Put a radius around locations and identify other locations within that radius

Here is some code to reproduce a data.frame containing coordinates:

x <- c(611547.6411, 589547.6411, 611447.6411, 609847.6411, 606347.6411, 611447.6411, 613547.6411,642747.6411, 589647.6411, 606447.6411, 613547.6411, 640347.6411, 642847.6411, 612147.6411, 613847.6411, 640247.6411, 642947.6411, 584347.6411, 587747.6411, 606447.6411, 614247.6411, 640447.6411, 642747.6411, 584447.6411, 608647.6411, 612047.6411, 612747.6411,
613847.6411, 643147.6411, 583147.6411, 608747.6411, 611847.6411, 609647.6411, 610047.6411, 613747.6411, 586247.6411, 588647.6411, 643147.6411, 584347.6411, 606447.6411, 610147.6411, 613347.6411, 614647.6411, 586047.6411, 587247.6411, 611547.6411, 640347.6411, 643147.6411, 587147.6411, 583047.6411, 608747.6411, 612047.6411, 613947.6411, 587647.6411, 588547.6411, 586847.6411, 611247.6411, 643247.6411, 587247.6411, 590347.6411, 582947.6411, 608947.6411, 611847.6411, 613447.6411, 614647.6411, 585147.6411, 587647.6411, 588547.6411, 586947.6411, 611247.6411, 643047.6411, 587147.6411, 583947.6411, 587747.6411, 608547.6411, 611747.6411, 614047.6411, 585247.6411, 586247.6411, 588447.6411, 589147.6411, 611347.6411, 642447.6411, 586947.6411, 585847.6411, 587747.6411, 581447.6411, 612447.6411, 611947.6411, 600547.6411,
612047.6411, 610347.6411, 614147.6411, 582847.6411, 588547.6411, 589247.6411, 611247.6411, 638147.6411, 640547.6411, 642947.6411, 587047.6411, 585947.6411, 587647.6411, 600447.6411, 611347.6411, 612347.6411, 610347.6411, 587747.6411, 579747.6411, 583847.6411, 586847.6411, 588447.6411, 589347.6411, 643347.6411, 589347.6411, 586947.6411, 588247.6411, 588847.6411, 585847.6411, 590847.6411, 589447.6411, 590947.6411, 581347.6411, 611847.6411, 600647.6411, 610347.6411, 615947.6411, 613947.6411, 586347.6411, 579647.6411, 584047.6411, 586347.6411, 587747.6411, 587947.6411, 586547.6411, 587647.6411, 614047.6411, 643047.6411, 587947.6411, 585747.6411, 584947.6411, 600547.6411, 611947.6411, 606847.6411, 600847.6411, 612847.6411, 615747.6411, 620747.6411, 614047.6411, 632947.6411, 588147.6411, 579747.6411, 582747.6411)

y <- c(5272140.5728, 5271740.5728, 5271640.5728, 5267440.5728, 5271540.5728, 5272040.5728, 5272340.5728, 5268540.5728, 5271240.5728, 5271640.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5277940.5728, 5278040.5728, 5278040.5728, 5266940.5728, 5267040.5728, 5267440.5728, 5268140.5728, 5268640.5728, 5271140.5728, 5271740.5728, 5271740.5728, 5271940.5728, 5272140.5728, 5272240.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272340.5728, 5277240.5728, 5278040.5728, 5268540.5728, 5271240.5728, 5271340.5728, 5272240.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728,
5272140.5728, 5272240.5728, 5272240.5728, 5277240.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5268540.5728, 5272240.5728, 5272340.5728, 5272040.5728, 5272040.5728, 5277340.5728, 5278140.5728, 5278140.5728, 5265640.5728, 5266840.5728, 5267240.5728, 5268440.5728, 5271540.5728, 5272140.5728, 5271840.5728, 5271940.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272340.5728, 5277140.5728, 5277240.5728, 5277340.5728, 5277740.5728, 5277740.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5278240.5728, 5278240.5728, 5264940.5728, 5265040.5728, 5265140.5728, 5266740.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5267140.5728, 5267340.5728, 5267440.5728, 5268340.5728,
5271240.5728, 5271840.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272340.5728, 5271840.5728, 5271840.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5272340.5728, 5274340.5728, 5274440.5728, 5274640.5728, 5285140.5728, 5285240.5728, 5277340.5728, 5277540.5728, 5277840.5728, 5278040.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5265640.5728, 5266740.5728, 5266740.5728, 5266940.5728, 5268340.5728, 5268440.5728, 5271440.5728, 5271540.5728, 5271540.5728, 5271740.5728, 5272040.5728, 5272340.5728, 5271740.5728, 5272240.5728, 5272240.5728, 5274540.5728, 5275040.5728, 5275340.5728, 5284840.5728, 5284940.5728, 5284940.5728, 5285040.5728, 5285040.5728)

coordinates.df <- as.data.frame(cbind(x,y))

