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.
- 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 (datetime.timedelta) – Minimum duration required for stop detection
min_angle (float) – Minimum angle for significant point extraction
References
Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on visualization and computer graphics, 17(2), 205-219.
- 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