GeoThinneR (version 1.0.0)

kd_tree_thinning: Perform K-D Tree ANN Thinning

Description

This function applies the K-D tree Approximate Nearest Neighbors (ANN) thinning algorithm on a set of spatial coordinates. It can optionally use space partitioning to improve the thinning process, which is particularly useful for large datasets.

Usage

kd_tree_thinning(
  coordinates,
  thin_dist = 10,
  trials = 10,
  all_trials = FALSE,
  space_partitioning = FALSE,
  euclidean = FALSE,
  R = 6371
)

Value

A list. If `all_trials` is `FALSE`, the list contains a single logical vector indicating which points are kept in the best trial. If `all_trials` is `TRUE`, the list contains a logical vector for each trial.

Arguments

coordinates

A matrix of coordinates to thin, with two columns representing longitude and latitude.

thin_dist

A numeric value representing the thinning distance in kilometers. Points closer than this distance to each other are considered redundant and may be removed.

trials

An integer specifying the number of trials to run for thinning. Multiple trials can help achieve a better result by randomizing the thinning process. Default is 10.

all_trials

A logical value indicating whether to return results of all attempts (`TRUE`) or only the best attempt with the most points retained (`FALSE`). Default is `FALSE`.

space_partitioning

A logical value indicating whether to use space partitioning to divide the coordinates into grid cells before thinning. This can improve efficiency in large datasets. Default is `FALSE`.

euclidean

Logical value indicating whether to compute the Euclidean distance (`TRUE`) or Haversine distance (`FALSE`, default).

R

A numeric value representing the radius of the Earth in kilometers. The default is 6371 km.

Examples

# Generate sample coordinates
set.seed(123)
coordinates <- matrix(runif(20, min = -180, max = 180), ncol = 2) # 10 random points

# Perform K-D Tree thinning without space partitioning
result <- kd_tree_thinning(coordinates, thin_dist = 10, trials = 5, all_trials = FALSE)
print(result)

# Perform K-D Tree thinning with space partitioning
result_partitioned <- kd_tree_thinning(coordinates, thin_dist = 5000, trials = 5,
                                       space_partitioning = TRUE, all_trials = TRUE)
print(result_partitioned)

# Perform K-D Tree thinning with Cartesian coordinates
cartesian_coordinates <- long_lat_to_cartesian(coordinates[, 1], coordinates[, 2])
result_cartesian <- kd_tree_thinning(cartesian_coordinates, thin_dist = 10, trials = 5,
                                     euclidean = TRUE)
print(result_cartesian)