AIS Data Exploration

AIS Data Exploration#

First notebook exploring coverage of AIS data. Data was extracted from the UN Global Platform (source data is from Spire / Exact Earth)

  • Coverage: December 2018 - August 2022

  • Ports: Tartus, Baniyas, Al Ladhiqiyah. 20 km buffer around each port

image.png

import os
from os.path import join
from glob import glob
import pandas as pd
import geopandas as gpd
import folium
from shapely.geometry import Point
import folium.plugins as plugins
import seaborn as sns
from matplotlib import pyplot as plt

pd.options.display.max_columns = None

ais_dir = join(os.path.expanduser("~"), "data", "AIS")
data_dir = join(ais_dir, "Syria")

data_files = glob(data_dir + "/*.csv")

dfs = [pd.read_csv(f, index_col=0) for f in data_files]

df = pd.concat(dfs)

df.head(2)
H3_int_index_6 message_type mmsi dt_insert_utc longitude latitude imo vessel_name callsign vessel_type vessel_type_code vessel_type_cargo vessel_class length width flag_country flag_code destination eta draught sog cog rot heading nav_status nav_status_code source dt_pos_utc dt_static_utc vessel_type_main vessel_type_sub eeid source_filename H3index_0 H3_int_index_0 H3_int_index_1 H3_int_index_2 H3_int_index_3 H3_int_index_4 H3_int_index_5 H3_int_index_7 H3_int_index_8 H3_int_index_9 H3_int_index_10 H3_int_index_11 H3_int_index_12 H3_int_index_13 H3_int_index_14 H3_int_index_15 polygon_name hex_resolution
90000 604285236087357439 1 355909000.0 2020-08-31 23:06:45 35.768167 35.518667 9037276.0 BEATA 3FZK3 Cargo NaN NaN A 130.0 18.0 Panama NaN SY ALD 8271300.0 8.0 0.0 180.0 0.0 197.0 Under Way Using Engine 0.0 T-AIS 2020-08-31 23:06:45 2020-08-31 23:06:45 Oil And Chemical Tanker Chemical Tanker NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835681173503 613292435295961087 617796034922020863 622299634549325823 626803234176679935 631306833804357119 635810433431727551 640314033059097991 644817632686468486 AL LADHIQIYAH 6
90001 604285236087357439 1 355909000.0 2020-08-31 22:11:15 35.768000 35.518833 9037276.0 BEATA 3FZK3 Cargo NaN NaN A 130.0 18.0 Panama NaN SY ALD 8271300.0 8.0 0.0 26.0 0.0 197.0 Under Way Using Engine 0.0 T-AIS 2020-08-31 22:11:15 2020-08-31 22:11:15 Oil And Chemical Tanker Chemical Tanker NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835681173503 613292435295961087 617796034922020863 622299634549325823 626803234176671743 631306833804040191 635810433431410239 640314033058780711 644817632686151200 AL LADHIQIYAH 6 
df.columns

Index(['H3_int_index_6', 'message_type', 'mmsi', 'dt_insert_utc', 'longitude',
       'latitude', 'imo', 'vessel_name', 'callsign', 'vessel_type',
       'vessel_type_code', 'vessel_type_cargo', 'vessel_class', 'length',
       'width', 'flag_country', 'flag_code', 'destination', 'eta', 'draught',
       'sog', 'cog', 'rot', 'heading', 'nav_status', 'nav_status_code',
       'source', 'dt_pos_utc', 'dt_static_utc', 'vessel_type_main',
       'vessel_type_sub', 'eeid', 'source_filename', 'H3index_0',
       'H3_int_index_0', 'H3_int_index_1', 'H3_int_index_2', 'H3_int_index_3',
       'H3_int_index_4', 'H3_int_index_5', 'H3_int_index_7', 'H3_int_index_8',
       'H3_int_index_9', 'H3_int_index_10', 'H3_int_index_11',
       'H3_int_index_12', 'H3_int_index_13', 'H3_int_index_14',
       'H3_int_index_15', 'polygon_name', 'hex_resolution'],
      dtype='object')

Check unique vessel ids

df.loc[df.loc[:, "mmsi"].isna(), "mmsi"] = -1

df.loc[:, "mmsi"] = df.loc[:, "mmsi"].astype("int")

len(df), len(df.mmsi.unique())

(98662, 498)

Ports from WPI

wpi = pd.read_csv(join(ais_dir, "wpi", "UpdatedPub150.csv"))

wpi = wpi.loc[wpi["Country Code"] == "Syria"].copy()

geoms = [Point(xy) for xy in zip(wpi.Longitude, wpi.Latitude)]
wpi_gdf = gpd.GeoDataFrame(wpi, crs="EPSG:4326", geometry=geoms)

wpi_gdf = wpi_gdf.to_crs("EPSG:32636")

wpi_gdf.loc[:, "geometry"] = wpi_gdf.buffer(20000)  # 10000

wpi_gdf = wpi_gdf.to_crs("EPSG:4326")

