Description Usage Arguments Value References Examples
Returns the indices of layer y
which are nearest neighbors of each feature of layer x
. The number of nearest neighbors k
and the search radius maxdist
can be modified.
The function has three modes of operation:
lonlat points—Calculation using C code from GeographicLib
, similar to sf::st_distance
projected points—Calculation using nabor::knn
, a fast search method based on the libnabo C++ library
lines or polygons, either lonlat or projected—Calculation based on sf::st_distance
1 2 3 4 5 6 7 8 9 10 
x 
Object of class 
y 
Object of class 
sparse 

k 
The maximum number of nearest neighbors to compute. Default is 
maxdist 
Search radius (in meters). Points farther than search radius are not considered. Default is 
returnDist 

progress 
Display progress bar? The default is 
parallel 
Number of parallel processes. The default 
If sparse=TRUE
(the default), a sparse list
with list element i
being a numeric vector with the indices j
of neighboring features from y
for the feature x[i,]
, or an empty vector (integer(0)
) in case there are no neighbors.
If sparse=FALSE
, a logical
matrix with element [i,j]
being TRUE
when y[j,]
is a neighbor of x[i]
.
If returnDists=TRUE
the function returns a list
, with the first element as specified above, and the second element a sparse list
with the distances (as units
vectors, in meters) between each pair of detected neighbors corresponding to the sparse list
of indices.
C. F. F. Karney, GeographicLib, Version 1.49 (2017mmdd), https://geographiclib.sourceforge.io/1.49/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93  data(cities)
data(towns)
cities = st_transform(cities, 32636)
towns = st_transform(towns, 32636)
water = st_transform(water, 32636)
# Nearest town
st_nn(cities, towns, progress = FALSE)
# Using 'sfc' objects
st_nn(st_geometry(cities), st_geometry(towns), progress = FALSE)
st_nn(cities, st_geometry(towns), progress = FALSE)
st_nn(st_geometry(cities), towns, progress = FALSE)
# With distances
st_nn(cities, towns, returnDist = TRUE, progress = FALSE)
## Not run:
# Distance limit
st_nn(cities, towns, maxdist = 7200)
st_nn(cities, towns, k = 3, maxdist = 12000)
st_nn(cities, towns, k = 3, maxdist = 12000, returnDist = TRUE)
# 3 nearest towns
st_nn(cities, towns, k = 3)
# Spatial join
st_join(cities, towns, st_nn, k = 1)
st_join(cities, towns, st_nn, k = 2)
st_join(cities, towns, st_nn, k = 1, maxdist = 7200)
st_join(towns, cities, st_nn, k = 1)
# Polygons to polygons
st_nn(water, towns, k = 4)
# Large example  Geo points
n = 1000
x = data.frame(
lon = (runif(n) * 2  1) * 70,
lat = (runif(n) * 2  1) * 70
)
x = st_as_sf(x, coords = c("lon", "lat"), crs = 4326)
start = Sys.time()
result1 = st_nn(x, x, k = 3)
end = Sys.time()
end  start
# Large example  Geo points  Parallel processing
start = Sys.time()
result2 = st_nn(x, x, k = 3, parallel = 4)
end = Sys.time()
end  start
all.equal(result1, result2)
# Large example  Proj points
n = 1000
x = data.frame(
x = (runif(n) * 2  1) * 70,
y = (runif(n) * 2  1) * 70
)
x = st_as_sf(x, coords = c("x", "y"), crs = 4326)
x = st_transform(x, 32630)
start = Sys.time()
result = st_nn(x, x, k = 3)
end = Sys.time()
end  start
# Large example  Polygons
set.seed(1)
n = 150
x = data.frame(
lon = (runif(n) * 2  1) * 70,
lat = (runif(n) * 2  1) * 70
)
x = st_as_sf(x, coords = c("lon", "lat"), crs = 4326)
x = st_transform(x, 32630)
x = st_buffer(x, 1000000)
start = Sys.time()
result1 = st_nn(x, x, k = 3)
end = Sys.time()
end  start
# Large example  Polygons  Parallel processing
start = Sys.time()
result2 = st_nn(x, x, k = 3, parallel = 4)
end = Sys.time()
end  start
all.equal(result1, result2)
## End(Not run)

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