flowcluster (version 0.2.1)

dbscan_sensitivity: Sensitivity analysis of DBSCAN parameters for flow clustering.

Description

The function allows you to test different combinations of epsilon and minPts parameters for clustering flows using DBSCAN. It can be used to determine what parameter values make sense for your data

Usage

dbscan_sensitivity(
  dist_mat,
  flows,
  options_epsilon,
  options_minpts,
  w_vec = NULL
)

Value

a tibble with columns: id (to identify eps and minpts), cluster, size (number of desire lines in cluster), count_sum (total count per cluster)

Arguments

dist_mat

a precalculated distance matrix between desire lines (output of distance_matrix())

flows

the original flows tibble (must contain flow_ID and 'count' column)

options_epsilon

a vector of options for the epsilon parameter

options_minpts

a vector of options for the minPts parameter

w_vec

Optional precomputed weight vector (otherwise computed internally from 'count' column)

Examples

flows <- sf::st_transform(flows_leeds, 3857)
flows <- head(flows, 1000) # for testing
# Add flow lengths and coordinates
flows <- add_flow_length(flows)
# filter by length
flows <- filter_by_length(flows, length_min = 5000, length_max = 12000)
# Add x, y, u, v coordinates to flows
flows <- add_xyuv(flows)
# Calculate distance matrix
distances <- flow_distance(flows, alpha = 1.5, beta = 0.5)
dmat <- distance_matrix(distances)
# Generate weight vector
w_vec <- weight_vector(dmat, flows, weight_col = "count")

# Define the parameters for sensitivity analysis
options_epsilon <- seq(1, 10, by = 2)
options_minpts <- seq(10, 100, by = 10)
# # Run the sensitivity analysis
results <- dbscan_sensitivity(
  dist_mat = dmat,
  flows = flows,
  options_epsilon = options_epsilon,
  options_minpts = options_minpts,
  w_vec = w_vec
)