Learn R Programming

spdep (version 1.1-7)

dnearneigh: Neighbourhood contiguity by distance

Description

The function identifies neighbours of region points by Euclidean distance between lower (greater than or equal to (changed from version 1.1-7)) and upper (less than or equal to) bounds, or with longlat = TRUE, by Great Circle distance in kilometers.

Usage

dnearneigh(x, d1, d2, row.names = NULL, longlat = NULL, bounds=c("GE", "LE"),
 use_kd_tree=TRUE, symtest=FALSE)

Arguments

x

matrix of point coordinates or a SpatialPoints object or an sf or sfc points 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("GE", "LE"), the first element may also be "GE", the second "LT"; the first bound default was changed from "GT" to "GE" in release 1.1-7

use_kd_tree

default TRUE, if TRUE, use dbscan frNN if available (permitting 3D distances).

symtest

Default FALSE; before release 1.1-7, TRUE - run symmetry check on output object, costly with large numbers of points.

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.

See Also

knearneigh, card

Examples

Run this code
# NOT RUN {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
coords <- st_centroid(st_geometry(columbus), of_largest_polygon=TRUE)
rn <- row.names(columbus)
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)
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
 main=paste("Distance based neighbours 0-",  format(all.linked), sep=""))
plot(col.nb.0.all, coords, add=TRUE)
par(opar)
(sfc_obj <- st_centroid(st_geometry(columbus)))
col.nb.0.all_sf <- dnearneigh(sfc_obj, 0, all.linked, row.names=rn)
all.equal(col.nb.0.all, col.nb.0.all_sf, check.attributes=FALSE)
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 Euclidean/Great Circle")

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)
# }

Run the code above in your browser using DataLab