# dnearneigh

0th

Percentile

##### Neighbourhood contiguity by distance

The function identifies neighbours of region points by Euclidean distance between lower (greater than) and upper (less than or equal to) bounds, or with longlat = TRUE, by Great Circle distance in kilometers.

Keywords
spatial
##### Usage
dnearneigh(x, d1, d2, row.names = NULL, longlat = NULL, bounds=c("GT", "LE"))
##### Arguments
x
matrix of point coordinates or a SpatialPoints object
d1
lower distance bound
d2
upper distance bound
row.names
character vector of region ids to be added to the neighbours list as attribute region.id, default seq(1, nrow(x))
longlat
TRUE if point coordinates are longitude-latitude decimal degrees, in which case distances are measured in kilometers; if x is a SpatialPoints object, the value is taken from the object itself, and overrides this argument if not NULL
bounds
character vector of length 2, default c("GT", "LE"), the first element may also be "GE", the second "LT"
##### Value

The function returns a list of integer vectors giving the region id numbers for neighbours satisfying the distance criteria. See card for details of “nb” objects.

knearneigh, card

• dnearneigh
##### Examples
example(columbus)
coords <- coordinates(columbus)
rn <- sapply(slot(columbus, "polygons"), function(x) slot(x, "ID"))
k1 <- knn2nb(knearneigh(coords))
col.nb.0.all <- dnearneigh(coords, 0, all.linked, row.names=rn)
summary(col.nb.0.all, coords)
plot(columbus, border="grey")
" distance units", sep=""))
data(state)
package="spdep"))
if (as.numeric(paste(version$major, version$minor, sep="")) < 19) {
m50.48 <- match(us48.fipsno$"State.name", state.name) } else { m50.48 <- match(us48.fipsno$"State_name", state.name)
}
xy <- as.matrix(as.data.frame(state.center))[m50.48,]
llk1 <- knn2nb(knearneigh(xy, k=1, longlat=FALSE))
ll.nb <- dnearneigh(xy, 0, all.linked, longlat=FALSE)
summary(ll.nb, xy, longlat=TRUE, scale=0.5)
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
gc.nb <- dnearneigh(xy, 0, all.linked, longlat=TRUE)
summary(gc.nb, xy, longlat=TRUE, scale=0.5)
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences between Euclidean and Great Circle neighbours")

xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
proj4string=CRS("+proj=longlat +ellps=GRS80"))
gck1a <- knn2nb(knearneigh(xy1, k=1))