Find how many points are within a certain radius of each other? (And display radius over each point on map?)

Question

I have two projects I'm working on where I need to do something like this. I have datasets of stores with lat and long coordinates and need to know, for each one, if there are any other stores within a 2 mile radius. For one of these projects, it would be especially useful if I could see the radius on a map. I have all the points mapped using mapview and would love if I could see a circle over each one so I can see if there are any gaps (new stores cannot be built within 2 miles of an existing store). For the other project, what I would like to get is a column showing the number of other stores in that radius. This one seems easier, though I haven't figured it out yet. I am new to R btw.

I've tried geodist and followed a tutorial to get a distance matrix, but I can't read it very well and I don't think it's what I need. I've also tried points_in_circle which did tell me which stores are within the radius, but I need it to do this for every pair of coordinates (I have 80 rows) and ideally give a more useable output.

Answer 1

For the second question (number of other stores within a certain radius), you can use the geodist function as you mentioned and then some data management to get your results.

The following commands will work on a small dataset of five stores. It contains the store ID with the lat and long coordinates.

n.stores <- 5
set.seed(1234)
stores <- cbind(sort(sample(1:10, n.stores)), 
                runif (n.stores, -0.1, 0.1), 
                runif (n.stores, -0.1, 0.1)); stores

colnames(stores) <- c("store_id", "lat", "long")
stores
#      store_id          lat        long
# [1,]        1  0.033216752  0.08468670
# [2,]        4  0.002850228 -0.04153683
# [3,]        5  0.038718258  0.06745913
# [4,]        6  0.008994967 -0.04275534
# [5,]       10 -0.043453283 -0.04663584

Now calculate the distances between each store and the others, including itself.

d <- geodist (stores[,2:3]) # A 5-by-5 matrix
d
#           [,1]       [,2]      [,3]       [,4]      [,5]
# [1,]     0.000 14427.8245  2009.804 14416.5478 16899.483
# [2,] 14427.825     0.0000 12752.057   696.1801  5176.953
# [3,]  2009.804 12752.0567     0.000 12686.0604 15625.871
# [4,] 14416.548   696.1801 12686.060     0.0000  5844.660
# [5,] 16899.483  5176.9527 15625.871  5844.6603     0.000

The distances are in metres. Convert this matrix to a data frame to facilitate the next step.

df <- as.data.frame(d)
colnames(df) <- stores[,'store_id']
df$store_A <- as.numeric(colnames(df))
#           1          4         5          6        10 store_A
# 1     0.000 14427.8245  2009.804 14416.5478 16899.483       1
# 2 14427.825     0.0000 12752.057   696.1801  5176.953       4
# 3  2009.804 12752.0567     0.000 12686.0604 15625.871       5
# 4 14416.548   696.1801 12686.060     0.0000  5844.660       6
# 5 16899.483  5176.9527 15625.871  5844.6603     0.000      10

Then you can reshape to long-form and list or count the number of stores within a certain distance of each other (say 10 km).

library(tidyr)
library(dplyr)

df.long <- pivot_longer(df, -store_A, 
                        names_to="store_B", 
                        values_to="distance", 
                        names_transform=as.numeric) %>%
  filter(store_A != store_B) # omit self-references

df.long
# A tibble: 10 × 3
#   store_A store_B distance
#     <dbl>   <dbl>    <dbl>
# 1       1       4   14428.
# 2       1       5    2010.
# 3       1       6   14417.
# 4       1      10   16899.
# 5       4       1   14428.
# 6       4       5   12752.
# 7       4       6     696.
# 8       4      10    5177.
# 9       5       1    2010.
#10       5       4   12752.
#11       5       6   12686.
#12       5      10   15626.
#13       6       1   14417.
#14       6       4     696.
#15       6       5   12686.
#16       6      10    5845.
#17      10       1   16899.
#18      10       4    5177.
#19      10       5   15626.
#20      10       6    5845.

Now the group counts.

group_by(df.long, store_A) %>%
  filter(distance<10000) %>%
  print() %>%
  summarise(n=n())

# A tibble: 8 × 3
# Groups:   store_A [4]
#   store_A store_B distance
#     <dbl>   <dbl>    <dbl>
# 1       1       5    2010.
# 2       4       6     696.
# 3       4      10    5177.
# 4       5       1    2010.
# 5       6       4     696.
# 6       6      10    5845.
# 7      10       4    5177.
# 8      10       6    5845.

# A tibble: 5 × 2
#   store_A     n
#     <dbl> <int>
# 1       1     1
# 2       4     2
# 3       5     1
# 4       6     2
# 5      10     2

So, store "1" has 1 store within 10 km, store "4" has 2, store "5" has 1, and stores "6" and "10" have two each.