Spatial K nearest neighbor

Find K nearest neighbors for two spatial objects

Finds nearest neighbor in x based on y and returns rownames, index and distance, If ids is NULL, rownames of x are returned. If coordinate matrix provided, columns need to be ordered [X,Y]. If a radius for d is specified than a maximum search radius is imposed. If no neighbor is found, a neighbor is not returned

You can specify weights to act as covariates for x and y. The vectors or matrices must match row dimensions with x and y as well as columns matching between weights. In other words, the covariates must match and be numeric.

knn(
  y,
  x,
  k = 1,
  d = NULL,
  ids = NULL,
  weights.y = NULL,
  weights.x = NULL,
  indexes = FALSE
)

Arguments

y	Spatial points or polygons object or coordinates matrix
x	Spatial points or polygons object or coordinates matrix
k	Number of neighbors
d	Optional search radius
ids	Optional column of ID's in x
weights.y	A vector or matrix representing covariates of y
weights.x	A vector or matrix representing covariates of x
indexes	(FALSE/TRUE) Return row indexes of x neighbors

Value

A data.frame with row indexes (optional), rownames, ids (optional) and distance of k

Author

Jeffrey S. Evans <jeffrey_evans@tnc.org>

Examples

 library(sp)
 data(meuse)
   coordinates(meuse) <- ~x+y
 
 idx <- sample(1:nrow(meuse), 10) 
   pts <- meuse[idx,]
   meuse <- meuse[-idx,]
     meuse$IDS <- 1:nrow(meuse)
 
 # Find 2 neighbors in meuse
 ( nn <- knn(pts, meuse, k=2, ids = "IDS", indexes = TRUE) )
#>    idx1 idx2 ids1 ids2     dist1     dist2
#> 1   135  136  135  136 125.03200 181.10770
#> 2    55   50   55   50 133.76472 136.61625
#> 3    97   65   97   65 183.35757 270.22398
#> 4    65  102   65  102 131.02671 137.31715
#> 5    31   33   31   33  62.62587 169.00000
#> 6    56   57   56   57 110.72488 117.54574
#> 7   103   65  103   65 107.37784 114.72576
#> 8    48   47   48   47 167.19450 207.32824
#> 9    32   31   32   31  81.02469 117.79643
#> 10   22   21   22   21  53.00000  76.02631
    plot(pts, pch=19, main="KNN")
      points(meuse[nn[,1],], pch=19, col="red")

# Using covariates (weights)
wx = as.matrix(meuse@data[,1:3])
wy = as.matrix(pts@data[,1:3])

( nn <- knn(pts, meuse, k=2, ids = "IDS", indexes = TRUE,
            weights.y=wy, weights.x=wx) )
#>    idx1 idx2 ids1 ids2     dist1    dist2
#> 1   135  136  135  136 129.88518 181.3386
#> 2    55   51   55   51 177.17847 229.8103
#> 3    97  144   97  144 184.14712 353.7544
#> 4   102  103  102  103 137.37918 253.7815
#> 5    31   33   31   33  64.24671 170.2551
#> 6   113   57  113   57 159.33926 173.6855
#> 7   103   64  103   64 108.83033 194.1445
#> 8    48   98   48   98 172.64487 223.2444
#> 9    32   31   32   31  81.05652 118.9832
#> 10   22   21   22   21  54.57115 114.7465
    plot(pts, pch=19, main="KNN")
      points(meuse[nn[,1],], pch=19, col="red")
    
 # Using coordinate matrices
 y <- coordinates(pts)
 x <- coordinates(meuse)
 knn(y, x, k=2)
#>    ids1 ids2     dist1     dist2
#> 1   149  151 125.03200 181.10770
#> 2    60   54 133.76472 136.61625
#> 3   105   76 183.35757 270.22398
#> 4    76  114 131.02671 137.31715
#> 5    32   37  62.62587 169.00000
#> 6    61   62 110.72488 117.54574
#> 7   116   76 107.37784 114.72576
#> 8    52   51 167.19450 207.32824
#> 9    35   32  81.02469 117.79643
#> 10   22   21  53.00000  76.02631

Arguments

Value

See also

Author

Examples