Trajectory#

Functions for identifying and grouping trajectories from point data.

cuspatial.derive_trajectories(object_ids, points: GeoSeries, timestamps)#

Derive trajectories from object ids, points, and timestamps.

Parameters

object_ids: column of object (e.g., vehicle) ids
pointsGeoSeries: The points of the trajectories
timestamps: column of timestamps in any resolution

Returns

resulttuple (objects, traj_offsets)

objectscudf.DataFrame: object_ids, xs, ys, and timestamps sorted by (object_id, timestamp), used by trajectory_bounding_boxes and trajectory_distances_and_speeds
traj_offsetscudf.Series: offsets of discovered trajectories

Examples

Compute sorted objects and discovered trajectories

>>> objects, traj_offsets = cuspatial.derive_trajectories(
        [0, 1, 0, 1],  # object_id
        [0, 0, 1, 1],  # x
        [0, 0, 1, 1],  # y
        [0, 10000, 0, 10000] # timestamp
    )
>>> print(traj_offsets)
    0  0
    1  2
>>> print(objects)
    object_id    x    y           timestamp
    0          0  0.0  0.0 1970-01-01 00:00:00
    1          0  1.0  1.0 1970-01-01 00:00:10
    2          1  0.0  0.0 1970-01-01 00:00:00
    3          1  1.0  1.0 1970-01-01 00:00:10

cuspatial.trajectory_distances_and_speeds(num_trajectories, object_ids, points: GeoSeries, timestamps)#

Compute the distance traveled and speed of sets of trajectories

Parameters

num_trajectories: number of trajectories (unique object ids)
object_ids: column of object (e.g., vehicle) ids
points: GeoSeries: A series of points
timestamps: column of timestamps in any resolution

Returns

resultcudf.DataFrame

meterscudf.Series: trajectory distance (in kilometers)
speedcudf.Series: trajectory speed (in meters/second)

Examples

Compute the distances and speeds of derived trajectories

>>> objects, traj_offsets = cuspatial.derive_trajectories(...)
>>> dists_and_speeds = cuspatial.trajectory_distances_and_speeds(
        len(traj_offsets)
        objects['object_id'],
        objects['x'],
        objects['y'],
        objects['timestamp']
    )
>>> print(dists_and_speeds)
                      distance       speed
    trajectory_id
    0              1414.213562  141.421356
    1              1414.213562  141.421356

cuspatial.trajectory_bounding_boxes(num_trajectories, object_ids, points: GeoSeries)#

Compute the bounding boxes of sets of trajectories.

Parameters

num_trajectories: number of trajectories (unique object ids)
object_ids: column of object (e.g., vehicle) ids
points: GeoSeries: Series of trajectory points

Returns

resultcudf.DataFrame

minimum bounding boxes (in kilometers) for each trajectory

x_mincudf.Series: the minimum x-coordinate of each bounding box
y_mincudf.Series: the minimum y-coordinate of each bounding box
x_maxcudf.Series: the maximum x-coordinate of each bounding box
y_maxcudf.Series: the maximum y-coordinate of each bounding box

Examples

Compute the minimum bounding boxes of derived trajectories

>>> objects, traj_offsets = cuspatial.derive_trajectories(
        [0, 0, 1, 1],  # object_id
        [0, 1, 2, 3],  # x
        [0, 0, 1, 1],  # y
        [0, 10, 0, 10] # timestamp
    )
>>> traj_bounding_boxes = cuspatial.trajectory_bounding_boxes(
        len(traj_offsets),
        objects['object_id'],
        objects['x'],
        objects['y']
    )
>>> print(traj_bounding_boxes)
    x_min   y_min   x_max   y_max
0     0.0     0.0     2.0     2.0
1     1.0     1.0     3.0     3.0