R/nearest_point_geo.R
get_nearest_point.RdFunction to select nearest community to a given sampling point (usually the centroid of a square grid for CSAS or of a hexagonal grid for S3M) using geodesic calculations based on a specified reference ellipsoid
get_nearest_point( data, data.x, data.y, query, n = 1, ellipsoid = c("AA", "AN", "??", "BR", "BN", "CC", "CD", "EB", "EA", "EC", "EF", "EE", "ED", "RF", "HE", "HO", "ID", "IN", "KA", "AM", "FA", "SA", "WD", "WE"), duplicate = FALSE )
| data | An input data frame or matrix containing longitude and latitude coordinates of village locations from which to find nearest neighbour |
|---|---|
| data.x | A character value specifying the variable name in |
| data.y | A character value specifying the variable name in |
| query | an object of class `SpatialPoints` containing sampling point locations. This is usually the output from applying `spsample()` function from package `gstat` to create an even spatial sample across the entire sampling area |
| n | Number of nearest villages to select |
| ellipsoid | Two letter character value specifying the reference ellipsoid to use for distance calculations |
| duplicate | Logical. Specify if duplicate selected villages are to be kept or discarded. Default is FALSE (discard). |
A subset data frame of data of selected villages/communities
nearest to the sampling points with a new variable d indicating
the distance of the village/community to the sampling point (in kms). If
duplicate is TRUE, the result has number of rows equal to
nrow(query)
# Use get_nearest_point() with test sampling points in Sennar to find 3 nearest #communities from the sampling points sennar <- subset(sudan01, STATE == "Sennar") samp.points <- sp::spsample(sennar, type = "hexagonal", n = 10)#> Warning: Discarded datum WGS_1984 in CRS definition, #> but +towgs84= values preserved#> Warning: Discarded datum WGS_1984 in CRS definition, #> but +towgs84= values preserved#> Warning: Discarded datum WGS_1984 in CRS definition, #> but +towgs84= values preserved#> Warning: CRS object has comment, which is lost in outputget_nearest_point(data = sennar_villages, data.x = "x", data.y = "y", query = samp.points, n = 3)#> Warning: CRS object has comment, which is lost in output#> Warning: Discarded datum Unknown based on WGS84 ellipsoid in CRS definition, #> but +towgs84= values preserved#> spid id x y village #> 722 1 923 33.81400 12.11506 AlGreibin #> 723 1 924 33.83056 12.11183 Tagali (AlMazmoum) #> 724 1 925 33.82814 12.10714 Wad Bireiga #> 124 2 124 34.77686 12.85164 Om Sagit #> 126 2 126 34.79900 12.84908 Om Bagara Sharig #> 244 2 244 34.79008 12.82597 Um Bagara Garb #> 18 3 18 33.70356 12.77214 UmMarda #> 19 3 19 33.56717 12.72828 Abo Rawag #> 731 3 932 33.53467 12.72550 Hafir kamtor #> 257 4 257 34.12308 12.78933 Alazara Alrawaj #> 258 4 258 34.14547 12.77569 Tayba #> 259 4 259 34.13689 12.77933 Alwa #> 499 6 499 33.14697 13.41047 Dood Albataheen + Madani Ewiasa #> 500 6 500 33.15650 13.40961 Dood Albasheer #> 501 6 501 33.15042 13.40625 Dood Abdallah #> 303 7 303 33.84000 13.37733 niyala elmtma #> 304 7 304 33.86844 13.41642 fthelrhman #> 306 7 306 33.88831 13.40231 bahtelreda #> locality d #> 722 AlDali and AlMazmoum 16.536039 #> 723 AlDali and AlMazmoum 16.131654 #> 724 AlDali and AlMazmoum 15.970387 #> 124 Al Dindir 80.022405 #> 126 Al Dindir 78.461575 #> 244 Al Dindir 76.867685 #> 18 Abu Hijar 19.586365 #> 19 Abu Hijar 11.542403 #> 731 Abu Hijar 11.201451 #> 257 AlSooki 8.831588 #> 258 AlSooki 8.199247 #> 259 AlSooki 7.792855 #> 499 Sennar 5.885795 #> 500 Sennar 5.449042 #> 501 Sennar 4.854442 #> 303 AlSooki 3.640581 #> 304 AlSooki 2.696516 #> 306 AlSooki 1.799006