compute_neighbors_kdtree {GeoThinneR} | R Documentation |
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
)
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.
Value
A list where each element corresponds to a point and contains the indices of its neighbors, excluding the point itself.
Examples
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,)