Applies the R-tree thinning algorithm on a set of coordinates.
Usage
r_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 longitude and latitude.
- thin_dist
Thinning distance in kilometers.
- trials
Number of trials to run for thinning.
- all_trials
If TRUE, returns results of all attempts; if FALSE, returns the best attempt with the most points retained (default: FALSE).
- space_partitioning
A logical value indicating whether to use space partitioning.
- euclidean
Logical value indicating whether to compute the Euclidean distance (`TRUE`) or Haversine distance (`FALSE`, default).
- R
Radius of the Earth in kilometers (default: 6371 km).
Value
A logical vector indicating which points are kept in the best trial if all_trials is FALSE; otherwise, a list of logical vectors for each trial.
Details
If you want to use R-trees you need to install the `rtree` package from `remotes::install_github("jmestret/rtree")`. It is a modified version from https://github.com/akoyabio/rtree.
Examples
if (requireNamespace("rtree", quietly = TRUE)) {
# Generate random coordinates
set.seed(123)
coordinates <- matrix(runif(20, min = -180, max = 180), ncol = 2) # 10 random points
# Perform thinning without space partitioning
result <- r_tree_thinning(coordinates, thin_dist = 10, trials = 5)
print(result)
# Perform thinning with space partitioning
result_space_part <- r_tree_thinning(coordinates, thin_dist = 10, trials = 5,
space_partitioning = TRUE)
print(result_space_part)
# Perform thinning with euclidean distance
result_euclidean <- r_tree_thinning(coordinates, thin_dist = 10, trials = 5, euclidean = TRUE)
print(result_euclidean)
}
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#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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