green_space_clustering: Green Space Clustering with K-Means and Tile Layer Control in...

In greenR: Green Index Quantification, Analysis and Visualization

Green Space Clustering with K-Means and Tile Layer Control in Leaflet

Description

This function performs K-means clustering on green spaces based on their area size and visualizes the results on a Leaflet map. Users must specify the number of clusters. The function includes a layer control for switching between different basemap tiles.

Usage

green_space_clustering(green_areas_data, num_clusters)

Arguments

green_areas_data	List containing green areas data (obtained from get_osm_data function or similar).
num_clusters	Integer number of clusters to divide the green spaces into.

Value

A Leaflet map object displaying clustered green spaces with layer control for basemap tiles.

Examples

  # Create example green_areas_data
  library(sf)
  green_areas <- st_sf(
    id = 1:5,
    geometry = st_sfc(
      st_polygon(list(rbind(c(0, 0), c(0, 1), c(1, 1), c(1, 0), c(0, 0)))),
      st_polygon(list(rbind(c(1, 1), c(1, 2), c(2, 2), c(2, 1), c(1, 1)))),
      st_polygon(list(rbind(c(2, 2), c(2, 3), c(3, 3), c(3, 2), c(2, 2)))),
      st_polygon(list(rbind(c(3, 3), c(3, 4), c(4, 4), c(4, 3), c(3, 3)))),
      st_polygon(list(rbind(c(4, 4), c(4, 5), c(5, 5), c(5, 4), c(4, 4))))
    ),
    crs = 4326  # Assign a CRS (WGS 84)
  )
  green_areas_data <- list(osm_polygons = green_areas)
  # Run the clustering function
  map <- green_space_clustering(green_areas_data, num_clusters = 2)
  map # to display the map

greenR documentation built on June 29, 2024, 9:07 a.m.