> having their center in the polygon is good enough. >> 2. A lot (~40%) of the polygons actually lie over the ocean or over >> continents for which the raster >> has no data. Therefore I must discard the unnecessary polygons first. >> I think I can do this with maptools, >> but can do it outside R as well. The only problem is that I would >> prefer not having broken polygons, so >> polygons should be either kept or eliminated. >> >> The polygons actually come from a grid. The raster is a map of %cover >> of Betula in Europe that we >> have to coarsen to an specified grid for a model of atmospheric >> transport that we expect will predict >> pollen abundance, which we'll check against data from pollen sampling >> stations. >> >> The grid is not aligned to the raster, this is why I'm using a polygon >> and a raster instead of 2 raster layers. >> But I can reconsider this if using 2 raster layer is faster. >> >> Thanks! >> >> Agus >> >> 2010/6/30 Nikhil Kaza <nikhil.l...@gmail.com>: >>> I second that you should reconsider weights argument and zonal statistics >>> are much faster. >>> >>> >>> In case you wanted starspan download here it is >>> >>> http://projects.atlas.ca.gov/frs/?group_id=48 >>> >>> >>> Nikhil Kaza >>> Asst. Professor, >>> City and Regional Planning >>> University of North Carolina >>> >>> nikhil.l...@gmail.com >>> >>> On Jun 30, 2010, at 1:15 PM, Robert J. Hijmans wrote: >>> >>>> Dear Agus, >>>> >>>> You are extracting values for 18000 polygons for a high res raster. >>>> That is going to take a while. And using "weights=TRUE" is also bad >>>> (in terms of processing speed!); do you really need it?. You can do >>>> some testing by subsetting the polygons object. >>>> >>>> If the polygons are not overlapping, you could consider to do >>>> polygonsToRaster and then zonal. That would likely be much faster (but >>>> you would not have the weights). >>>> >>>> I have not attempted to optimize polygonValues much and 'raster' does >>>> not do multi-processor computations. I hope to have that implemented, >>>> at least for some slower functions like this one, by the end of this >>>> year. >>>> >>>> Robert >>>> >>>> On Wed, Jun 30, 2010 at 7:12 AM, Agustin Lobo <alobolis...@gmail.com> >>>> wrote: >>>>> >>>>> Hi! >>>>> I'm trying: >>>>> >>>>>> eugrd025EFDC <- readOGR(dsn="eugrd025EFDC",layer="eugrd025EFDC") >>>>> >>>>> v <- polygonValues(p=eugrd025EFDC, Br, weights=TRUE) >>>>> >>>>> where >>>>> >>>>>> str(eugrd025EFDC,max.level=2) >>>>> >>>>> Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots >>>>> ..@ data :'data.frame': 18000 obs. of 5 variables: >>>>> ..@ polygons :List of 18000 >>>>> .. .. [list output truncated] >>>>> ..@ plotOrder : int [1:18000] 17901 17900 17902 17903 17899 17898 >>>>> 17904 17897 17905 17906 ... >>>>> ..@ bbox : num [1:2, 1:2] 2484331 1314148 6575852 4328780 >>>>> .. ..- attr(*, "dimnames")=List of 2 >>>>> ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slots >>>>> >>>>>> summary(Br) >>>>> >>>>> Cells: 13967442 >>>>> NAs : 0 >>>>> >>>>> >>>>> Min. 0.00 >>>>> 1st Qu. 0.00 >>>>> Median 0.00 >>>>> Mean 48.82 >>>>> 3rd Qu. 0.00 >>>>> Max. 4999.00 >>>>> >>>>> so quite large objects. >>>>> >>>>> The problem is that polygonValues() has been running (and not >>>>> completed the task) for >>>>> more than 2 h on a intel core i7 machine with 16 Gb RAM (Dell >>>>> Precision M6500), so a pretty powerful machine. >>>>> Is there any way I could speed up this process? >>>>> Also, is there anything I could do in order to take better advantage >>>>> of the 8 processing threads? >>>>> Currently, I see only 1 cpu working for R processes and the rest >>>>> remain pretty inactive >>>>> >>>>> Thanks >>>>> >>>>> Agus >>>>> >>>>> _______________________________________________ >>>>> R-sig-Geo mailing list >>>>> R-sig-Geo@stat.math.ethz.ch >>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>>>> >>>> >>>> _______________________________________________ >>>> R-sig-Geo mailing list >>>> R-sig-Geo@stat.math.ethz.ch >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>> >>> >> > _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
