MovingPandas Integration¶

This tutorial is derived from an upstream MovingPandas example.

We'll plot and animate AIS ship data published by the Danish Maritime Authority.

Dependencies¶

pip install lonboard movingpandas "geopandas>=1" pyarrow

Imports¶

In [1]:

from datetime import timedelta

import geopandas as gpd
import movingpandas as mpd
import pandas as pd
import pyarrow as pa

from lonboard import Map, PathLayer, viz
from lonboard.colormap import apply_categorical_cmap
from lonboard.experimental import TripsLayer

from datetime import timedelta import geopandas as gpd import movingpandas as mpd import pandas as pd import pyarrow as pa from lonboard import Map, PathLayer, viz from lonboard.colormap import apply_categorical_cmap from lonboard.experimental import TripsLayer

/Users/kyle/github/developmentseed/lonboard/.venv/lib/python3.11/site-packages/movingpandas/__init__.py:37: UserWarning: Missing optional dependencies. To use the trajectory smoother classes please install Stone Soup (see https://stonesoup.readthedocs.io/en/latest/#installation).
  warnings.warn(e.msg, UserWarning)

Loading sample AIS data¶

We'll load the same AIS data as the upstream notebook.

In [2]:

url = "https://raw.githubusercontent.com/movingpandas/movingpandas-examples/refs/heads/main/data/ais.gpkg"
gdf = gpd.read_file(url, use_arrow=True)

url = "https://raw.githubusercontent.com/movingpandas/movingpandas-examples/refs/heads/main/data/ais.gpkg" gdf = gpd.read_file(url, use_arrow=True)

Let's see what the data looks like:

In [3]:

gdf.head()

gdf.head()

Out[3]:

	Timestamp	MMSI	NavStatus	SOG	COG	Name	ShipType	geometry
0	05/07/2017 00:00:03	219632000	Under way using engine	0.0	270.4	None	Undefined	POINT (11.85958 57.68817)
1	05/07/2017 00:00:05	265650970	Under way using engine	0.0	0.5	None	Undefined	POINT (11.84175 57.66150)
2	05/07/2017 00:00:06	265503900	Under way using engine	0.0	0.0	None	Undefined	POINT (11.90650 57.69077)
3	05/07/2017 00:00:14	219632000	Under way using engine	0.0	188.4	None	Undefined	POINT (11.85958 57.68817)
4	05/07/2017 00:00:19	265519650	Under way using engine	0.0	357.2	None	Undefined	POINT (11.87192 57.68233)

And let's plot the raw data on a map. This plots each individual point from all ships.

In [4]:

viz(gdf)

viz(gdf)

Out[4]:

Map(basemap_style=<CartoBasemap.DarkMatter: 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'…

As with the upstream example, let's remove any records where the ships aren't moving.

In [5]:

print(f"Original size: {len(gdf)} rows")
gdf = gdf[gdf["SOG"] > 0]
print(f"Reduced to {len(gdf)} rows after removing 0 speed records")

print(f"Original size: {len(gdf)} rows") gdf = gdf[gdf["SOG"] > 0] print(f"Reduced to {len(gdf)} rows after removing 0 speed records")

Original size: 84702 rows
Reduced to 33593 rows after removing 0 speed records

Creating trajectories¶

This is drawn directly from the upstream example.

In [6]:

gdf["t"] = pd.to_datetime(gdf["Timestamp"], format="%d/%m/%Y %H:%M:%S")
traj_collection = mpd.TrajectoryCollection(gdf, "MMSI", t="t", min_length=100)
print(f"Finished creating {len(traj_collection)} trajectories")

gdf["t"] = pd.to_datetime(gdf["Timestamp"], format="%d/%m/%Y %H:%M:%S") traj_collection = mpd.TrajectoryCollection(gdf, "MMSI", t="t", min_length=100) print(f"Finished creating {len(traj_collection)} trajectories")

