knearneigh | R Documentation |

The function returns a matrix with the indices of points belonging to the set of the k nearest neighbours of each other. If longlat = TRUE, Great Circle distances are used. A warning will be given if identical points are found.

knearneigh(x, k=1, longlat = NULL, use_kd_tree=TRUE)

`x` |
matrix of point coordinates, an object inheriting from SpatialPoints or an |

`k` |
number of nearest neighbours to be returned |

`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; longlat will override |

`use_kd_tree` |
logical value, if the dbscan package is available, use for finding k nearest neighbours when longlat is FALSE, and when there are no identical points; from https://github.com/r-spatial/spdep/issues/38, the input data may have more than two columns if dbscan is used |

The underlying legacy C code is based on the `knn`

function in the class package.

A list of class `knn`

`nn` |
integer matrix of region number ids |

`np` |
number of input points |

`k` |
input required k |

`dimension` |
number of columns of x |

`x` |
input coordinates |

Roger Bivand Roger.Bivand@nhh.no

`knn`

, `dnearneigh`

,
`knn2nb`

, `kNN`

columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE) coords <- st_centroid(st_geometry(columbus), of_largest_polygon=TRUE) col.knn <- knearneigh(coords, k=4) plot(st_geometry(columbus), border="grey") plot(knn2nb(col.knn), coords, add=TRUE) title(main="K nearest neighbours, k = 4") 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,] llk4.nb <- knn2nb(knearneigh(xy, k=4, longlat=FALSE)) gck4.nb <- knn2nb(knearneigh(xy, k=4, longlat=TRUE)) plot(llk4.nb, xy) plot(diffnb(llk4.nb, gck4.nb), xy, add=TRUE, col="red", lty=2) title(main="Differences between Euclidean and Great Circle k=4 neighbours") summary(llk4.nb, xy, longlat=TRUE, scale=0.5) summary(gck4.nb, xy, longlat=TRUE, scale=0.5) #xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,], # proj4string=CRS("+proj=longlat +ellps=GRS80")) #gck4a.nb <- knn2nb(knearneigh(xy1, k=4)) #summary(gck4a.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) system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) summary(gck4a.nb, xy1, scale=0.5) sf_use_s2(FALSE) system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) summary(gck4a.nb, xy1, scale=0.5) sf_use_s2(old_use_s2) # https://github.com/r-spatial/spdep/issues/38 if (require("dbscan", quietly=TRUE)) { set.seed(1) x <- cbind(runif(50), runif(50), runif(50)) out <- knearneigh(x, k=5) knn2nb(out) try(out <- knearneigh(rbind(x, x[1:10,]), k=5)) }

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