flowcluster (version 0.2.1)

aggregate_clustered_flows: Aggregate clustered OD flows into representative lines

Description

This function aggregates flows within clusters and creates a single representative line for each cluster. The start and end coordinates are computed as weighted averages (weighted by flow counts or another variable), or simple means if no weights are provided. Each cluster is represented by one LINESTRING.

Usage

aggregate_clustered_flows(flows, weight = NULL, crs = sf::st_crs(flows))

Value

An sf object with one line per cluster, containing:

• count_total: total weight (if provided), otherwise number of flows

• size: the cluster size (from the input, not recomputed)

• geometry: a LINESTRING representing the aggregated OD flow

Arguments

flows

An sf object containing OD flows with coordinates for origins (x, y) and destinations (u, v), a cluster column, and optionally a count or other weighting variable.

weight

(optional) Name of a column in flows to use for weighting. If NULL (default), unweighted means are used.

crs

Coordinate reference system for the output (default: taken from flows).

Examples

# ----- 1. Basic Usage: A quick, runnable example ---
# This demonstrates the function with minimal, fast data preparation.
flows <- flowcluster::flows_leeds

# Create the required input columns in a single, fast pipeline
flows_clustered <- flows |>
  add_xyuv() |>
  # Manually create 3 dummy clusters for demonstration
  dplyr::mutate(cluster = sample(1:3, size = nrow(flows), replace = TRUE)) |>
  # The function requires a 'size' column, so we add it
  dplyr::group_by(cluster) |>
  dplyr::add_tally(name = "size") |>
  dplyr::ungroup()

# Demonstrate the function
flows_agg_w <- aggregate_clustered_flows(flows_clustered, weight = "count")
print(flows_agg_w)

# ----- 2. Detailed Workflow (not run by default) ---
if (FALSE) {
  # This example shows the ideal end-to-end workflow, from raw data
  # to clustering and finally aggregation. It is not run during checks
  # because the clustering steps are too slow.

  # a) Prepare the data by filtering and adding coordinates
  flows_prep <- flowcluster::flows_leeds |>
    sf::st_transform(3857) |>
    add_flow_length() |>
    filter_by_length(length_min = 5000, length_max = 12000) |>
    add_xyuv()

  # b) Calculate distances and cluster the flows
  distances <- flow_distance(flows_prep, alpha = 1.5, beta = 0.5)
  dmat <- distance_matrix(distances)
  wvec <- weight_vector(dmat, flows_prep, weight_col = "count")
  flows_clustered_real <- cluster_flows_dbscan(dmat, wvec, flows_prep, eps = 8, minPts = 70)

  # c) Filter clusters and add a 'size' column
  flows_clustered_real <- flows_clustered_real |>
    dplyr::filter(cluster != 0) |> # Filter out noise points
    dplyr::group_by(cluster) |>
    dplyr::mutate(size = dplyr::n()) |>
    dplyr::ungroup()

  # d) Now, use the function on the clustered data
  flows_agg_real <- aggregate_clustered_flows(flows_clustered_real, weight = "count")
  print(flows_agg_real)

  # e) Visualize the results
  if (requireNamespace("tmap", quietly = TRUE)) {
    library(tmap)
    # This plot uses modern tmap v4 syntax.
    tm_shape(flows_clustered_real, facet = "cluster") +
      tm_lines(col = "grey50", alpha = 0.5) +
    tm_shape(flows_agg_real) +
      tm_lines(col = "red", lwd = 2) +
    tm_layout(title = "Original Flows (Grey) and Aggregated Flows (Red)")
  }
}