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
)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.
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.
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)
#> [[1]]
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>
# 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)
#> [[1]]
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
#>
#> [[2]]
#> [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#>
#> [[3]]
#> [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#>
#> [[4]]
#> [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#>
#> [[5]]
#> [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#>
# 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)
#> [[1]]
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>