Neighbourhood contiguity by distance

The function identifies neighbours of region points by Euclidean distance in the metric of the points 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. If x is an "sf" object and use_s2= is TRUE, spherical distances in km are used.

dnearneigh(x, d1, d2, row.names = NULL, longlat = NULL, bounds=c("GE", "LE"),
 use_kd_tree=TRUE, symtest=FALSE, use_s2=packageVersion("s2") > "1.0.7", k=200,
 dwithin=TRUE)

Arguments

x

matrix of point coordinates, an object inheriting from SpatialPoints or an "sf" or "sfc" object; if the "sf" or "sfc" object geometries are in geographical coordinates (use_s2=FALSE, sf::st_is_longlat(x) == TRUE and sf::sf_use_s2() == TRUE), s2 will be used to find the neighbours because it will (we hope) use spatial indexing https://github.com/r-spatial/s2/issues/125 as opposed to the legacy method which uses brute-force (at present s2 also uses brute-force)

d1

lower distance bound in the metric of the points if planar coordinates, in km if in geographical coordinates

d2

upper distance boundd in the metric of the points if planar coordinates, in km if in geographical coordinates

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 geographical 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"), (GE: greater than or equal to, LE: less than or equal to) that is the finite and closed interval [d1, d2], d1 <= x <= d2. The first element may also be "GT" (GT: greater than), the second "LT" (LT: less than) for finite, open intervals excluding the bounds; the first bound default was changed from "GT" to "GE" in release 1.1-7. When creating multiple distance bands, finite, half-open right-closed intervals may be used until the final interval to avoid overlapping on bounds: "GE", "LT", that is [d1, d2), d1 <= x < d2

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.

use_s2

default=packageVersion("s2") > "1.0.7", as of s2 > 1.0-7, distance bound computations use spatial indexing so when sf::sf_use_s2() is TRUE, s2::s2_dwithin_matrix() will be used for distances on the sphere for "sf" or "sfc" objects if s2 > 1.0-7.

k

default 200, the number of closest points to consider when searching when using s2::s2_closest_edges()

dwithin

default TRUE, if FALSE, use s2::s2_closest_edges(), both if use_s2=TRUE, sf::st_is_longlat(x) == TRUE and sf::sf_use_s2() == TRUE; s2::s2_dwithin_matrix() yields the same lists of neighbours as s2::s2_closest_edges() is k= is set correctly.

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

Roger Bivand Roger.Bivand@nhh.no

See also

knearneigh, card

Examples

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)
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 252 
#> Percentage nonzero weights: 10.49563 
#> Average number of links: 5.142857 
#> 2 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  1  2  3  4  5  6  7  8  9 10 11 
#>  4  8  6  2  5  8  6  2  6  1  1 
#> 4 least connected regions:
#> 6 10 21 47 with 1 link
#> 1 most connected region:
#> 28 with 11 links
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)))
#> Geometry set for 49 features 
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 6.221943 ymin: 11.01003 xmax: 10.95359 ymax: 14.36908
#> CRS:           NA
#> First 5 geometries:
#> POINT (8.827218 14.36908)
#> POINT (8.332658 14.03162)
#> POINT (9.012265 13.81972)
#> POINT (8.460801 13.71696)
#> POINT (9.007982 13.29637)
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)
#> [1] TRUE
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))))
#> [1] 5.161223
ll.nb <- dnearneigh(xy, 0, all.linked, longlat=FALSE)
summary(ll.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 190 
#> Percentage nonzero weights: 8.246528 
#> Average number of links: 3.958333 
#> 5 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  1  2  3  4  5  7  8  9 10 
#> 11  9  4  8  4  4  4  3  1 
#> 11 least connected regions:
#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
gc.nb <- dnearneigh(xy, 0, all.linked, longlat=TRUE)
summary(gc.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 220 
#> Percentage nonzero weights: 9.548611 
#> Average number of links: 4.583333 
#> 2 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  1  2  3  4  5  6  7  8  9 10 
#>  5  9  6  8  5  2  3  3  5  2 
#> 5 least connected regions:
#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
#> 7 28 with 10 links
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)

xy1 <- st_as_sf((as.data.frame(state.center))[m50.48,], coords=1:2,
  crs=st_crs("OGC:CRS84"))
old_use_s2 <- sf_use_s2()
sf_use_s2(TRUE)
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#>    user  system elapsed 
#>   0.006   0.000   0.006 
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#>    user  system elapsed 
#>   0.011   0.000   0.010 
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 220 
#> Percentage nonzero weights: 9.548611 
#> Average number of links: 4.583333 
#> 2 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  1  2  3  4  5  6  7  8  9 10 
#>  5  9  6  8  5  2  3  3  5  2 
#> 5 least connected regions:
#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
}
#>    user  system elapsed 
#>   0.008   0.000   0.008 
if (packageVersion("s2") > "1.0.7") {
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
}
#>    user  system elapsed 
#>   0.010   0.000   0.011 
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.dwithin, xy1, scale=0.5)
}
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 218 
#> Percentage nonzero weights: 9.461806 
#> Average number of links: 4.541667 
#> 1 region with no links:
#> 2
#> 3 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  0  1  2  3  4  5  6  7  8  9 10 
#>  1  5  8  6  8  5  2  3  3  5  2 
#> 5 least connected regions:
#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 218 
#> Percentage nonzero weights: 9.461806 
#> Average number of links: 4.541667 
#> 1 region with no links:
#> 2
#> 3 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  0  1  2  3  4  5  6  7  8  9 10 
#>  1  5  8  6  8  5  2  3  3  5  2 
#> 5 least connected regions:
#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#>    user  system elapsed 
#>   0.004   0.000   0.004 
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48 
#> Number of nonzero links: 220 
#> Percentage nonzero weights: 9.548611 
#> Average number of links: 4.583333 
#> 2 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  1  2  3  4  5  6  7  8  9 10 
#>  5  9  6  8  5  2  3  3  5  2 
#> 5 least connected regions:
#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb, gc.nb.closest, check.attributes=FALSE)
#> [1] "Component 2: Mean relative difference: 1"    
#> [2] "Component 29: Numeric: lengths (2, 1) differ"
all.equal(gc.nb, gc.nb.dwithin, check.attributes=FALSE)
#> [1] "Component 2: Mean relative difference: 1"    
#> [2] "Component 29: Numeric: lengths (2, 1) differ"
sf_use_s2(old_use_s2)
# example of reading points with readr::read_csv() yielding a tibble
load(system.file("etc/misc/coords.rda", package="spdep"))
class(coords)
#> [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame" 
k1 <- knn2nb(knearneigh(coords, k=1))
all.linked <- max(unlist(nbdists(k1, coords)))
dnearneigh(coords, 0, all.linked)
#> Neighbour list object:
#> Number of regions: 100 
#> Number of nonzero links: 676 
#> Percentage nonzero weights: 6.76 
#> Average number of links: 6.76