Finished creating 77 trajectories

In [7]:

traj_collection = mpd.MinTimeDeltaGeneralizer(traj_collection).generalize(
    tolerance=timedelta(minutes=1)
)

traj_collection = mpd.MinTimeDeltaGeneralizer(traj_collection).generalize( tolerance=timedelta(minutes=1) )

Plotting trajectories¶

In [8]:

shiptype_to_color = {
    "Passenger": "blue",
    "HSC": "green",
    "Tanker": "red",
    "Cargo": "orange",
    "Sailing": "grey",
    "Other": "grey",
    "Tug": "grey",
    "SAR": "grey",
    "Undefined": "grey",
    "Pleasure": "grey",
    "Dredging": "grey",
    "Law enforcement": "grey",
    "Pilot": "grey",
    "Fishing": "grey",
    "Diving": "grey",
    "Spare 2": "grey",
}

shiptype_to_color = { "Passenger": "blue", "HSC": "green", "Tanker": "red", "Cargo": "orange", "Sailing": "grey", "Other": "grey", "Tug": "grey", "SAR": "grey", "Undefined": "grey", "Pleasure": "grey", "Dredging": "grey", "Law enforcement": "grey", "Pilot": "grey", "Fishing": "grey", "Diving": "grey", "Spare 2": "grey", }

We'll extract the ship type from each trajectory. In general, you'd expect this to be consistent, but in this dataset apparently some trajectories have multiple values for this, so we'll take the first unique value.

In [9]:

ship_types = []
for traj in traj_collection.trajectories:
    unique_vals = traj.df["ShipType"].unique()
    # Apparently this is not always consistent
    # assert len(unique_vals) == 1, "Expected single ship type per trajectory"
    ship_types.append(unique_vals[0])

ship_types = [] for traj in traj_collection.trajectories: unique_vals = traj.df["ShipType"].unique() # Apparently this is not always consistent # assert len(unique_vals) == 1, "Expected single ship type per trajectory" ship_types.append(unique_vals[0])

Now we'll use apply_categorical_cmap to get colors from this data and color mapping.

In [10]:

get_color = apply_categorical_cmap(pa.array(ship_types), shiptype_to_color)

get_color = apply_categorical_cmap(pa.array(ship_types), shiptype_to_color)

Then we can pass our data to TripsLayer.from_movingpandas:

In [11]:

trips_layer = TripsLayer.from_movingpandas(
    traj_collection, get_color=get_color, width_min_pixels=5, trail_length = 200
)

trips_layer = TripsLayer.from_movingpandas( traj_collection, get_color=get_color, width_min_pixels=5, trail_length = 200 )

/Users/kyle/github/developmentseed/lonboard/lonboard/experimental/traits.py:146: UserWarning: Reducing precision of input timestamp data to 's' to fit into available GPU precision.
  warnings.warn(

In [12]:

m = Map(trips_layer, _height=600)
m

m = Map(trips_layer, _height=600) m

Out[12]:

Map(custom_attribution='', layers=(TripsLayer(get_color=arro3.core.ChunkedArray<FixedSizeList(Field { name: ""…

Now we can animate our data! Click the play button after running the following cell to watch the animation:

In [13]:

trips_layer.animate(step=timedelta(seconds=30), fps=50)

trips_layer.animate(step=timedelta(seconds=30), fps=50)

Out[13]:

HBox(children=(Play(value=-16777216, interval=20, max=-16690894, min=-16777216, repeat=True, step=30), Output(…

Adding the full trajectories¶

We can use the same Arrow data from the above TripsLayer and pass that into a PathLayer. The Arrow data from the TripsLayer is accessed with trips_layer.table and can be passed directly into the table argument of the PathLayer.

We'll set the opacity pretty low as we want the full trajectories to be background information:

In [14]:

linestring_layer = PathLayer(
    table=trips_layer.table, get_color=get_color, width_min_pixels=1, opacity=0.005
)

linestring_layer = PathLayer( table=trips_layer.table, get_color=get_color, width_min_pixels=1, opacity=0.005 )

And we'll add that layer to the existing map:

In [15]:

m.add_layer(linestring_layer)

m.add_layer(linestring_layer)

In [ ]: