Hi all, I’ve followed the thread on the updates to raster and would like to acknowledge the wonderful work done by Robert and others in making raster. It is an amazing piece of software. Thanks to all who help people like me do my job using the fruits of your labors!
Anyways, I’m working with some largish rasters and running into a step that is more computationally intensive than I would like. I’m trying to randomly sample a raster within the bounds of a polygon. E.g., in the example below I’d like to apply sampleRandom() on the raster r but constrain the sample to points within the polygons defined by xy.sp. require(raster) xy = cbind( x = c(13.4, 13.4, 13.6, 13.6, 13.4), y = c(48.9, 49, 49, 48.9, 48.9) ) hole.xy <- cbind( x = c(13.5, 13.5, 13.45, 13.45, 13.5), y = c(48.98, 48.92, 48.92, 48.98, 48.98) ) xy.sp <- SpatialPolygons(list( Polygons(list(Polygon(xy), Polygon(hole.xy, hole = TRUE)), "1"), Polygons(list(Polygon(xy + 0.2), Polygon(xy + 0.35), Polygon(hole.xy + 0.2, hole = TRUE)), "2") )) r <- raster(nrow=100, ncol=100, ext=extent(xy.sp), resolution=0.01) r[] <- runif(ncell(r)) plot(xy.sp,col="grey") plot(r,add=T,alpha=0.5) I can accomplish this with a mask via: sampleRandom(mask(r, xy.sp),size = 10) However, I have many different polygons to apply over a big raster and the mask() is taking a long time. Is there a better way to go about this? Thanks in advance, A _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo |
Hi Andy,
I've gotten around this before using package 'sf' and list-columns, here's a workflow using your data: library(sf) library(tidyverse) xy.sf <- st_as_sf(xy.sp) %>% mutate(ID = 1:nrow(.)) # need at least one attribute xy.sf.s <- xy.sf %>% # get intersecting cells mutate(intersecting_cells = # this is my go-to for row-wise functions atm, open to improvements split(., 1:nrow(.)) %>% map(., function(g) { poly_sp <- as(g, 'Spatial') # can we prioritise raster support for sf pls :P cells <- as.integer(unlist(cellFromPolygon(r, poly_sp))) cells <- if (length(cells) == 0) { NA } else { cells } }) %>% setNames(., NULL)) %>% filter(!(is.na(intersecting_cells))) %>% # drop geometry here - its easier. output is just a df with several list cols st_set_geometry(., NULL) %>% # randomly subsample each polygon's cells mutate(sample = split(., 1:nrow(.)) %>% # you could store desired sample n in another column, but lets go with 10: map(function(row) { sample(unlist(row[ , 'intersecting_cells']), size = 10, replace = FALSE) })) %>% # get values of those cells mutate(values = split(., 1:nrow(.)) %>% map(function(row) { raster::extract(r, y = unlist(row[ , 'sample'])) %>% setNames(., NULL) })) # proof that samples are where they should be samp_1 <- xyFromCell(r, xy.sf.s$sample[[1]]) %>% st_multipoint(.) %>% st_sfc() samp_2 <- xyFromCell(r, xy.sf.s$sample[[2]]) %>% st_multipoint(.) %>% st_sfc() plot(samp_1, add = T, col = 'red', pch = 19) plot(samp_2, add = T, col = 'blue', pch = 19) HTH, Lauren On Tue, Oct 24, 2017 at 2:57 AM, Andy Bunn <[hidden email]> wrote: > Hi all, I’ve followed the thread on the updates to raster and would like to acknowledge the wonderful work done by Robert and others in making raster. It is an amazing piece of software. Thanks to all who help people like me do my job using the fruits of your labors! > > Anyways, I’m working with some largish rasters and running into a step that is more computationally intensive than I would like. I’m trying to randomly sample a raster within the bounds of a polygon. E.g., in the example below I’d like to apply sampleRandom() on the raster r but constrain the sample to points within the polygons defined by xy.sp. > > > require(raster) > > xy = cbind( > x = c(13.4, 13.4, 13.6, 13.6, 13.4), > y = c(48.9, 49, 49, 48.9, 48.9) > ) > hole.xy <- cbind( > x = c(13.5, 13.5, 13.45, 13.45, 13.5), > y = c(48.98, 48.92, 48.92, 48.98, 48.98) > ) > > xy.sp <- SpatialPolygons(list( > Polygons(list(Polygon(xy), > Polygon(hole.xy, hole = TRUE)), "1"), > Polygons(list(Polygon(xy + 0.2), > Polygon(xy + 0.35), > Polygon(hole.xy + 0.2, hole = TRUE)), "2") > )) > > r <- raster(nrow=100, ncol=100, ext=extent(xy.sp), resolution=0.01) > r[] <- runif(ncell(r)) > > plot(xy.sp,col="grey") > plot(r,add=T,alpha=0.5) > > > > I can accomplish this with a mask via: > sampleRandom(mask(r, xy.sp),size = 10) > > However, I have many different polygons to apply over a big raster and the mask() is taking a long time. Is there a better way to go about this? > > Thanks in advance, A > > > _______________________________________________ > R-sig-Geo mailing list > [hidden email] > https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo |
