GeoThinneR (version 2.1.1)

estimate_k_max: Estimate Maximum Neighbors for kd-Tree Thinning

Description

This function estimates the maximum value of k (the number of nearest neighbors) for kd-tree-based thinning by evaluating the densest regions of a spatial dataset. The function uses a histogram-based binning approach for efficiency and low memory usage.

Usage

estimate_k_max(coordinates, thin_dist, distance = c("haversine", "euclidean"))

Value

A numeric value representing the maximum k (number of nearest neighbors) required for the densest regions in the dataset.

Arguments

coordinates

A matrix of spatial coordinates with two columns for longitude and latitude.

thin_dist

A positive numeric value representing the thinning distance in kilometers. This defines the resolution of the grid used for density calculations.

distance

Distance metric used `c("haversine", "euclidean")`.

Details

The function divides the spatial domain into grid cells based on the specified thinning distance. Grid cell sizes are determined assuming approximately 111.32 km per degree (latitude/longitude). The function identifies the densest grid cells and their immediate neighbors to compute the maximum k value.

Examples

# Generate sample data
set.seed(123)
coordinates <- matrix(runif(200, min = -10, max = 10), ncol = 2)

# Estimate k for kd-tree thinning
k_max <- estimate_k_max(coordinates, thin_dist = 50)
print(k_max)