Thinning Algorithm for Spatial Data — max_thinning

This function performs the core thinning algorithm used to reduce the density of points in spatial data while maintaining spatial representation. It iteratively removes the points with the most neighbors until no points with neighbors remain. The algorithm supports multiple trials to find the optimal thinning solution.

Usage

max_thinning_algorithm(
  neighbor_indices,
  trials,
  all_trials = FALSE,
  priority = NULL
)

Arguments

neighbor_indices: A list of integer vectors where each element contains the indices of the neighboring points for each point in the dataset.
trials: A positive integer specifying the number of thinning trials to perform. 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`.
priority: A numeric vector of the same length as the number of points, specifying a priority weight for each point. Higher values indicate higher importance and are favored when selecting which points to retain. Priority is used to guide selection when multiple candidate points are otherwise equally valid (e.g., points in the same grid cell, with the same rounded coordinates, or with the same number of neighbors).

Value

A list of logical vectors indicating which points are kept in each trial if all_trials is TRUE; otherwise, a list with a single logical vector indicating the points kept in the best trial.

Examples

# Example usage within a larger thinning function
neighbor_indices <- list(c(2, 3), c(1, 3), c(1, 2))
trials <- 5
all_trials <- FALSE
kept_points <- max_thinning_algorithm(neighbor_indices, trials, all_trials)
print(kept_points)
#> [[1]]
#> [1]  TRUE FALSE FALSE
#>