Exploring AIS vessel-traffic data¶
This Jupyter notebook demonstrates how to use the Datashader-based rendering in HoloViews to explore and analyze US Coast Guard Automatic Identification System (AIS) vessel-location data. AIS data includes vessels identified by their Maritime Mobile Service Identity numbers along with other data such as vessel type. Data is provided here for January 2020 (200 million datapoints), but additional months and years of data can be downloaded for US coastal areas from marinecadastre.gov, and with slight modifications the same code here should work for AIS data available for other regions. This notebook also illustrates a workflow for visualizing large categorical datasets in general, letting users interact with individual datapoints even though the data itself is never sent to the browser for plotting.
import os, requests, numpy as np, pandas as pd, holoviews as hv, holoviews.operation.datashader as hd import dask.dataframe as dd, panel as pn, colorcet as cc, datashader as ds import spatialpandas as sp, spatialpandas.io, spatialpandas.geometry, spatialpandas.dask from PIL import Image from holoviews.util.transform import lon_lat_to_easting_northing as ll2en from dask.diagnostics import ProgressBar hv.extension('bokeh', 'matplotlib', width=100)