OSM Traces (GPX files)¶

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This notebook illustrates the use of GPS traces shared publicly by OSM community members in GPX format.

In [1]:
import numpy as np
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import hvplot.pandas
import matplotlib.pyplot as plt

from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts, dim
from os.path import exists
from urllib.request import urlretrieve

import warnings

warnings.filterwarnings("ignore")

plot_defaults = {"linewidth": 5, "capstyle": "round", "figsize": (9, 3), "legend": True}
opts.defaults(
    opts.Overlay(active_tools=["wheel_zoom"], frame_width=500, frame_height=400)
)
hvplot_defaults = {"tiles": None, "cmap": "Viridis", "colorbar": True}

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

Download OSM traces and generate a GeoDataFrame¶

In [2]:
def get_osm_traces(page=0, bbox="16.18,48.09,16.61,48.32"):
    file = "osm_traces.gpx"
    url = f"https://api.openstreetmap.org/api/0.6/trackpoints?bbox={bbox}&page={page}"
    if not exists(file):
        urlretrieve(url, file)
    gdf = gpd.read_file(file, layer="track_points")
    # OPTIONAL: dropping empty columns
    gdf.drop(
        columns=[
            "ele",
            "course",
            "speed",
            "magvar",
            "geoidheight",
            "name",
            "cmt",
            "desc",
            "src",
            "url",
            "urlname",
            "sym",
            "type",
            "fix",
            "sat",
            "hdop",
            "vdop",
            "pdop",
            "ageofdgpsdata",
            "dgpsid",
        ],
        inplace=True,
    )
    return gdf

TrajectoryCollection from OSM traces GeoDataFrame¶

In [3]:
gdf = get_osm_traces()
osm_traces = mpd.TrajectoryCollection(gdf, "track_fid", t="time")
print(f"The OSM traces download contains {len(osm_traces)} tracks")
The OSM traces download contains 6 tracks
In [4]:
for track in osm_traces:
    print(f'Track {track.id}: length={track.get_length(units="km"):.2f} km')
Track 0: length=33.27 km
Track 1: length=0.06 km
Track 2: length=10.95 km
Track 3: length=33.27 km
Track 4: length=33.28 km
Track 5: length=2.58 km
In [5]:
track.plot()
Out[5]:
<Axes: >
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In [6]:
track.explore(color="red")
Out[6]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Generalizing and visualizing¶

Generalization is optional but speeds up rendering

In [7]:
osm_traces = mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(
    tolerance=timedelta(minutes=1)
)
osm_traces.hvplot(title="OSM Traces", line_width=7, width=700, height=400)
Out[7]:
In [8]:
osm_traces.explore(
    column="track_fid",
    tiles="cartoDB positron",
    cmap="gist_rainbow",
    style_kwds={"weight": 4},
)
Out[8]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [9]:
osm_traces.trajectories[0].add_speed(overwrite=True, units=("km", "h"))
osm_traces.trajectories[0].hvplot(
    title="Speed (km/h) along track",
    c="speed",
    cmap="RdYlBu",
    line_width=7,
    width=700,
    height=400,
    tiles="CartoLight",
    colorbar=True,
)
Out[9]:

Continue exploring MovingPandas¶

  1. Bird migration analysis
  2. Ship data analysis
  3. Horse collar data exploration
  4. OSM traces
  5. Soccer game
  6. Mars rover & heli
  7. Ever Given
  8. Iceberg
  9. Pollution data