I have two sets of coordinate data (lat and long). The first set denotes positions of restaurants within the city. The second set of coordinates represents parks in the city. For each restaurant, I want to calculate the number of parks that are within 160 meters.
I tried using the simple features (sf) package to perform this operation, per another post, but am not getting the output I want.
Sample data:
Restaurants:
45.59841 -122.3275
45.59932 -122.3343
45.59932 -122.3343
45.59820 -122.3343
45.59820 -122.3343
45.59814 -122.3340
45.59814 -122.3340
45.60016 -122.3341
45.60016 -122.3341
45.60020 -122.3343
Parks:
45.61221 -122.3426
45.61065 -122.3402
45.60868 -122.3384
45.60624 -122.3361
45.61082 -122.3388
45.60983 -122.3379
45.60910 -122.3372
45.60829 -122.3365
45.60653 -122.3349
45.60503 -122.3335
Using sf, I tried to perform the following operations:
library(tidyverse)
library(sf)
park_points <- st_as_sf(ride_coords, coords = c(x = "Latitude", y = "Longitude"), crs = 4326)
rest_points <- st_as_sf(can_coords, coords = c(x = "lat", "lon"), crs = 4326)
I then tried to create a 160m buffer around each restaurant, after which I was hoping to attempt to calculate the number of parks residing in each of the restaurant buffers.
buffer_160 <- park_points %>% st_transform(26910) %>% st_buffer(dist = 160) %>% st_transform(2926)
I then received the following error:
"Error in UseMethod("st_transform") : no applicable method for 'st_transform' applied to an object of class "data.frame"
Anyway, the goal is to create buffers around each restaurant, then count the number of parks within each buffer (I know there will be overlap, so I will have to figure out how to handle this too)
I'm very new with R spatial tools. Thanks for any help!
You could use spatialrisk::points_in_circle and map over the restaurants with purrr.
Didn't find parks in a radius of 160 m, so extended radius to 1000 m :
Restaurants <- read.table(text="
lat lon
45.59841 -122.3275
45.59932 -122.3343
45.59932 -122.3343
45.59820 -122.3343
45.59820 -122.3343
45.59814 -122.3340
45.59814 -122.3340
45.60016 -122.3341
45.60016 -122.3341
45.60020 -122.3343",header = T)
Parks <- read.table(text = "
lat lon
45.61221 -122.3426
45.61065 -122.3402
45.60868 -122.3384
45.60624 -122.3361
45.61082 -122.3388
45.60983 -122.3379
45.60910 -122.3372
45.60829 -122.3365
45.60653 -122.3349
45.60503 -122.3335",header = T)
library(purrr)
library(spatialrisk)
Restaurants %>% pmap(~with(list(...),points_in_circle(Parks, lon_center = lon, lat_center = lat,radius = 1000)))
#> [[1]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 872.6082
#>
#> [[2]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 638.6807
#> 4 45.60624 -122.3361 782.9830
#> 9 45.60653 -122.3349 803.9727
#>
#> [[3]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 638.6807
#> 4 45.60624 -122.3361 782.9830
#> 9 45.60653 -122.3349 803.9727
#>
#> [[4]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 762.8609
#> 4 45.60624 -122.3361 905.9215
#> 9 45.60653 -122.3349 928.4681
#>
#> [[5]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 762.8609
#> 4 45.60624 -122.3361 905.9215
#> 9 45.60653 -122.3349 928.4681
#>
#> [[6]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 767.9792
#> 4 45.60624 -122.3361 916.4012
#> 9 45.60653 -122.3349 936.5971
#>
#> [[7]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 767.9792
#> 4 45.60624 -122.3361 916.4012
#> 9 45.60653 -122.3349 936.5971
#>
#> [[8]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 544.1362
#> 4 45.60624 -122.3361 694.5149
#> 9 45.60653 -122.3349 711.8371
#> 8 45.60829 -122.3365 924.1272
#>
#> [[9]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 544.1362
#> 4 45.60624 -122.3361 694.5149
#> 9 45.60653 -122.3349 711.8371
#> 8 45.60829 -122.3365 924.1272
#>
#> [[10]]
#> lat lon distance_m
#> 10 45.60503 -122.3335 541.2711
#> 4 45.60624 -122.3361 686.8285
#> 9 45.60653 -122.3349 706.2001
#> 8 45.60829 -122.3365 916.7284
#> 3 45.60868 -122.3384 996.5307
Or to summarize, use concentration which directly gives the number of parks around each restaurant:
Parks$count <- 1
spatialrisk::concentration(Restaurants, Parks, value = count, radius = 1000)
lat lon amount concentration
1 45.59841 -122.3275 1 1
2 45.59932 -122.3343 1 3
3 45.59932 -122.3343 1 3
4 45.59820 -122.3343 1 3
5 45.59820 -122.3343 1 3
6 45.59814 -122.3340 1 3
7 45.59814 -122.3340 1 3
8 45.60016 -122.3341 1 4
9 45.60016 -122.3341 1 4
10 45.60020 -122.3343 1 5