In this page, we will see how we can use AISDb to discretize AIS tracks to hexagons.
Introduction
Hexagonal Geospatial Indexing (H3): Uber’s hierarchical hexagonal geospatial indexing system, partitions
the Earth into a multi-resolution hexagonal grid. Its key
advantage over square grids is the “one-distance rule,” where
all neighbors of a hexagon lie at comparable step distances.
As illustrated in the figure above, this uniformity removes the diagonal-versus-edge ambiguity present in square lattices. For maritime work, hexagons are great because they reduce directional bias and make neighborhood queries and aggregation intuitive.
Note: H3 indexes are 64-bit IDs typically shown as hex strings like “860e4d31fffffff.”
Discretize AIS Lat/Lon points to hexagons using AISDb
The code below provides a complete example of how to connect to a database of AIS data using AISDb and generate the corresponding H3 index for each data point.
import aisdb
from aisdb import DBQuery
from aisdb.database.dbconn import PostgresDBConn
from datetime import datetime, timedelta
from aisdb.discretize.h3 import Discretizer # main import to convert lat/lon to H3 indexes
# >>> PostgreSQL connection details (replace placeholders or use environment variables) <<<
db_user = '<>' # PostgreSQL username
db_dbname = '<>' # PostgreSQL database/schema name
db_password = '<>' # PostgreSQL password
db_hostaddr = '127.0.0.1' # PostgreSQL host (localhost shown)
# Create a database connection handle for AISDB to use
dbconn = PostgresDBConn(
port=5555, # PostgreSQL port (5432 is default; 5555 here is just an example)
user=db_user, # username for authentication
dbname=db_dbname, # database/schema to connect to
host=db_hostaddr, # host address or DNS name
password=db_password, # password for authentication
)
# ------------------------------
# Define the spatial and temporal query window
# Note: bbox is [xmin, ymin, xmax, ymax] in lon/lat; variables below help readability
xmin, ymin, xmax, ymax = -70, 45, -58, 53
gulf_bbox = [xmin, xmax, ymin, ymax] # optional helper; not used directly below
start_time = datetime(2023, 8, 1) # query start (inclusive)
end_time = datetime(2023, 8, 2) # query end (exclusive or inclusive per DB settings)
# Build a query that streams AIS rows in the time window and bounding box
qry = DBQuery(
dbconn=dbconn,
start=start_time, end=end_time,
xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
# Callback filters rows by time, bbox, and ensures MMSI validity (helps remove junk)
callback=aisdb.database.sqlfcn_callbacks.in_time_bbox_validmmsi
)
# Prepare containers/generators for streamed processing (memory-efficient)
ais_tracks = [] # placeholder list if you want to collect tracks (unused below)
rowgen = qry.gen_qry() # generator that yields raw AIS rows from the database
# ------------------------------
# Instantiate the H3 Discretizer at a chosen resolution
# Resolution 6 ≈ regional scale hexagons; increase for finer grids, decrease for coarser grids
# Note: variable name 'descritizer' is kept to match the original snippet (typo is harmless)
descritizer = Discretizer(resolution=6)
# Build tracks from rows; decimate=True reduces oversampling and speeds up processing
tracks = aisdb.track_gen.TrackGen(rowgen, decimate=True)
# Optionally split long tracks into time-bounded segments (e.g., 4-week chunks)
# Useful for chunked processing or time-based aggregation; not used further in this snippet
tracks_segment = aisdb.track_gen.split_timedelta(
tracks,
timedelta(weeks=4)
)
# Discretize each track: adds an H3 index array aligned with lat/lon points for that track
# Each yielded track will have keys like 'lat', 'lon', and 'h3_index'
tracks_with_indexes = descritizer.yield_tracks_discretized_by_indexes(tracks)
# Example (optional) usage:
# for t in tracks_with_indexes:
# # Access the first point's H3 index for this track
# print(t['mmsi'], t['timestamp'][0], t['lat'][0], t['lon'][0], t['h3_index'][0])
# break
# Output: H3 Index for lat 50.003334045410156, lon -66.76000213623047: 860e4d31fffffff
