Merging spatial buffers in R

June 11, 2018
By

(This article was first published on R – Insights of a PhD, and kindly contributed to R-bloggers)

I’m sure there’s a better way out there, but I struggled to find a way to dissolve polygons that touched/overlapped each other (the special case being buffers). For example,  using the osmdata package, we can download the polygons representing hospital buildings in Bern, Switzerland.

library(osmdata)
library(rgdal) ; library(maptools) ; library(rgeos)

q0 <- opq(bbox = "Bern, Switzerland", timeout = 60)
q1 <- add_osm_feature(q0, key = 'building', value = "hospital")
x <- osmdata_sp(q1)

library(leaflet)

spChFIDs(x$osm_polygons) <- 1:nrow([email protected])
cent <- gCentroid(x$osm_polygons, byid = TRUE)
leaflet(cent) %>% addTiles() %>% addCircles()

Here we plot the building centroids.

hospcent

Each point represents a hospital building. We don’t particularly care about the buildings themselves though. We want to create hospitals. To do so, we try a 150m buffer around each centroid.

buff <- gBuffer(cent, byid = TRUE, width = 0.0015)
leaflet(cent) %>% addTiles() %>% addPolygons(data = buff, col = "red") %>% addCircles()

hospbuff

We then want to merge the buffers into, in this case, four groups. This is the point that doesn’t seem to be implemented anywhere that I could see (I also tried QGIS but that just created one big feature, rather than many small ones). My approach is to get the unique sets of intersections, add them as a variable to the buffer and unify the polygons.

buff <- SpatialPolygonsDataFrame(buff, data.frame(row.names = names(buff), n = 1:length(buff)))
gt <- gIntersects(buff, byid = TRUE, returnDense = FALSE)
ut <- unique(gt)
nth <- 1:length(ut)
buff$n <- 1:nrow(buff)
buff$nth <- NA
for(i in 1:length(ut)){
  x <- ut[[i]]
  buff$nth[x] <- i
}
buffdis <- gUnaryUnion(buff, buff$nth)
leaflet(cent) %>% addTiles() %>% addPolygons(data = buffdis, col = "red") %>% addCircles()

hospbuff2.png

As you see, it almost worked. The lower left group is composed of three polygons. Doing the same process again clears it (only code shown). Large jobs might need more iterations (or larger buffers). The final job is to get the hospital centroids.

gt <- gIntersects(buffdis, byid = TRUE, returnDense = FALSE)
ut <- unique(gt)
nth <- 1:length(ut)
buffdis <- SpatialPolygonsDataFrame(buffdis, data.frame(row.names = names(buffdis), n = 1:length(buffdis)))
buffdis$nth <- NA
for(i in 1:length(ut)){
  x <- ut[[i]]
  buffdis$nth[x] <- i
}
buffdis <- gUnaryUnion(buffdis, buffdis$nth)
leaflet(cent) %>% addTiles() %>% addPolygons(data = buffdis, col = "red") %>% addCircles()

buffcent <- gCentroid(buffdis, byid = TRUE

Code here.

To leave a comment for the author, please follow the link and comment on their blog: R – Insights of a PhD.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)