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) and the number of unique trajectory objects summarized in the flow (obj_weight).
Return type:: GeoDataFrame

get_significant_points_gdf()#

Return the extracted significant points