TrajectoryCollectionAggregator#

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