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Find the nearest latitude and longitude points from different data sets of different lengths

I have two datasets from different stations. Data is basically data.frames with coordinates, longitudes and latitudes. Given the first data set (or vice versa), I want to find the nearest station for each station in a different data set. My main problem here is that the coordinates are not ordered and that the data sets have different lengths. For example, the first contains 2228 stations, and the second - 1782. Thus, I do not know how to deal with this. I know about the rdist.earth function, and I tried to use it. This is a short example:

      #First data set of stations
        set1 <- structure(list(lon = c(13.671114, 12.866947, 15.94223, 11.099736,  
         12.958342, 14.203892, 11.86389, 16.526674, 16.193064, 17.071392
        ), lat = c(48.39167, 48.148056, 48.721111, 47.189167, 47.054443, 
         47.129166, 47.306667, 47.84, 47.304167, 48.109444)), .Names = c("lon", 
       "lat"), row.names = c(NA, 10L), class = "data.frame")

      #Second data set
      set2 <- structure(list(lon = structure(c(14.4829998016357, 32.4000015258789, 
      -8.66600036621094, 15.4670000076294, 18.9160003662109, 19.0160007476807, 
      31.0990009307861, 14.3660001754761, 9.59899997711182, 11.0830001831055
       ), .Dim = 10L), lat = structure(c(35.8499984741211, 34.75, 70.9329986572266, 
      78.25, 69.6829986572266, 74.515998840332, 70.3659973144531, 67.265998840332, 
       63.6990013122559, 60.1990013122559), .Dim = 10L)), .Names = c("lon", 
      "lat"), row.names = c(NA, 10L), class = "data.frame")
       #computing distance
       dd<- rdist.earth(set1,set2,miles=FALSE)

dd ... , . , , 1, , ? ?

.

+4
4

:

library(rgeos)
set1sp <- SpatialPoints(set1)
set2sp <- SpatialPoints(set2)
set1$nearest_in_set2 <- apply(gDistance(set1sp, set2sp, byid=TRUE), 1, which.min)

head(set1)
       lon      lat nearest_in_set2
## 1 13.67111 48.39167              10
## 2 12.86695 48.14806              10
## 3 15.94223 48.72111              10
## 4 11.09974 47.18917               1
## 5 12.95834 47.05444               1
## 6 14.20389 47.12917               1
+11

. , x y set1 set2, -- - ​​-- p1 p2. [. , set1 set2 - , set2, 1 )

distp1p2 <- function(p1,p2) {
    dst <- sqrt((p1[1]-p2[1])^2+(p1[2]-p2[2])^2)
    return(dst)
}

dist2 <- function(y) min(apply(set2, 1, function(x) min(distp1p2(x,y))))

apply(set1, 1, dist2)

, , , min, .min dist2()

dist2b <- function(y) which.min(apply(set2, 1, function(x) min(distp1p2(x,y))))
apply(set1, 1, dist2b)

lat-lon

set2[apply(set1, 1, dist2b),]
+3

, , . "ann" "yaImpute" , . , , " " , ( k), .

: , , . - . . .

FWIW, I'm really not joking fast. I used this to find knn distances for two matrices, each with millions of points. Creating a distance matrix for this or iteratively building line by line is either not feasible or painfully slow.

Quick example:

# Hypothetical coordinate data
set.seed(2187); foo1 <- round(abs(data.frame(x=runif(5), y=runif(5))*100))
set.seed(2187); foo2 <- round(abs(data.frame(x=runif(10), y=runif(10))*100))
foo1; foo2

# the 'ann' command from the 'yaImpute' package
install.packages("yaImpute")
library(yaImpute)

# Approximate nearest-neighbour search, reporting 2 nearest points (k=2)
# This command finds the 3 nearest points in foo2 for each point in foo1
# In the output:
#   The first k columns are the row numbers of the points
#   The next k columns (k+1:2k) are the *squared* euclidean distances
knn.out <- ann(as.matrix(foo2), as.matrix(foo1), k=3)
knn.out$knnIndexDist

     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    5    4  729 1658 2213
[2,]    2    3    7   16  100 1025
[3,]    9    7    5   40   81  740
[4,]    4    1    6   16  580  673
[5,]    5    7    9    0  677  980

https://cran.r-project.org/web/packages/yaImpute/index.html

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I don’t know exactly what you want, but maybe this gives you some tips if you want to get the min value for each column

  dd <- as.data.frame(dd)
  sapply(dd, min)
  paste(rownames(dd), ":", apply(dd,2,which.min)) #or
0
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