In reply to this post by Andy Bunn-3
Andy, xy.r <- extract(r, xy.sp) On 10/23/2017 12:57 PM, Andy Bunn
wrote:
Hi all, I’ve followed the thread on the updates to raster and would like to acknowledge the wonderful work done by Robert and others in making raster. It is an amazing piece of software. Thanks to all who help people like me do my job using the fruits of your labors! Anyways, I’m working with some largish rasters and running into a step that is more computationally intensive than I would like. I’m trying to randomly sample a raster within the bounds of a polygon. E.g., in the example below I’d like to apply sampleRandom() on the raster r but constrain the sample to points within the polygons defined by xy.sp. require(raster) xy = cbind( x = c(13.4, 13.4, 13.6, 13.6, 13.4), y = c(48.9, 49, 49, 48.9, 48.9) ) hole.xy <- cbind( x = c(13.5, 13.5, 13.45, 13.45, 13.5), y = c(48.98, 48.92, 48.92, 48.98, 48.98) ) xy.sp <- SpatialPolygons(list( Polygons(list(Polygon(xy), Polygon(hole.xy, hole = TRUE)), "1"), Polygons(list(Polygon(xy + 0.2), Polygon(xy + 0.35), Polygon(hole.xy + 0.2, hole = TRUE)), "2") )) r <- raster(nrow=100, ncol=100, ext=extent(xy.sp), resolution=0.01) r[] <- runif(ncell(r)) plot(xy.sp,col="grey") plot(r,add=T,alpha=0.5) I can accomplish this with a mask via: sampleRandom(mask(r, xy.sp),size = 10) However, I have many different polygons to apply over a big raster and the mask() is taking a long time. Is there a better way to go about this? Thanks in advance, A _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo |
Sorry the exact code is: xy.r <- extract(r, xy.sp) On 10/24/2017 01:34 PM, Bacou, Melanie
wrote:
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Thanks Mel and Lauren for your replies. Very helpful indeed.
From: "Bacou, Melanie" <[hidden email]> Reply-To: "[hidden email]" <[hidden email]> Date: Tuesday, October 24, 2017 at 10:37 AM To: Andy Bunn <[hidden email]>, R-sig-Geo <[hidden email]> Subject: Re: [R-sig-Geo] sampling a raster within a polygon Sorry the exact code is: xy.r <- extract(r, xy.sp) xy.r.sample <- lapply(xy.r, sample, 10) On 10/24/2017 01:34 PM, Bacou, Melanie wrote: Andy, Simple use `extract()` instead of `mask()`, and then randomly sample cells in each polygon. Should be much faster, e.g.: xy.r <- extract(r, xy.sp) xy.r.sample <- lapply(xy.r, sample.int, n=10) --Mel. On 10/23/2017 12:57 PM, Andy Bunn wrote: Hi all, I’ve followed the thread on the updates to raster and would like to acknowledge the wonderful work done by Robert and others in making raster. It is an amazing piece of software. Thanks to all who help people like me do my job using the fruits of your labors! Anyways, I’m working with some largish rasters and running into a step that is more computationally intensive than I would like. I’m trying to randomly sample a raster within the bounds of a polygon. E.g., in the example below I’d like to apply sampleRandom() on the raster r but constrain the sample to points within the polygons defined by xy.sp. require(raster) xy = cbind( x = c(13.4, 13.4, 13.6, 13.6, 13.4), y = c(48.9, 49, 49, 48.9, 48.9) ) hole.xy <- cbind( x = c(13.5, 13.5, 13.45, 13.45, 13.5), y = c(48.98, 48.92, 48.92, 48.98, 48.98) ) xy.sp <- SpatialPolygons(list( Polygons(list(Polygon(xy), Polygon(hole.xy, hole = TRUE)), "1"), Polygons(list(Polygon(xy + 0.2), Polygon(xy + 0.35), Polygon(hole.xy + 0.2, hole = TRUE)), "2") )) r <- raster(nrow=100, ncol=100, ext=extent(xy.sp), resolution=0.01) r[] <- runif(ncell(r)) plot(xy.sp,col="grey") plot(r,add=T,alpha=0.5) I can accomplish this with a mask via: sampleRandom(mask(r, xy.sp),size = 10) However, I have many different polygons to apply over a big raster and the mask() is taking a long time. Is there a better way to go about this? Thanks in advance, A _______________________________________________ R-sig-Geo mailing list [hidden email]<mailto:[hidden email]> https://stat.ethz.ch/mailman/listinfo/r-sig-geo [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo |
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