Getting started with MovingPandas¶

No description has been provided for this image

Binder IPYNB HTML

MovingPandas provides a trajectory datatype based on GeoPandas. The project home is at https://github.com/movingpandas/movingpandas

The documentation is available at https://movingpandas.readthedocs.io/

In [1]:
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import hvplot.pandas

from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts

import warnings

warnings.filterwarnings("ignore")

opts.defaults(
    opts.Overlay(active_tools=["wheel_zoom"], frame_width=500, frame_height=400)
)

mpd.show_versions()

MovingPandas 0.20.0

SYSTEM INFO
-----------
python     : 3.10.15 | packaged by conda-forge | (main, Oct 16 2024, 01:15:49) [MSC v.1941 64 bit (AMD64)]
executable : c:\Users\Agarkovam\AppData\Local\miniforge3\envs\mpd-ex\python.exe
machine    : Windows-10-10.0.19045-SP0

GEOS, GDAL, PROJ INFO
---------------------
GEOS       : None
GEOS lib   : None
GDAL       : None
GDAL data dir: None
PROJ       : 9.5.0
PROJ data dir: C:\Users\Agarkovam\AppData\Local\miniforge3\envs\mpd-ex\Library\share\proj

PYTHON DEPENDENCIES
-------------------
geopandas  : 1.0.1
pandas     : 2.2.3
fiona      : None
numpy      : 1.23.1
shapely    : 2.0.6
pyproj     : 3.7.0
matplotlib : 3.9.2
mapclassify: 2.8.1
geopy      : 2.4.1
holoviews  : 1.20.0
hvplot     : 0.11.1
geoviews   : 1.13.0
stonesoup  : 1.4

Creating a trajectory from scratch¶

Trajectory objects consist of a trajectory ID and a GeoPandas GeoDataFrame with a DatetimeIndex. The data frame therefore represents the trajectory data as a Pandas time series with associated point locations (and optional further attributes).

Let's create a small toy trajectory to see how this works:

In [2]:
df = pd.DataFrame(
    [
        {"geometry": Point(0, 0), "t": datetime(2018, 1, 1, 12, 0, 0)},
        {"geometry": Point(6, 0), "t": datetime(2018, 1, 1, 12, 6, 0)},
        {"geometry": Point(6, 6), "t": datetime(2018, 1, 1, 12, 10, 0)},
        {"geometry": Point(9, 9), "t": datetime(2018, 1, 1, 12, 15, 0)},
    ]
).set_index("t")
gdf = GeoDataFrame(df, crs=31256)
toy_traj = mpd.Trajectory(gdf, 1)
toy_traj
Out[2]:
Trajectory 1 (2018-01-01 12:00:00 to 2018-01-01 12:15:00) | Size: 4 | Length: 16.2m
Bounds: (0.0, 0.0, 9.0, 9.0)
LINESTRING (0 0, 6 0, 6 6, 9 9)
In [3]:
toy_traj.plot()
Out[3]:
<Axes: >
No description has been provided for this image
In [4]:
toy_traj.hvplot()
Out[4]:

We can also access the trajectory's GeoDataFrame:

In [5]:
toy_traj.df
Out[5]:
geometry traj_id
t
2018-01-01 12:00:00 POINT (0 0) 1
2018-01-01 12:06:00 POINT (6 0) 1
2018-01-01 12:10:00 POINT (6 6) 1
2018-01-01 12:15:00 POINT (9 9) 1
In [6]:
toy_traj.df.plot()
Out[6]:
<Axes: >
No description has been provided for this image

Loading trajectories from CSV¶

In [7]:
df = pd.read_csv("../data/geolife_small.csv", delimiter=";")
df
Out[7]:
X Y fid id sequence trajectory_id tracker t
0 116.391305 39.898573 1 1 1 1 19 2008-12-11 04:42:14+00
1 116.391317 39.898617 2 2 2 1 19 2008-12-11 04:42:16+00
2 116.390928 39.898613 3 3 3 1 19 2008-12-11 04:43:26+00
3 116.390833 39.898635 4 4 4 1 19 2008-12-11 04:43:32+00
4 116.389410 39.898723 5 5 5 1 19 2008-12-11 04:43:47+00
... ... ... ... ... ... ... ... ...
5903 116.337194 39.926232 6993 6993 867 5 2 2009-02-25 14:31:04+00
5904 116.337207 39.926237 6994 6994 868 5 2 2009-02-25 14:31:09+00
5905 116.337259 39.926207 6995 6995 869 5 2 2009-02-25 14:31:14+00
5906 116.337290 39.926201 6996 6996 870 5 2 2009-02-25 14:31:19+00
5907 116.337332 39.926186 6997 6997 871 5 2 2009-02-25 14:31:24+00

