I am trying to find the nearest distance of locations in dataset 1 to dataset 2. Both data sets are different sizes. Ive looked into using the Haversine function but I'm unsure what I need to do after.
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问题:
回答1:
Since you have not provided a sample of your data, I am going to use the oregon.tract
data set from the UScensus2000tract
library as a reproducible example.
Here is a solution based on fast data.table
that I get from this other answer here.
# load libraries
library(data.table)
library(geosphere)
library(UScensus2000tract)
library(rgeos)
Now let's create a new data.table
with all possible pair combinations of origins (census centroids) and destinations (facilities)
# get all combinations of origin and destination pairs
# Note that I'm considering here that the distance from A -> B is equal
from B -> A.
odmatrix <- CJ(Datatwo$Code_A , Dataone$Code_B)
names(odmatrix) <- c('Code_A', 'Code_B') # update names of columns
# add coordinates of Datatwo centroids (origin)
odmatrix[Datatwo, c('lat_orig', 'long_orig') := list(i.Latitude,
i.Longitude), on= "Code_A" ]
# add coordinates of facilities (destination)
odmatrix[Dataone, c('lat_dest', 'long_dest') := list(i.Latitude,
i.Longitude), on= "Code_B" ]
Now you just need to:
# calculate distances
odmatrix[ , dist := distHaversine(matrix(c(long_orig, lat_orig), ncol
= 2),
matrix(c(long_dest, lat_dest), ncol
= 2))]
# and get the nearest destinations for each origin
odmatrix[, .( Code_B = Code_B[which.min(dist)],
dist = min(dist)),
by = Code_A]
### Prepare data for this reproducible example
# load data
data("oregon.tract")
# get centroids as a data.frame
centroids <- as.data.frame(gCentroid(oregon.tract,byid=TRUE))
# Convert row names into first column
setDT(centroids, keep.rownames = TRUE)[]
# get two data.frames equivalent to your census and facility data
frames
Datatwo<- copy(centroids)
Dataone <- copy(centroids)
names(Datatwo) <- c('Code_A', 'Longitude', 'Latitude')
names(Dataone) <- c('Code_B', 'Longitude', 'Latitude')