compute_neighbors_kdtree: Compute Neighbors Using kd-Tree

Description

Computes neighbors for each point in a set of coordinates using a kd-tree for efficient neighbor searches. This method is particularly useful for large datasets.

Usage

compute_neighbors_kdtree(
  coordinates,
  thin_dist,
  k = NULL,
  distance = c("haversine", "euclidean"),
  R = 6371
)

Value

A list where each element corresponds to a point and contains the indices of its neighbors, excluding the point itself.

Arguments

coordinates: A matrix of coordinates to thin, with two columns representing longitude and latitude.
thin_dist: A positive numeric value representing the thinning distance in kilometers.
k: An integer specifying the maximum number of neighbors to consider for each point.
distance: A character string specifying the distance metric to use `c("haversine", "euclidean")`.
R: A numeric value representing the radius of the Earth in kilometers. The default is 6371 km.

Details

This function uses kd-tree (via `nabor` package) for efficient spatial searches. The kd-tree inherently works with Euclidean distances. If `"haversine"` is selected, the function first converts geographic coordinates to 3D Cartesian coordinates before constructing the kd-tree.

Examples

Run this code

set.seed(123)
coords <- matrix(runif(20, min = -180, max = 180), ncol = 2)

# Compute neighbors using kd-tree
neighbors <- compute_neighbors_kdtree(coords, thin_dist = 10,)

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