Calculate Harversine distance

Calculate Harversine distance

Usage

haversine_kmeans(data, latitude, longitude, k)

Arguments

data

is a dataframe with spatial attributes - Latitude and Logitude

latitude

nested imput from ec_flag_outlier

longitude

nested imput from ec_flag_outlier

k

is number of cluster required for the data set you have. Normally visual inspection can give a sense on number of clusters. Cautious to have more than expected clusters to fit all data points, as overfitting can end up inluding bad data points in the analysis. e.g. k = 3

Value

A data frame with centroid and clusters using Harversine distance matrix

Examples

data_x <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  BO_sstmean = c(12, 13, 14),
  BO_sstmin = c(9, 6, 10),
  BO_sstmax = c(14, 16, 18)
)

result <- haversine_kmeans(
  data_x,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  k = 3
)