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.list at 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 <alobolistas at gmail.com>
wrote:
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
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