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Spatial Thinning of Points

Source: R/thin_points.R
thin_points.Rd

This function performs spatial thinning of geographic points to reduce point density while maintaining spatial representation. Points are thinned based on a specified distance, grid, or precision, and multiple trials can be performed to identify the best thinned dataset.

Usage

thin_points(
  data,
  long_col = NULL,
  lat_col = NULL,
  group_col = NULL,
  method = c("brute_force", "kd_tree", "r_tree", "round_hash", "grid", "precision"),
  trials = 10,
  all_trials = FALSE,
  target_points = NULL,
  seed = NULL,
  verbose = FALSE,
  ...
)

Arguments

data

A data frame or tibble containing the points to thin. Must contain longitude and latitude columns.

long_col

Name of the column with longitude coordinates (default: "decimalLongitude").

lat_col

Name of the column with latitude coordinates (default: "decimalLatitude").

group_col

Name of the column for grouping points (e.g., species name, year). If NULL, no grouping is applied.

method

Thinning method to use `c("brute_force", "kd_tree", "r_tree", "round_hash", "grid", "precision")`.

trials

Number of thinning iterations to perform (default: 10).

all_trials

If TRUE, returns results of all attempts; if FALSE, returns the best attempt with the most points retained (default: FALSE).

target_points

Optional; a numeric value specifying the exact number of points to keep. If NULL (default), maximizes the number of kept points.

seed

Optional; an integer seed for reproducibility of results.

verbose

If TRUE, prints progress messages (default: FALSE).

...

Additional parameters passed to specific thinning methods (e.g., thin_dist, precision, resolution, origin, R).

Value

A tibble of thinned points, or a combined result of all attempts if `all_trials` is TRUE.

Details

The thinning methods available are: - `brute_force`: Uses a brute force approach to thin points. - `kd_tree`: Uses K-D trees for thinning. - `r_tree`: Uses R-trees for thinning. - `round_hash`: Uses rounding and hashing for efficient thinning. - `grid`: Applies a grid-based thinning method. - `precision`: Utilizes precision-based thinning.

For more information on specific thinning methods and inputs, refer to their respective documentation: - `brute_force_thinning()` - `grid_thinning()` - `kd_tree_thinning()` - `r_tree_thinning()` - `rounding_hashing_thinning()` - `precision_thinning()`

Examples

# Generate sample data
set.seed(123)
sample_data <- data.frame(
  decimalLongitude = runif(100, -180, 180),
  decimalLatitude = runif(100, -90, 90)
)

# Perform thinning using K-D tree method
thinned_data <- thin_points(sample_data,
                             long_col = "decimalLongitude",
                             lat_col = "decimalLatitude",
                             method = "kd_tree",
                             trials = 5,
                             verbose = TRUE)
#> Starting spatial thinning at 2024-10-05 07:04:46 
#> Starting thinning process using method: kd_tree 
#> Thinning process completed.
#> Total execution time: 0 seconds

# Perform thinning with grouping
sample_data$species <- sample(c("species_A", "species_B"), 100, replace = TRUE)
thinned_grouped_data <- thin_points(sample_data,
                                     long_col = "decimalLongitude",
                                     lat_col = "decimalLatitude",
                                     group_col = "species",
                                     method = "kd_tree",
                                     trials = 10)