# Spatial joins

#### 2026-01-10

This vignette shows how to use `ddbs_join()` to perform fast spatial join operations on large data sets with three different approaches:

1 In-memory: pass `sf` objects and get an `sf` result (DuckDB runs under the hood, no persistent DataBase). 2. Connected: pass table names stored in an existing DuckDB connection and get an `sf` result. 3. Write-to-DB: same as (2), but write the result to a new DuckDB table.

Let’s see a few examples. First, let’s load a few libraries and our sample data:

    library(duckspatial)
    # library(mapview)
    library(sf)

    # polygons
    countries_sf  <- sf::st_read(
        system.file("spatial/countries.geojson",  package = "duckspatial"),
        quiet = TRUE
        )

    # random points
    set.seed(42)
    n <- 10000
    points_sf <- data.frame(
      id = 1:n,
      x  = runif(n, min = -180, max = 180),
      y  = runif(n, min =  -90, max =  90)
    ) |>
      sf::st_as_sf(coords = c("x","y"), crs = 4326)

# 1) In-memory: pass `sf`, return `sf`

The simplest way to perform fast spatial join. You simply pass two `sf` objects, and `ddbs_join()` spins up a temporary DuckDB, runs the join, and returns an `sf.`

- When to use: quick analysis, prototyping, or when you don’t need to persist intermediate tables.

    out_sf1 <- ddbs_join(
      x    = points_sf,
      y    = countries_sf,
      join = "within"
    )

    # quick peek
    # mapview(out_sf1, zcol="NAME_ENGL")

# 2) Connected: pass table names in DuckDB, return `sf`

In the second and third approaches, we make use of a connection to an existing DuckDB database. So let’s create a fresh DuckDB connection using the `ddbs_create_conn()` function, which automatically install and load DuckDB spatial extension to the connection.

    # create a fresh DuckDB connection
    conn <- duckspatial::ddbs_create_conn()

Now, in this second approach you need first to write your layers to DuckDB, and perform the spatial join by referencing their table names. Like this:

    # write data to DuckDB
    ddbs_write_vector(conn, points_sf,   "points",    overwrite = TRUE)
    ddbs_write_vector(conn, countries_sf, "countries", overwrite = TRUE)

    # spatial join inside DuckDB; result returned as sf
    out_sf2 <- ddbs_join(
      conn,
      x    = "points",
      y    = "countries",
      join = "within"
    )

- When to use: iterative workflows, larger-than-memory data, or when you’ll run multiple queries on the same tables.

# 3) Write-to-DB: create a new table with the join result

The output of approaches 1 and 2 is an `sf` object loaded to your memory. In this third approach, `ddbs_join()` writes a new table in the DuckDB database. You simply need to the `name` of the new table.

    ddbs_join(
        conn = conn,
        x = "points",
        y = "countries",
        join = "within",
        name = "points_in_countries",
        overwrite = TRUE
    )

    # use the result in SQL (or read back as sf later)
    # DBI::dbReadTable(conn, "points_in_countries") |>
    #     sf::st_as_sf(wkt = 'geometry') |>
    #     head()

- When to use: iterative workflows, larger-than-memory data, or when you’ll run multiple queries on the same tables.

# Spatial Join Predicates:

A spatial predicate is really just a function that evaluates some spatial relation between two geometries and returns true or false, e.g., “does a contain b” or “is a within distance x of b”. The `join` argument accepts the spatial predicates:

- `"ST_Intersects"`: Whether a intersects b
- `"ST_Contains"`: Whether a contains b
- `"ST_ContainsProperly"`: Whether a contains b without b touching a’s boundary
- `"ST_Within"`: Whether a is within b
- `"ST_Overlaps"`: Whether a overlaps b
- `"ST_Touches"`: Whether a touches b
- `"ST_Equals"`: Whether a is equal to b
- `"ST_Crosses"`: Whether a crosses b
- `"ST_Covers"`: Whether a covers b
- `"ST_CoveredBy"`: Whether a is covered by b
- `"ST_DWithin"`: x) Whether a is within distance x of b

# Clean up

Don’t forget to disconnect from the database.

    duckdb::dbDisconnect(conn)