MovingPandas.TrajectoryCollection¶

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

class movingpandas.TrajectoryCollection(data, traj_id_col=None, obj_id_col=None, t=None, x=None, y=None, crs='epsg:4326', min_length=0, min_duration=None)¶

__init__(data, traj_id_col=None, obj_id_col=None, t=None, x=None, y=None, crs='epsg:4326', min_length=0, min_duration=None)¶

Create TrajectoryCollection from list of trajectories or GeoDataFrame

Parameters:

data (list[Trajectory] or GeoDataFrame or DataFrame) – List of Trajectory objects or a GeoDataFrame with trajectory IDs, point geometry column and timestamp index
traj_id_col (string) – Name of the GeoDataFrame column containing trajectory IDs
obj_id_col (string) – Name of the GeoDataFrame column containing moving object IDs
t (string) – Name of the DataFrame column containing the timestamp
x (string) – Name of the DataFrame column containing the x coordinate
y (string) – Name of the DataFrame column containing the y coordinate
crs (string) – CRS of the x/y coordinates
min_length (numeric) – Desired minimum length of trajectories. Length is calculated using CRS units, except if the CRS is geographic (e.g. EPSG:4326 WGS84) then length is calculated in meters. (Shorter trajectories are discarded.)
min_duration (timedelta) – Desired minimum duration of trajectories. (Shorter trajectories are discarded.)

Examples

>>> import geopandas as read_file
>>> import movingpandas as mpd
>>>
>>> gdf = read_file('data.gpkg')
>>> collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')

add_acceleration(overwrite=False, name='acceleration', units=(None, None, None, None))¶

Add acceleration column and values to the trajectories.

Acceleration is calculated as CRS units per second squared, except if the CRS is geographic (e.g. EPSG:4326 WGS84) then acceleration is calculated in meters per second squared.

Parameters:

overwrite (bool) – Whether to overwrite existing acceleration values (default: False)
name (str) – Name of the acceleration column (default: “acceleration”)
units (tuple(str)) –
Units in which to calculate acceleration

distancestr
Abbreviation for the distance unit (default: CRS units, or metres if geographic)

timestr
Abbreviation for the time unit (default: seconds)

time2str
Abbreviation for the second time unit (default: seconds)

For more info, check the list of supported units at https://movingpandas.org/units

add_angular_difference(overwrite=False, name='angular_difference')¶

Add angular difference to the trajectory’s DataFrame.

Angular difference is calculated as the absolute smaller angle between direction for points along the trajectory. Values are [0, 180.0]

Parameters:: overwrite (bool) – Whether to overwrite existing angular difference values (default: False)

add_direction(overwrite=False, name='direction')¶

Add direction column and values to the trajectories.

The direction is calculated between consecutive locations. Direction values are in degrees, starting North turning clockwise.

Parameters:: overwrite (bool) – Whether to overwrite existing direction values (default: False)

add_distance(overwrite=False, name='distance', units=None)¶

Add distance column and values to the trajectories.

Parameters:

overwrite (bool) – Whether to overwrite existing distance values (default: False)
name (str) – Name of the distance column (default: “distance”)
units (str) – Units in which to calculate distance values (default: CRS units) For more info, check the list of supported units at https://movingpandas.org/units

add_speed(overwrite=False, name='speed', units=(None, None, None, None))¶

Add speed column and values to the trajectories.

Speed is calculated as CRS units per second, except if the CRS is geographic (e.g. EPSG:4326 WGS84) then speed is calculated in meters per second.

Parameters:

overwrite (bool) – Whether to overwrite existing speed values (default: False)
units (tuple(str)) –
Units in which to calculate speed

distancestr
Abbreviation for the distance unit (default: CRS units, or metres if geographic)

timestr
Abbreviation for the time unit (default: seconds)

For more info, check the list of supported units at https://movingpandas.org/units

add_timedelta(overwrite=False, name='timedelta')¶

Add timedelta column and values to the trajectories.

Timedelta is calculated as the time difference between the current and the previous row. Values are instances of datetime.timedelta.

Parameters:

overwrite (bool) – Whether to overwrite existing timedelta values (default: False)
name (str) – Name of the timedelta column (default: “timedelta”)

add_traj_id(overwrite=False)¶

Add trajectory id column and values to the trajectories.

Parameters:: overwrite (bool) – Whether to overwrite existing trajectory id values (default: False)

clip(polygon, point_based=False)¶

Clip trajectories by the given polygon.

Parameters:

polygon (shapely.geometry.Polygon) – Polygon to clip with
point_based (bool) – Clipping method

Returns:

Resulting clipped trajectory segments

Return type:

TrajectoryCollection

copy()¶

Return a copy of the trajectory collection.

