This section demonstrates integrating AIS data with external bathymetric data to enrich our analysis. In the following example, we identified all vessels within a 500-kilometer radius around the central area of Halifax, Canada, on January 1, 2018.
Raster file preparation
First, we imported the necessary packages and prepared the bathymetry data. It’s important to note that the downloaded bathymetric data is divided into eight segments, organized by latitude and longitude. In a later step, you will need to select the appropriate bathymetric raster file based on the geographical region covered by your vessel track data.
import os
import aisdb
import nest_asyncio
from datetime import datetime
from aisdb.database.dbconn import SQLiteDBConn
from aisdb import DBConn, DBQuery, DomainFromPoints
nest_asyncio.apply()
# set the path to the data storage directory
bathymetry_data_dir = "./bathymetry_data/"
# check if the directory exists
if not os.path.exists(bathymetry_data_dir):
os.makedirs(bathymetry_data_dir)
# check if the directory is empty
if os.listdir(bathymetry_data_dir) == []:
# download the bathymetry data
bathy = aisdb.webdata.bathymetry.Gebco(data_dir=bathymetry_data_dir)
bathy.fetch_bathymetry_grid()
else:
print("Bathymetry data already exists.")
Coloring the tracks
We defined a coloring criterion to classify tracks based on their average depths relative to the bathymetry. Tracks that traverse shallow waters with an average depth of less than 100 meters are colored in yellow. Those spanning depths between 100 and 1,000 meters are represented in orange, indicating a transition to deeper waters. As the depth increases, tracks reaching up to 20 kilometers are marked pink. The deepest tracks, descending beyond 20 kilometers, are distinctly colored in red.
def add_color(tracks):
for track in tracks:
# Calculate the average coastal distance
avg_coast_distance = sum(abs(dist) for dist in track['coast_distance']) / len(track['coast_distance'])
# Determine the color based on the average coastal distance
if avg_coast_distance <= 100:
track['color'] = "yellow"
elif avg_coast_distance <= 1000:
track['color'] = "orange"
elif avg_coast_distance <= 20000:
track['color'] = "pink"
else:
track['color'] = "red"
yield track
Integration with the bathymetric raster file
Next, we query the AIS data to be integrated with the bathymetric raster file and apply the coloring function to mark the tracks based on their average depths relative to the bathymetry.
dbpath = 'YOUR_DATABASE.db' # define the path to your database
end_time = datetime.strptime("2018-01-02 00:00:00", '%Y-%m-%d %H:%M:%S')
start_time = datetime.strptime("2018-01-01 00:00:00", '%Y-%m-%d %H:%M:%S')
domain = DomainFromPoints(points=[(-63.6, 44.6)], radial_distances=[500000])
with SQLiteDBConn(dbpath=dbpath) as dbconn:
qry = DBQuery(
dbconn=dbconn, start=start_time, end=end_time,
xmin=domain.boundary['xmin'], xmax=domain.boundary['xmax'],
ymin=domain.boundary['ymin'], ymax=domain.boundary['ymax'],
callback=aisdb.database.sqlfcn_callbacks.in_time_bbox_validmmsi,
)
tracks = aisdb.track_gen.TrackGen(qry.gen_qry(), decimate=False)
# Merge the tracks with the raster data
raster_path = "./bathymetry_data/gebco_2022_n90.0_s0.0_w-90.0_e0.0.tif"
raster = aisdb.webdata.load_raster.RasterFile(raster_path)
tracks_raster = raster.merge_tracks(tracks, new_track_key="coast_distance")
# Add color to the tracks
tracks_colored = add_color(tracks_raster)
if __name__ == '__main__':
aisdb.web_interface.visualize(
tracks_colored,
visualearth=True,
open_browser=True,
)
The integrated results are color-coded and can be visualized as shown below:
