Neighbourhood contiguity by distance

Description

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.

Usage

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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.

Author(s)

Roger Bivand Roger.Bivand@nhh.no

See Also

knearneigh, card

Examples

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example(columbus)
coords <- coordinates(columbus)
rn <- sapply(slot(columbus, "polygons"), function(x) slot(x, "ID"))
k1 <- knn2nb(knearneigh(coords))
all.linked <- max(unlist(nbdists(k1, coords)))
col.nb.0.all <- dnearneigh(coords, 0, all.linked, row.names=rn)
summary(col.nb.0.all, coords)
plot(columbus, border="grey")
plot(col.nb.0.all, coords, add=TRUE)
title(main=paste("Distance based neighbours 0-",  format(all.linked),
 " distance units", sep=""))
data(state)
us48.fipsno <- read.geoda(system.file("etc/weights/us48.txt",
 package="spdep")[1])
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))
all.linked <- max(unlist(nbdists(llk1, xy, 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))
all.linked <- max(unlist(nbdists(gck1, xy, 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))
all.linked <- max(unlist(nbdists(gck1a, xy1)))
gc.nb <- dnearneigh(xy1, 0, all.linked)
summary(gc.nb, xy1, scale=0.5)

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