How do I calculate Euclidean distance in km from a spatial point that has been converted from a geometry column into a data frame. (The points are points which were derived from a spatial join of spatial data and polygon centroids)
I tried data_sample <- data_sample %>% mutate(distance= distm(cbind(origin_y,origin_x), cbind(dest_y,dest_x),fun = distHaversine)/1000)
and the error i'm getting is Error in .pointsToMatrix(x) : longitude > 360
Below is a sample data frame
data_sample <- data.frame(
origin_x = c(
623613.87,
625678.02,
625678.02,
624359.91,
628136.40,
628136.40,
628136.40,
628136.40,
632329.70
),
origin_y = c(
6438093.66,
6455468.02,
6455468.02,
6449819.06,
6462017.42,
6462017.42,
6462017.42,
6462017.42,
6446947.75
),
dest_x = c(
659627.84,
642136.20,
642136.20,
630395.03,
628422.74,
642136.20,
642136.20,
659627.84,
659627.84
),
dest_y = c(
6473200.36,
6456562.78,
6456562.78,
6451979.98,
6459817.02,
6456562.78,
6456562.78,
6473200.36,
6473200.36)
)
Is there any reason not to use the sf builtin function st_distance??
library(sf)
# Using your data, but separate data.frames
origin_df <- data.frame(
origin_x = c(623613.87,
625678.02,
625678.02,
624359.91,
628136.40,
628136.40,
628136.40,
628136.40,
632329.70
),
origin_y = c(
6438093.66,
6455468.02,
6455468.02,
6449819.06,
6462017.42,
6462017.42,
6462017.42,
6462017.42,
6446947.75)
)
dest_df <- data.frame(
dest_x = c(
659627.84,
642136.20,
642136.20,
630395.03,
628422.74,
642136.20,
642136.20,
659627.84,
659627.84
),
dest_y = c(
6473200.36,
6456562.78,
6456562.78,
6451979.98,
6459817.02,
6456562.78,
6456562.78,
6473200.36,
6473200.36)
)
# Now convert to sf objects
# Just guessing at the CRS. Replace with the correct one
origin_sf = st_as_sf(origin_df, coords=c("origin_x", "origin_y"), crs="EPSG:32632")
dest_sf <- st_as_sf(dest_df, coords=c("dest_x", "dest_y"), crs="EPSG:32632")
# Now get the distance matrix, (will be in meters, since the CRS units are meters
dist_matrix <- st_distance(origin_sf, dest_sf)
# Dispaly in kilometers
(dist_matrix)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 50.29400 26.15693 26.15693 15.453608 22.249261 26.15693 26.15693 50.29400
[2,] 38.30178 16.49455 16.49455 5.866567 5.142693 16.49455 16.49455 38.30178
[3,] 38.30178 16.49455 16.49455 5.866567 5.142693 16.49455 16.49455 38.30178
[4,] 42.31444 19.01248 19.01248 6.410324 10.791932 19.01248 19.01248 42.31444
[5,] 33.41809 15.02490 15.02490 10.288421 2.218953 15.02490 15.02490 33.41809
[6,] 33.41809 15.02490 15.02490 10.288421 2.218953 15.02490 15.02490 33.41809
[7,] 33.41809 15.02490 15.02490 10.288421 2.218953 15.02490 15.02490 33.41809
[8,] 33.41809 15.02490 15.02490 10.288421 2.218953 15.02490 15.02490 33.41809
[9,] 37.87331 13.73376 13.73376 5.391316 13.449255 13.73376 13.73376 37.87331
[,9]
[1,] 50.29400
[2,] 38.30178
[3,] 38.30178
[4,] 42.31444
[5,] 33.41809
[6,] 33.41809
[7,] 33.41809
[8,] 33.41809
[9,] 37.87331
And responding to the additional comment from william-g-k:
dist_km = dist_matrix / 1000
dist_km = as.data.frame(matrix(dist_km, nrow=1))