[R-sig-Geo] clipping SpatialPolygonsDataFrames

[Anthony Fischbach]

I wish to clip SpatialPolygonsDataFrames with a SpatialPolygonsDataFrame and
return a SpatialPolygonsDataFrames with new clipped boundaries and with data
from the source SpatialPolygonsDataFrames.
[The ArcGIS equivalent is:
arcpy.Identity_analysis("InterestingPolygonsToBeClipped","StudyAreaClippingPolygons",
                                   
"resultingPolygons.shp","ALL","#","NO_RELATIONSHIPS")]  

Some have suggested that polygon on polygon clipping may best be done by a
GIS, and have lamented that efforts to implement this within R have suffered
from depenentcies on commercial software (see for example:
http://r-sig-geo.2731867.n2.nabble.com/clipping-polygons-overlaying-big-polygon-on-polygons-td4606656.html). 
Yet, I still wish to be able to do this entirely within R, because I have
needs to clip a large number  (sample size ~ 1000) of daily sea ice extent
polygons by study area polygons, and wish to handle the tabulated results
with the richness of R.

Without the ability to clip SpatialPolygonsDataFrames by a
SpatialPolygonsDataFrame , I am trying to solve this problem with the raster
package.  I rasterize my stack of sea ice extent polygons , as well as the
study area polygons.  Then overlay operations are run within the raster
context.  
## Code sketch  ##
## Dates is a data.frame containing columns for dates, names of sea ice
shape files 
## provided by a sea ice agency, and a column for the results.
## Study Area is a SpatialPolygonsDataframe defined elsewhere.
## First read in raster of appropriate resolution (5km x 5km cells from
across the study area).
r<-raster(file='rasterOfAppropriateResolution.tif')
StudyArea.r<-rasterize(StudyArea, r)  ## rasterize a
SpatialPolygonsDataFrame of the study area
                                                         ## StudyArea.r
cells are 1 in the study area, 0 elsewhere.

for(d 1:nrow(Dates)){  ##Loop through the hundreds of days of sea ice data
     Ice<-readOGR(dsn='D:/Temp/Ice', layer=Dates[d, 'shapeFile'])  ## Read
the Ice shape file
     Ice.r<-rasterize(Ice, StudyArea.r, getCover=TRUE)   ## rasterize sea
ice SpatialPolygonsDataFrame
     IceInStudyArea.r<-Ice.r * StudyArea.r  ## raster multiplication
     Dates[d,'IceInStudyArea']<-(cellStats(IceInStudyArea.r, stat=sum)/100)
}

It works great, but runs slowly on my workstation.  Is there a better
approach?



-----
Tony Fischbach, Wildlife Biologist
Walrus Research Program
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508-4650

AFischbach at usgs.gov
http://alaska.usgs.gov/science/biology/walrus
--
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