I asked this question previously but did not get a response, so I'll try and do a better job this time around!
I want to analyze spatial density of gas station points using R. I need to create a buffer (let's say 1,000m) around the gas stations and count the number of gas stations within the buffer. I'll then need to play around with buffer distances to see what's a reasonable buffer to see something interesting. I won't post the entire shape file because it's fairly messy, but this is what the data look like:
all <- readShapePoints("sbc_gas.shp")
all.df <- as(all, "data.frame")
head(all)
OBJECTID Fuellocati Name Latitude Longitude
1 34828 WORLD OIL #104 34.44190 -119.8304
2 48734 STOP AND SHOP GAS 34.41962 -119.6768
3 51276 EL RANCHERO MARKET 34.41911 -119.7162
4 52882 EDUCATED CAR WASH 34.44017 -119.7439
5 74038 CIRCLE K 34.63925 -120.4406
6 103685 7-ELEVEN #23855 34.40506 -119.5296
I was able to create a buffer around the points with the following code, but now how do I count the number of points within the buffer?
require(sp)
require(rdgal)
require(geosphere)
coordinates(all) <- c("Longitude", "Latitude")
pc <- spTransform(all, CRS( "+init=epsg:3347" ) )
distInMeters <- 1000
pc100km <- gBuffer(pc, width=100*distInMeters, byid=TRUE )
# Add data, and write to shapefile
pc100km <- SpatialPolygonsDataFrame(pc100km, data=pc100km@data )
writeOGR( pc100km, "pc100km", "pc100km", driver="ESRI Shapefile" )
plot(pc100km)
I'm open to other ways to go about this.
Came up with a (simple) solution for my own question using geosphere:
cbind(coordinates(all.df), X=rowSums(distm (coordinates(all.df)[,1:2], fun = distHaversine) / 1000 <= 10)) # number of points within distance 10 km
Longitude Latitude X
0 -119.8304 34.44190 25
1 -119.6768 34.41962 29
2 -119.7162 34.41911 34
3 -119.7439 34.44017 39
4 -120.4406 34.63925 13
5 -119.5296 34.40506 7
6 -120.4198 34.93860 26
7 -119.8221 34.43598 30
来源：https://stackoverflow.com/questions/42524328/create-buffer-and-count-points-in-r