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Query the k nearest spatial objects.

Source: R/spatial_queries.R
sedona_knn_query.Rd

Given a spatial RDD, a query object x, and an integer k, find the k nearest spatial objects within the RDD from x (distance between x and another geometrical object will be measured by the minimum possible length of any line segment connecting those 2 objects).

Usage

sedona_knn_query(
  rdd,
  x,
  k,
  index_type = c("quadtree", "rtree"),
  result_type = c("rdd", "sdf", "raw")
)

Arguments

rdd

A Sedona spatial RDD.

x

The query object.

k

Number of nearest spatail objects to return.

index_type

Index to use to facilitate the KNN query. If NULL, then do not build any additional spatial index on top of x. Supported index types are "quadtree" and "rtree".

result_type

Type of result to return. If "rdd" (default), then the k nearest objects will be returned in a Sedona spatial RDD. If "sdf", then a Spark dataframe containing the k nearest objects will be returned. If "raw", then a list of k nearest objects will be returned. Each element within this list will be a JVM object of type org.locationtech.jts.geom.Geometry.

Value

The KNN query result.

See also

Other Sedona spatial query: sedona_range_query()

Examples

library(sparklyr)
library(apache.sedona)

sc <- spark_connect(master = "spark://HOST:PORT")

if (!inherits(sc, "test_connection")) {
  knn_query_pt_x <- -84.01
  knn_query_pt_y <- 34.01
  knn_query_pt_tbl <- sdf_sql(
    sc,
    sprintf(
      "SELECT ST_GeomFromText(\"POINT(%f %f)\") AS `pt`",
      knn_query_pt_x,
      knn_query_pt_y
    )
  ) %>%
      collect()
  knn_query_pt <- knn_query_pt_tbl$pt[[1]]
  input_location <- "/dev/null" # replace it with the path to your input file
  rdd <- sedona_read_geojson_to_typed_rdd(
    sc,
    location = input_location,
    type = "polygon"
  )
  knn_result_sdf <- sedona_knn_query(
    rdd,
    x = knn_query_pt, k = 3, index_type = "rtree", result_type = "sdf"
  )
}