Return type:: TrajectoryCollection

drop(**kwargs)¶

Drop columns or rows from the trajectories’ DataFrames

Examples

>>> tc.drop(columns=['abc','def'])

filter(property_name, property_values)¶

Filter trajectories by property

A property is a value in the df that is constant for the whole trajectory. The filter only checks if the value on the first row equals the requested property value.

Parameters:

property_name (string) – Name of the DataFrame column containing the property
property_values (list(any)) – Desired property values

Returns:

Trajectories that fulfill the filter criteria

Return type:

TrajectoryCollection

Examples

>>> filtered = trajectory_collection.filter('object_type', ['TypeA', 'TypeB'])

get_column_names()¶

Return the list of column names

Return type:: list

get_crs()¶: Return the CRS of the trajectories

get_direction_col()¶

Return name of the direction column

Return type:: string

get_end_locations(with_direction=False)¶

Returns GeoDataFrame with trajectory end locations

Returns:: Trajectory end locations
Return type:: GeoDataFrame

get_geom_col()¶

Return name of the geometry column

Return type:: string

get_intersecting(polygon)¶

Return trajectories that intersect the given polygon.

Parameters:: polygon (shapely.geometry.Polygon) – Polygon to intersect with
Returns:: Resulting intersecting trajectories
Return type:: TrajectoryCollection

get_locations_at(t, with_direction=False)¶

Returns GeoDataFrame with trajectory locations at the specified timestamp

Parameters:: t (datetime.datetime) – Timestamp to extract trajectory locations for
Returns:: Trajectory locations at timestamp t
Return type:: GeoDataFrame

get_max(column)¶

Return maximum value in the provided DataFrame column over all trajectories

Parameters:: column (string) – Name of the DataFrame column
Returns:: Maximum value
Return type:: Sortable

get_min(column)¶

Return minimum value in the provided DataFrame column over all trajectories

Parameters:: column (string) – Name of the DataFrame column
Returns:: Minimum value
Return type:: Sortable

get_segments_between(t1, t2)¶

Return Trajectory segments between times t1 and t2.

Parameters:

t1 (datetime.datetime) – Start time for the segments
t2 (datetime.datetime) – End time for the segments

Returns:

Extracted trajectory segments

Return type:

TrajectoryCollection

get_speed_col()¶

Return name of the speed column

Return type:: string

get_start_locations(with_direction=False)¶

Returns GeoDataFrame with trajectory start locations

Returns:: Trajectory start locations
Return type:: GeoDataFrame

get_traj_id_col()¶

Return name of the trajectory ID column

Return type:: string

get_trajectory(traj_id)¶

Return the Trajectory with the requested ID

Parameters:: traj_id (any) – Trajectory ID
Return type:: Trajectory

hvplot(*args, **kwargs)¶

Generate an interactive plot.

Parameters:

args – These parameters will be passed to the TrajectoryPlotter
kwargs –
These parameters will be passed to the TrajectoryPlotter

To customize the plots, check the list of supported colormaps_. .. _colormaps: https://holoviews.org/user_guide/Colormaps.html#available-colormaps

Examples

Plot speed along trajectories (with legend and specified figure size):

>>> collection.hvplot(c='speed', line_width=7.0, width=700, height=400,
                      colorbar=True)

intersection(feature, point_based=False)¶

Intersect trajectories with the given polygon feature.

Feature attributes are appended to the trajectory’s DataFrame.

By default, the trajectory’s line representation is clipped by the polygon. If pointbased=True, the trajectory’s point representation is used instead, leading to shorter segments.

Parameters:

feature (shapely Feature) – Feature to intersect with
point_based (bool) – Clipping method

Returns:

Intersecting trajectory segments

Return type:

TrajectoryCollection

is_latlon()¶: Return True if the trajectory CRS is geographic (e.g. EPSG:4326 WGS84)

plot(*args, **kwargs)¶

Generate a plot.

Parameters:

args – These parameters will be passed to the TrajectoryPlotter
kwargs – These parameters will be passed to the TrajectoryPlotter

Examples

Plot speed along trajectories (with legend and specified figure size):

>>> trajectory_collection.plot(column='speed', legend=True, figsize=(9,5))

to_line_gdf(columns=None)¶

Return the trajectories’ line segments as GeoDataFrame.

Return type:: GeoDataFrame

to_point_gdf()¶

Return the trajectories’ points as GeoDataFrame.

Return type:: GeoDataFrame

to_traj_gdf(wkt=False, agg=False)¶

Return a GeoDataFrame with one row per Trajectory within the TrajectoryCollection

Return type:: GeoDataFrame

MovingPandas.TrajectoryCollection¶

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