# add an ID column that might be helpful and rearrange the columns
coordinates.df$ID <- 1:nrow(coordinates.df)
coordinates.df <- coordinates.df[c(3,1:2)]

X and Y coordinates are included. They correspond to projected coordinates in meters. Each row represents a location provided with an ID. Now there are two things that should be done:

• put a radius of 500 m around each of the locations
• check if there are intersections between the resulting circles and display these cases

The goal is to have as much locations as possible without intersections at the end. Locations that lie within a circle are supposed to be removed. Lower location IDs are to be preferred. This means for example that if ID2 and ID14 intersect, ID14 shall be removed.

EDIT after the answer below from VinceGreg. After performing the steps, the resulting plot from this

# Graphical
df_sf %>% st_buffer( dist = 500)  %>% 
  ggplot()  + geom_sf()+ geom_sf(data= df_sf2)

looks like this:

using

# Graphical
df_sf2 %>% st_buffer( dist = 500)  %>% 
  ggplot()  + geom_sf()+ geom_sf(data= df_sf2)

leads to this:

In both cases (I think the second one is the proper one with df_sf2) there are still some overlaps/intersections between some circles visible.

Second attempt I used a while loop to remove one of the closest point of the distance matrice at every iteration; until a distance of 1000m is observed. That way, two circles of 500.001 m of radius won't touch. My PC took 2 seconds for this dataset.

If 3 or more points are close togheter, the closest pair is selectionned; and the highest ID is removed. The process is then repeated.

library(sf)
library(tidyverse)

x <- c(611547.6411, 589547.6411, 611447.6411, 609847.6411, 606347.6411, 611447.6411, 613547.6411,642747.6411, 589647.6411, 606447.6411, 613547.6411, 640347.6411, 642847.6411, 612147.6411, 613847.6411, 640247.6411, 642947.6411, 584347.6411, 587747.6411, 606447.6411, 614247.6411, 640447.6411, 642747.6411, 584447.6411, 608647.6411, 612047.6411, 612747.6411,
       613847.6411, 643147.6411, 583147.6411, 608747.6411, 611847.6411, 609647.6411, 610047.6411, 613747.6411, 586247.6411, 588647.6411, 643147.6411, 584347.6411, 606447.6411, 610147.6411, 613347.6411, 614647.6411, 586047.6411, 587247.6411, 611547.6411, 640347.6411, 643147.6411, 587147.6411, 583047.6411, 608747.6411, 612047.6411, 613947.6411, 587647.6411, 588547.6411, 586847.6411, 611247.6411, 643247.6411, 587247.6411, 590347.6411, 582947.6411, 608947.6411, 611847.6411, 613447.6411, 614647.6411, 585147.6411, 587647.6411, 588547.6411, 586947.6411, 611247.6411, 643047.6411, 587147.6411, 583947.6411, 587747.6411, 608547.6411, 611747.6411, 614047.6411, 585247.6411, 586247.6411, 588447.6411, 589147.6411, 611347.6411, 642447.6411, 586947.6411, 585847.6411, 587747.6411, 581447.6411, 612447.6411, 611947.6411, 600547.6411,
       612047.6411, 610347.6411, 614147.6411, 582847.6411, 588547.6411, 589247.6411, 611247.6411, 638147.6411, 640547.6411, 642947.6411, 587047.6411, 585947.6411, 587647.6411, 600447.6411, 611347.6411, 612347.6411, 610347.6411, 587747.6411, 579747.6411, 583847.6411, 586847.6411, 588447.6411, 589347.6411, 643347.6411, 589347.6411, 586947.6411, 588247.6411, 588847.6411, 585847.6411, 590847.6411, 589447.6411, 590947.6411, 581347.6411, 611847.6411, 600647.6411, 610347.6411, 615947.6411, 613947.6411, 586347.6411, 579647.6411, 584047.6411, 586347.6411, 587747.6411, 587947.6411, 586547.6411, 587647.6411, 614047.6411, 643047.6411, 587947.6411, 585747.6411, 584947.6411, 600547.6411, 611947.6411, 606847.6411, 600847.6411, 612847.6411, 615747.6411, 620747.6411, 614047.6411, 632947.6411, 588147.6411, 579747.6411, 582747.6411)

