MovingPandas.TrajectoryCollectionAggregator

movingpandas: Implementation of Trajectory classes and functions built on top of GeoPandas

TrajectoryCollectionAggregator generalizes and aggregates trajectories by extracting clusters of significant trajectory points and computing flows between the clusters. It is based on an algorithm by Andrienko & Andrienko (2011) as described in https://anitagraser.com/2016/11/07/movement-data-in-gis-3-visualizing-massive-trajectory-datasets/.

class movingpandas.TrajectoryCollectionAggregator(traj_collection, max_distance, min_distance, min_stop_duration, min_angle=45)
__init__(traj_collection, max_distance, min_distance, min_stop_duration, min_angle=45)

Aggregates trajectories by extracting significant points, clustering those points, and extracting flows between clusters.

Based on the algorithm described by Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on visualization and computer graphics, 17(2), 205-219.

Parameters:
  • traj_collection (TrajectoryCollection) – TrajectoryCollection to be aggregated
  • max_distance (float) – Maximum distance between significant points (distance is calculated in CRS units, except if the CRS is geographic, e.g. EPSG:4326 WGS84, then distance is calculated in meters)
  • min_distance (float) – Minimum distance between significant points
  • min_stop_duration (integer) – Minimum duration required for stop detection (in seconds)
  • min_angle (float) – Minimum angle for significant point extraction
get_clusters_gdf()

Return the extracted cluster centroids

Returns:Cluster centroids, incl. the number of clustered significant points (n)
Return type:GeoDataFrame
get_flows_gdf()

Return the extracted flows

Returns:Flow lines, incl. the number of trajectories summarized in the flow (weight)
Return type:GeoDataFrame
get_significant_points_gdf()

Return the extracted significant points

Returns:Significant points
Return type:GeoDataFrame