computing nearest neighbours with RANN

nn.R

## lets use this nearest neighbour package:
require(RANN)

## create a village as a cluster of houses around x,y:
village <- function(x, y, n, id, sd){
    ## return a data frame of x, y, and id values:
    data.frame(x=x+rnorm(n,0,sd),
               y=y+rnorm(n,0,sd),
               id=id
               )
}

## create some villages with some number of houses and spatial sd
## with centres in unit square:
villages <- function(nvillages, mu, sd){
    ## return a data frame of x, y, and village id values:
    do.call(rbind,
            lapply(1:nvillages,function(i){
                village(runif(1),runif(1),rpois(1,mu)+1,i,sd)
            })
    )
}


## create a grid over the x and y coordinates:
vgrid <- function(villages, ng=100){
    grid = expand.grid(
        x = seq(min(villages$x), max(villages$x),len=ng),
        y = seq(min(villages$y), max(villages$y),len=ng)
        )
    grid
}

## make some data:
set.seed(3)

## 3 villages, with about 5 houses, spread about 0.06
v = villages(3, 5, .06)
vg = vgrid(v, 100)

## now compute first (k=1) nearest neighbour to each grid point
##  limit search to 0.1 units:
nns = nn2(v[,c("x","y")], vg, k=1, radius=.1, searchtype="radius")

## index of nearest village locations is this component.
## when a grid point has no neighbour in range this is zero:
iv = nns$nn.idx

## we are going to use this to index into the village data frame,
## and indexing by zero is going to fail (comment this line out to
## see how badly), so set to NA
iv[iv==0] <- NA

## plot the grid coloured by colour of corresponding village id:
plot(vg$x, vg$y, col=v$id[iv], pch=19, asp=1)

## add village points with a white outline marker in the village colour.
## you should just see white circles with the inner colour matching the
## region colour:
points(v$x, v$y, bg=v$id, asp=1, pch=21, col="white")