y <- c(5272140.5728, 5271740.5728, 5271640.5728, 5267440.5728, 5271540.5728, 5272040.5728, 5272340.5728, 5268540.5728, 5271240.5728, 5271640.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5277940.5728, 5278040.5728, 5278040.5728, 5266940.5728, 5267040.5728, 5267440.5728, 5268140.5728, 5268640.5728, 5271140.5728, 5271740.5728, 5271740.5728, 5271940.5728, 5272140.5728, 5272240.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272340.5728, 5277240.5728, 5278040.5728, 5268540.5728, 5271240.5728, 5271340.5728, 5272240.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728,
       5272140.5728, 5272240.5728, 5272240.5728, 5277240.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5268540.5728, 5272240.5728, 5272340.5728, 5272040.5728, 5272040.5728, 5277340.5728, 5278140.5728, 5278140.5728, 5265640.5728, 5266840.5728, 5267240.5728, 5268440.5728, 5271540.5728, 5272140.5728, 5271840.5728, 5271940.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272340.5728, 5277140.5728, 5277240.5728, 5277340.5728, 5277740.5728, 5277740.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5278240.5728, 5278240.5728, 5264940.5728, 5265040.5728, 5265140.5728, 5266740.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5267140.5728, 5267340.5728, 5267440.5728, 5268340.5728,
       5271240.5728, 5271840.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272340.5728, 5271840.5728, 5271840.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5272340.5728, 5274340.5728, 5274440.5728, 5274640.5728, 5285140.5728, 5285240.5728, 5277340.5728, 5277540.5728, 5277840.5728, 5278040.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5265640.5728, 5266740.5728, 5266740.5728, 5266940.5728, 5268340.5728, 5268440.5728, 5271440.5728, 5271540.5728, 5271540.5728, 5271740.5728, 5272040.5728, 5272340.5728, 5271740.5728, 5272240.5728, 5272240.5728, 5274540.5728, 5275040.5728, 5275340.5728, 5284840.5728, 5284940.5728, 5284940.5728, 5285040.5728, 5285040.5728)

coordinates.df <- as.data.frame(cbind(x,y))

# add an ID column that might be helpful and rearrange the columns
coordinates.df$ID <- 1:nrow(coordinates.df)
coordinates.df <- coordinates.df[c(3,1:2)]

# Making a sf object
df_sf = coordinates.df %>% st_as_sf(coords=c("x","y"), remove= FALSE)# %>% 

min_dist = 1

while(min_dist < 1000){
  
  x = st_distance(df_sf) %>% as_tibble()
  colnames(x)= df_sf$ID
  
  # Getting the pair of closest poiunt
  x= x %>%  mutate(id0 = df_sf$ID) %>% 
    pivot_longer(cols = -id0 , names_to= "id1", values_to = "dist") %>%
    mutate(id1 = as.numeric(id1)) %>% 
    filter(dist!=0) %>% 
    slice_min(dist, with_ties = FALSE)
  
  # Extracting the distance for the WHILE criterion
  min_dist = x$dist
  
  # Getting the biggest ID to remove
  x_remove = max(x$id0, x$id1)
  
 # Removing the ID. The IF statement might be usefull to avoid an extra removal
  if(min_dist < 1000) {
    df_sf = df_sf %>% filter(ID != x_remove)
  }
  
}

# Plot (with buffer)
df_sf %>% st_buffer( dist = 500)  %>% 
  ggplot()  + geom_sf()+ geom_sf(data= df_sf) +
  geom_sf_text( aes(label= ID))

Previous attempt : this solution won't work if there is 3 or more points nearby. Ex. Point B could be selected from circle C; and A and B would be too close.

Here is a solution with sf and tidyverse. I created circles around each points (radius of 500 m ). For each circles, I checked which points (index) are inside this cricle, and took the minimal ID. After removing duplicated values, I selected the points corresponding to those indexes.

library(sf)
library(tidyverse)

x <- c(611547.6411, 589547.6411, 611447.6411, 609847.6411, 606347.6411, 611447.6411, 613547.6411,642747.6411, 589647.6411, 606447.6411, 613547.6411, 640347.6411, 642847.6411, 612147.6411, 613847.6411, 640247.6411, 642947.6411, 584347.6411, 587747.6411, 606447.6411, 614247.6411, 640447.6411, 642747.6411, 584447.6411, 608647.6411, 612047.6411, 612747.6411,
       613847.6411, 643147.6411, 583147.6411, 608747.6411, 611847.6411, 609647.6411, 610047.6411, 613747.6411, 586247.6411, 588647.6411, 643147.6411, 584347.6411, 606447.6411, 610147.6411, 613347.6411, 614647.6411, 586047.6411, 587247.6411, 611547.6411, 640347.6411, 643147.6411, 587147.6411, 583047.6411, 608747.6411, 612047.6411, 613947.6411, 587647.6411, 588547.6411, 586847.6411, 611247.6411, 643247.6411, 587247.6411, 590347.6411, 582947.6411, 608947.6411, 611847.6411, 613447.6411, 614647.6411, 585147.6411, 587647.6411, 588547.6411, 586947.6411, 611247.6411, 643047.6411, 587147.6411, 583947.6411, 587747.6411, 608547.6411, 611747.6411, 614047.6411, 585247.6411, 586247.6411, 588447.6411, 589147.6411, 611347.6411, 642447.6411, 586947.6411, 585847.6411, 587747.6411, 581447.6411, 612447.6411, 611947.6411, 600547.6411,
       612047.6411, 610347.6411, 614147.6411, 582847.6411, 588547.6411, 589247.6411, 611247.6411, 638147.6411, 640547.6411, 642947.6411, 587047.6411, 585947.6411, 587647.6411, 600447.6411, 611347.6411, 612347.6411, 610347.6411, 587747.6411, 579747.6411, 583847.6411, 586847.6411, 588447.6411, 589347.6411, 643347.6411, 589347.6411, 586947.6411, 588247.6411, 588847.6411, 585847.6411, 590847.6411, 589447.6411, 590947.6411, 581347.6411, 611847.6411, 600647.6411, 610347.6411, 615947.6411, 613947.6411, 586347.6411, 579647.6411, 584047.6411, 586347.6411, 587747.6411, 587947.6411, 586547.6411, 587647.6411, 614047.6411, 643047.6411, 587947.6411, 585747.6411, 584947.6411, 600547.6411, 611947.6411, 606847.6411, 600847.6411, 612847.6411, 615747.6411, 620747.6411, 614047.6411, 632947.6411, 588147.6411, 579747.6411, 582747.6411)

y <- c(5272140.5728, 5271740.5728, 5271640.5728, 5267440.5728, 5271540.5728, 5272040.5728, 5272340.5728, 5268540.5728, 5271240.5728, 5271640.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5277940.5728, 5278040.5728, 5278040.5728, 5266940.5728, 5267040.5728, 5267440.5728, 5268140.5728, 5268640.5728, 5271140.5728, 5271740.5728, 5271740.5728, 5271940.5728, 5272140.5728, 5272240.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272340.5728, 5277240.5728, 5278040.5728, 5268540.5728, 5271240.5728, 5271340.5728, 5272240.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728,
       5272140.5728, 5272240.5728, 5272240.5728, 5277240.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5268540.5728, 5272240.5728, 5272340.5728, 5272040.5728, 5272040.5728, 5277340.5728, 5278140.5728, 5278140.5728, 5265640.5728, 5266840.5728, 5267240.5728, 5268440.5728, 5271540.5728, 5272140.5728, 5271840.5728, 5271940.5728, 5271940.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272140.5728, 5272140.5728, 5272140.5728, 5272340.5728, 5277140.5728, 5277240.5728, 5277340.5728, 5277740.5728, 5277740.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5278240.5728, 5278240.5728, 5264940.5728, 5265040.5728, 5265140.5728, 5266740.5728, 5266840.5728, 5266940.5728, 5267040.5728, 5267140.5728, 5267340.5728, 5267440.5728, 5268340.5728,
       5271240.5728, 5271840.5728, 5271940.5728, 5272040.5728, 5272040.5728, 5272340.5728, 5271840.5728, 5271840.5728, 5272140.5728, 5272140.5728, 5272240.5728, 5272240.5728, 5272340.5728, 5274340.5728, 5274440.5728, 5274640.5728, 5285140.5728, 5285240.5728, 5277340.5728, 5277540.5728, 5277840.5728, 5278040.5728, 5278040.5728, 5278140.5728, 5278140.5728, 5265540.5728, 5265640.5728, 5266740.5728, 5266740.5728, 5266940.5728, 5268340.5728, 5268440.5728, 5271440.5728, 5271540.5728, 5271540.5728, 5271740.5728, 5272040.5728, 5272340.5728, 5271740.5728, 5272240.5728, 5272240.5728, 5274540.5728, 5275040.5728, 5275340.5728, 5284840.5728, 5284940.5728, 5284940.5728, 5285040.5728, 5285040.5728)

coordinates.df <- as.data.frame(cbind(x,y))

# add an ID column that might be helpful and rearrange the columns
coordinates.df$ID <- 1:nrow(coordinates.df)
coordinates.df <- coordinates.df[c(3,1:2)]

# Making a sf object
df_sf = coordinates.df %>% st_as_sf(coords=c("x","y"), remove= FALSE)# %>% 

# Creating n polygons (circle) around each point, 
# checking which circles intercepts, and keeping the first value (minimum ID).
index_to_keep = st_intersects(
  df_sf %>% st_buffer( dist = 500),
                              df_sf, sparse=TRUE )  %>%
  map(min) %>% unlist() %>% 
  # Removing duplicates
  unique()

# Selecting the index with only one point
df_sf2 =  df_sf[index_to_keep, ]

# Graphical
df_sf %>% st_buffer( dist = 500)  %>% 
  ggplot()  + geom_sf()+ geom_sf(data= df_sf2)