Where's my towel - 5 months ago 24

R Question

Pictures are better than words, so please have a look at

What I have is

- a RasterLayer object (filled with random values here for illustration purposes only, the actual values don't matter)
- a SpatialPolygons object with lots and lots of polygons in it

You can re-create the example data I used for the image with the following code:

`library(sp)`

library(raster)

library(rgeos)

# create example raster

r <- raster(nrows=10, ncol=15, xmn=0, ymn=0)

values(r) <- sample(x=1:1000, size=150)

# create example (Spatial) Polygons

p1 <- Polygon(coords=matrix(c(50, 100, 100, 50, 50, 15, 15, 35, 35, 15), nrow=5, ncol=2), hole=FALSE)

p2 <- Polygon(coords=matrix(c(77, 123, 111, 77, 43, 57, 66, 43), nrow=4, ncol=2), hole=FALSE)

p3 <- Polygon(coords=matrix(c(110, 125, 125, 110, 67, 75, 80, 67), nrow=4, ncol=2), hole=FALSE)

lots.of.polygons <- SpatialPolygons(list(Polygons(list(p1, p2, p3), 1)))

crs(lots.of.polygons) <- crs(r) # copy crs from raster to polygons (please ignore any potential problems related to projections etc. for now)

# plot both

plot(r) #values in this raster for illustration purposes only

plot(lots.of.polygons, add=TRUE)

For each cell in the raster, I want to know how much of it is covered by one or more polygons. Or actually: the area of all polygons within the raster cell, without what is outside the cell in question. If there are multiple polygons overlapping a cell, I only need their combined area.

The following code does what I want, but takes more than a week to run with the actual data sets:

`# empty the example raster (don't need the values):`

values(r) <- NA

# copy of r that will hold the results

r.results <- r

for (i in 1:ncell(r)){

r.cell <- r # fresh copy of the empty raster

r.cell[i] <- 1 # set the ith cell to 1

p <- rasterToPolygons(r.cell) # create a polygon that represents the i-th raster cell

cropped.polygons <- gIntersection(p, lots.of.polygons) # intersection of i-th raster cell and all SpatialPolygons

if (is.null(cropped.polygons)) {

r.results[i] <- NA # if there's no polygon intersecting this raster cell, just return NA ...

} else{

r.results[i] <- gArea(cropped.polygons) # ... otherwise return the area

}

}

plot(r.results)

plot(lots.of.polygons, add=TRUE)

I can squeeze out a bit more speed by using

`sapply`

`for`

`rasterize()`

`fun=`

Answer

Maybe `gIntersection(..., byid = T)`

with `gUnaryUnion(lots.of.polygons)`

(they enable you to treat all cells at once) is faster than for loop (If `gUnaryUnion()`

takes too much time, this is a bad idea).

```
r <- raster(nrows=10, ncol=15, xmn=0, ymn=0)
set.seed(1); values(r) <- sample(x=1:1000, size=150)
rr <- rasterToPolygons(r)
# joining intersecting polys and put all polys into single SpatialPolygons (in this example, it is unnecessary)
lots.of.polygons <- gUnaryUnion(lots.of.polygons)
gi <- gIntersection(rr, lots.of.polygons, byid = T)
ind <- as.numeric(do.call(rbind, strsplit(names(gi), " "))[,1])
r[] <- NA
r[ind] <- sapply(gi@polygons, function(x) slot(x, 'area')) # a bit faster than gArea(gi, byid = T)
plot(r)
plot(lots.of.polygons, add=TRUE)
```