In [1]:

import os
os.chdir("../")
import pandas as pd
import numpy as np
import dask.dataframe as dd

import geopandas as gpd
import shapely
from shapely.geometry import Point, LineString

import folium
from folium import plugins

In [2]:

path = "/Users/czhang/Downloads/AIS_2022_01_01.csv"
ddf = dd.read_csv(path,
                  dtype={
                      'Length': 'float64',
                      'VesselType': 'float64',
                      'Width': 'float64'
                  })
ddf.head(5)

Out[2]:

	MMSI	BaseDateTime	LAT	LON	SOG	COG	Heading	VesselName	IMO	CallSign	VesselType	Status	Length	Width	Draft	Cargo	TransceiverClass
0	368084090	2022-01-01T00:00:00	29.93174	-89.99243	6.0	296.2	299.0	LARRY B WHIPPLE	NaN	WDK7401	57.0	12.0	23.0	10.0	3.0	57.0	A
1	368140160	2022-01-01T00:00:00	30.33475	-87.14429	0.0	312.0	87.0	TWISTED ANGEL	IMO0000000	WDL5339	36.0	NaN	12.0	7.0	NaN	NaN	B
2	366941830	2022-01-01T00:00:02	29.30919	-94.79702	0.0	180.2	511.0	SAN PATRICIO	NaN	WCX6675	31.0	5.0	18.0	7.0	NaN	57.0	A
3	316005971	2022-01-01T00:00:06	46.50268	-84.35674	2.4	258.6	257.0	BEVERLY M I	IMO9084047	CFP2004	31.0	0.0	34.0	10.0	5.3	99.0	A
4	316004054	2022-01-01T00:00:07	46.50326	-84.37506	0.3	61.9	511.0	ADANAC III	IMO8745333	VCLT	31.0	0.0	24.0	5.0	3.0	50.0	A

In [3]:

sample_df = ddf[ddf.VesselType.isin([70,71,72])].compute()

In [4]:

sample_df = sample_df[sample_df.MMSI == 441486000]

In [5]:

sample_df = sample_df.reset_index().drop("index", axis=1)
sample_df["geometry"] = [Point(x, y) for x, y in zip(sample_df.LON, sample_df.LAT)]
gdf = gpd.GeoDataFrame(sample_df)

In [6]:

test_locs = [[b,a] for a, b in zip(gdf.LAT, gdf.LON)]
test_locs_re = [[a,b] for a, b in zip(gdf.LAT, gdf.LON)]
test_lst = []
for idx, val in enumerate(test_locs):
    test_dict = {}
    if idx < len(test_locs) -1:
        test_dict["coordinates"] = [val, test_locs[idx + 1]]
        test_dict["dates"] = [sample_df["BaseDateTime"][idx], sample_df["BaseDateTime"][idx+1]]
        test_dict["color"] = "red"
        test_lst.append(test_dict)

In [7]:

m = folium.plugins.DualMap([37.06237, -75.18991], zoom_start=3)
folium.plugins.AntPath(locations=test_locs_re,
                       reverse="True",
                       dash_array=[20, 30]).add_to(m.m1)
m.m1.fit_bounds(m.m1.get_bounds())


features = [{
    'type': 'Feature',
    'geometry': {
        'type': 'LineString',
        'coordinates': line['coordinates'],
    },
    'properties': {
        'times': line['dates'],
        'style': {
            'color': "blue",
            'weight': line['weight'] if 'weight' in line else 5
        }
    }
} for line in test_lst]

plugins.TimestampedGeoJson(
    {
        'type': 'FeatureCollection',
        'features': features,
    },
    period='PT1M',
    add_last_point=True).add_to(m.m2)

display(m)

Make this Notebook Trusted to load map: File -> Trust Notebook

An Alternative¶

In [7]:

import matplotlib.pyplot as plt
import matplotlib.animation as animation
import contextily as ctx

In [10]:

lat = np.array(sample_df["LAT"])
lon = np.array(sample_df["LON"])

fig, ax = plt.subplots(figsize=(12, 6))
ax.set_ylim(37.00, 37.24)
ax.set_xlim(-76.00, -74.74)
ax.set_axis_off()
ctx.add_basemap(ax, crs="EPSG:4326")
line, = ax.plot(lon, lat, lw=2)


def animate(n, lat, lon, line):
    line.set_data(lon[:n], lat[:n])
    return line,


anim = animation.FuncAnimation(fig, animate, len(lat), fargs=(lat, lon, line) , interval=20, blit=True)
anim.save('output/basic_animation.gif')

MovieWriter ffmpeg unavailable; using Pillow instead.