5908 rows × 8 columns

In [8]:
traj_collection = mpd.TrajectoryCollection(df, "trajectory_id", t="t", x="X", y="Y")
print(traj_collection)

TrajectoryCollection with 5 trajectories
In [9]:
traj_collection.plot(column="trajectory_id", legend=True, figsize=(9, 5))
Out[9]:
<Axes: >
No description has been provided for this image

Loading trajectories from a GeoPackage¶

In [10]:
gdf = read_file("../data/geolife_small.gpkg")
gdf
Out[10]:
id sequence trajectory_id tracker t geometry
0 1 1 1 19 2008-12-11 04:42:14+00 POINT (116.3913 39.89857)
1 2 2 1 19 2008-12-11 04:42:16+00 POINT (116.39132 39.89862)
2 3 3 1 19 2008-12-11 04:43:26+00 POINT (116.39093 39.89861)
3 4 4 1 19 2008-12-11 04:43:32+00 POINT (116.39083 39.89864)
4 5 5 1 19 2008-12-11 04:43:47+00 POINT (116.38941 39.89872)
... ... ... ... ... ... ...
5903 6993 867 5 2 2009-02-25 14:31:04+00 POINT (116.33719 39.92623)
5904 6994 868 5 2 2009-02-25 14:31:09+00 POINT (116.33721 39.92624)
5905 6995 869 5 2 2009-02-25 14:31:14+00 POINT (116.33726 39.92621)
5906 6996 870 5 2 2009-02-25 14:31:19+00 POINT (116.33729 39.9262)
5907 6997 871 5 2 2009-02-25 14:31:24+00 POINT (116.33733 39.92619)

5908 rows × 6 columns

After reading the trajectory point data from file, we want to construct the trajectories.

In [11]:
traj_collection = mpd.TrajectoryCollection(gdf, "trajectory_id", t="t")
print(traj_collection)

TrajectoryCollection with 5 trajectories
In [12]:
traj_collection.plot(column="trajectory_id", legend=True, figsize=(9, 5))
Out[12]:
<Axes: >
No description has been provided for this image
In [13]:
traj_collection.plot()
Out[13]:
<Axes: >
No description has been provided for this image

To visualize trajectories in their geographical context, we can also create interactive plots with basemaps:

In [14]:
traj_collection.explore(column="trajectory_id", cmap="plasma", tiles="CartoDB positron")
Out[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Accessing individual trajectories¶

In [15]:
my_traj = traj_collection.trajectories[1]
print(my_traj)

Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38764.6m
Bounds: (116.319212, 39.971703, 116.592616, 40.082514)
LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004,
In [16]:
my_traj.plot(linewidth=5, capstyle="round", figsize=(9, 3))
Out[16]:
<Axes: >
No description has been provided for this image

To visualize trajectories in their geographical context, we can again create interactive plots with basemaps:

In [17]:
my_traj.hvplot(line_width=7.0, tiles="OSM")
Out[17]:
In [18]:
my_traj.explore(tiles="CartoDB positron", style_kwds={"weight": 4})
Out[18]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Creating a TrajectoryCollection from scratch¶

In [19]:
df = pd.DataFrame(
    {
        "t": pd.date_range("2020-01-01", periods=5, freq="min"),
        "trajectory_id": [1, 1, 2, 2, 2],
        "geometry": [Point(0, 0), Point(0, 1), Point(1, 2), Point(1, 3), Point(2, 4)],
    }
)
gdf = gpd.GeoDataFrame(df, crs=4326)
tc = mpd.TrajectoryCollection(gdf, traj_id_col="trajectory_id", t="t")
tc
Out[19]:
TrajectoryCollection with 2 trajectories
In [20]:
tc.hvplot()
Out[20]:

We can filter out trajectories based on their duration:

In [21]:
min_duration = timedelta(minutes=1)
tc.trajectories = [traj for traj in tc if traj.get_duration() > min_duration]
tc
Out[21]:
TrajectoryCollection with 1 trajectories
In [ ]: