Grouping polygons based on clusters

Question

I have a polygon sf and all I want is to get the polygons grouped based on the clusters they belong to - ideally, the cluster grouping should be added as one more attribute to each polygon. The definition of cluster here is any polygon that shares an edge or a vertex with any neighbouring polygon will be in the same cluster.

My attemp with using poly2ng is as follows

tiles_cluster <- poly2nb(tiles)
coords <- st_coordinates(st_centroid(st_geometry(tiles)))
plot(st_geometry(tiles), border="grey")
plot(tiles_cluster, coords, col="grey", add = TRUE)

But I don't know how to extract the clusters from the output.

I would also like to know if I can cluster them using a threshold distance meaning even if the polygons are discountinous if they are within the threshold distance they should be clustered together.

tiles 

    structure(list(x = c(15L, 14L, 15L, 16L, 14L, 15L, 16L, 17L, 
13L, 14L, 15L, 16L, 17L, 18L, 12L, 13L, 14L, 15L, 16L, 17L, 11L, 
12L, 13L, 14L, 15L, 16L, 11L, 13L, 14L, 15L, 16L, 13L, 14L, 15L, 
13L, 14L, 11L, 8L, 9L, 10L, 11L, 12L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 6L, 7L, 11L, 12L, 13L, 7L, 8L, 12L, 13L, 8L, 14L, 8L, 15L, 
8L, 16L, 7L, 15L, 14L, 15L, 16L, 14L, 15L, 16L, 17L, 13L, 14L
), y = c(2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
8L, 8L, 9L, 9L, 9L, 10L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 
15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 20L, 2L, 3L, 3L, 3L, 
4L, 4L, 4L, 4L, 5L, 5L), haz = c(0.226500004529953, 0.226899996399879, 
0.268900007009506, 0.342599987983704, 0.235200002789497, 0.342099994421005, 
0.990299999713898, 0.580600023269653, 0.228200003504753, 0.324699997901917, 
1.32990002632141, 1.70360004901886, 0.628899991512299, 0.199000000953674, 
0.153699994087219, 0.25450000166893, 0.156499996781349, 0.79720002412796, 
0.8367999792099, 0.196700006723404, 0.109300002455711, 0.209600001573563, 
0.174199998378754, 0.20440000295639, 0.359400004148483, 0.286500006914139, 
0.143500000238419, 0.233400002121925, 0.32480001449585, 0.359800010919571, 
0.164100006222725, 0.233500003814697, 0.347799986600876, 0.3125, 
0.236000001430511, 0.267500013113022, 0.107600003480911, 0.267500013113022, 
0.602299988269806, 0.527700006961823, 0.329199999570847, 0.200399994850159, 
0.205300003290176, 0.110699996352196, 0.5242999792099, 1.16700005531311, 
0.865100026130676, 0.370400011539459, 0.184400007128716, 0.211600005626678, 
0.214300006628036, 0.620500028133392, 0.561200022697449, 0.364499986171722, 
0.145699992775917, 0.177100002765656, 0.207599997520447, 0.213899999856949, 
0.196600005030632, 0.439900010824203, 0.288800001144409, 0.311500012874603, 
0.206900000572205, 0.10419999808073, 0.137799993157387, 0.226500004529953, 
0.226899996399879, 0.268900007009506, 0.342599987983704, 0.235200002789497, 
0.342099994421005, 0.990299999713898, 0.580600023269653, 0.228200003504753, 
0.324699997901917), geom = structure(list(structure(list(structure(c(384875, 
384880, 384880, 384875, 384875, 205270, 205270, 205265, 205265, 
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    384885, 384880, 384880, 205260, 205260, 205255, 205255, 205260
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    structure(list(structure(c(384885, 384890, 384890, 384885, 
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    ), dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg")), 
    structure(list(structure(c(384840, 384845, 384845, 384840, 
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y = NA_integer_, haz = NA_integer_), class = "factor", levels = c("constant", 
"aggregate", "identity")))

Dissolve will convert them as single polygon per cluster I think. What I want is more complicated than that. — rm167, Commented Dec 14, 2022 at 13:10

Bera · Accepted Answer · 2022-12-14 19:21:18Z

Try this:

require(sf)

shape <- read_sf(dsn = "/home/bera/Desktop/GIStest/grid_R.shp")
geometries <- st_cast(st_union(st_buffer(shape, 0.1)), "POLYGON") #Buffer to make the corners touch, union to dissolve adjacent borders
dissolved <- st_sf(geometries) #Create a sf object from the geometries
dissolved$clusterID=1:length(geometries) #Add cluster id to each row
result <- st_join(shape, dissolved) #Join cluster id to the original polygons

st_write(result, "/home/bera/Desktop/GIStest/grid_R_dissolved.shp")

dimfalk · Accepted Answer · 2022-12-17 15:45:33Z

2

I'd like to show a different approach using {terra}, since this is easier to solve with raster data from my point of view:

library(terra)
#> terra 1.6.49

tiles <- vect(tiles)

# extract some attributes for next step
e <- ext(tiles) 
ref <- crs(tiles)

# create the `y` attribute for `rasterize()`
r <- rast(extent = e, crs = ref, resolution = 5)

r2 <- rasterize(tiles, r, field = "haz")

# determine patches using Queen's case
patches <- patches(r2, directions = 8)

# inspect
plot(patches)

^{Created on 2022-12-17 with reprex v2.0.2}

answered Dec 17, 2022 at 15:45

dimfalk

1,4191 gold badge7 silver badges17 bronze badges

Thanks. This is a helpful answer, but I prefer BERA's answer as doesn't change the underlying data format
– rm167
Commented Feb 7, 2023 at 14:56

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