wpi_gdf
World Port Index Number Region Name Main Port Name Alternate Port Name UN/LOCODE Country Code World Water Body IHO S-130 Sea Area Sailing Direction or Publication Standard Nautical Chart ... Supplies - Diesel Oil Supplies - Aviation Fuel Supplies - Deck Supplies - Engine Repairs Dry Dock Railway Latitude Longitude geometry
422 45003 Syria -- 45000 Tartus SY TTS Syria Mediterranean Sea; North Atlantic Ocean Sailing Directions Pub. 132 (Enroute) - Easter... 56048 ... Unknown Unknown Unknown Unknown Limited Medium Medium 34.900000 35.866667 POLYGON ((36.08527 34.89464, 36.08356 34.87702...
861 45005 Syria -- 45000 Baniyas SY BAN Syria Mediterranean Sea; North Atlantic Ocean Sailing Directions Pub. 132 (Enroute) - Easter... 56047 ... Yes Unknown Unknown Unknown Limited Unknown Unknown 35.183333 35.950000 POLYGON ((36.16934 35.17779, 36.16760 35.16016...
1367 45010 Syria -- 45000 Al Ladhiqiyah Latakia SY ALD Syria Mediterranean Sea; North Atlantic Ocean Sailing Directions Pub. 132 (Enroute) - Easter... 56048 ... Yes Unknown Yes Yes Limited Large Medium 35.533333 35.766667 POLYGON ((35.98700 35.52807, 35.98528 35.51045...

3 rows × 108 columns

wpi_gdf.loc[:, "color"] = ["blue", "orange", "green"]

def style_function(feature):
    idx = int(feature["id"])
    color = wpi_gdf.loc[idx, "color"]
    return {"fillOpacity": 0.2, "weight": 0, "fillColor": color}

gdf_col = wpi_gdf.geometry.envelope.__geo_interface__

# df_sample = df.head(1000).copy()
df_sample = df.copy()

df_sample.loc[:, "bool"] = 1

[lat, lon] = buffer_df[["Latitude", "Longitude"]].iloc[0].tolist()
m = folium.Map(
    # location = [-8.102782	,156.833900	],
    location=[lat, lon],
    tiles="cartodbpositron",
    zoom_start=8,
)
a = plugins.HeatMap(
    df_sample[["latitude", "longitude"]].values, radius=2, blur=2, name="HeatMap"
).add_to(m)
a = folium.GeoJson(gdf_col, name="BBox", style_function=style_function).add_to(m)
folium.LayerControl().add_to(m)
m
Make this Notebook Trusted to load map: File -> Trust Notebook
df_mmsi = df.copy()

df_mmsi.drop_duplicates("mmsi", inplace=True)

len(df_mmsi)

498

list(df_mmsi.columns)

['H3_int_index_6',
 'message_type',
 'mmsi',
 'dt_insert_utc',
 'longitude',
 'latitude',
 'imo',
 'vessel_name',
 'callsign',
 'vessel_type',
 'vessel_type_code',
 'vessel_type_cargo',
 'vessel_class',
 'length',
 'width',
 'flag_country',
 'flag_code',
 'destination',
 'eta',
 'draught',
 'sog',
 'cog',
 'rot',
 'heading',
 'nav_status',
 'nav_status_code',
 'source',
 'dt_pos_utc',
 'dt_static_utc',
 'vessel_type_main',
 'vessel_type_sub',
 'eeid',
 'source_filename',
 'H3index_0',
 'H3_int_index_0',
 'H3_int_index_1',
 'H3_int_index_2',
 'H3_int_index_3',
 'H3_int_index_4',
 'H3_int_index_5',
 'H3_int_index_7',
 'H3_int_index_8',
 'H3_int_index_9',
 'H3_int_index_10',
 'H3_int_index_11',
 'H3_int_index_12',
 'H3_int_index_13',
 'H3_int_index_14',
 'H3_int_index_15',
 'polygon_name',
 'hex_resolution']

df_mmsi.vessel_type.value_counts()

Cargo              373
Tanker              47
Tug                 18
Unknown             13
UNAVAILABLE         13
Fishing              8
Other                5
Passenger            5
Towing               4
SAR                  2
WIG                  2
Port Tender          2
Reserved             1
Dredging             1
Diving               1
Law Enforcement      1
Pleasure Craft       1
Pilot                1
Name: vessel_type, dtype: int64

df_mmsi.vessel_type_main.value_counts()

General Cargo Ship         107
Bulk Carrier                67
Container Ship              49
Oil And Chemical Tanker     26
Ro Ro Cargo Ship            16
Specialized Cargo Ship      11
Tug                          8
Fishing Vessel               4
Service Ship                 4
Offshore Vessel              3
Other Tanker                 3
Gas Tanker                   3
Other                        1
Pleasure Craft               1
Name: vessel_type_main, dtype: int64

df_mmsi.flag_country.value_counts()

Togo                                61
Panama                              48
Comoros                             35
Turkey                              33
Russian Federation                  31
Liberia                             25
Malta                               24
Palau                               16
Marshall Islands                    16
Belize                              15
Tanzania                            15
Iran                                12
Sierra Leone                        11
Antigua and Barbuda                 11
Japan                               11
Egypt                               10
Moldova                             10
Lebanon                              8
Italy                                7
Cyprus                               7
Norway                               7
Syria                                7
Singapore                            6
Saint Kitts and Nevis                5
Cameroon                             5
South Korea                          5
China                                5
USA                                  4
Bahamas                              4
Mozambique                           3
Netherlands                          3
Guyana                               3
Saint Vincent and the Grenadines     2
Gibraltar                            2
Greece                               2
Hong Kong                            2
Cayman Islands                       2
Denmark                              2
Cook Islands                         2
Vanuatu                              2
Tuvalu                               2
Germany                              1
Taiwan                               1
Barbados                             1
Ireland                              1
Mongolia                             1
Equatorial Guinea                    1
Poland                               1
Zambia                               1
Luxembourg                           1
Brazil                               1
Honduras                             1
Spain                                1
Madeira                              1
Israel                               1
Name: flag_country, dtype: int64

df_mmsi.loc[df_mmsi.flag_country == "Togo", "mmsi"]

90067    671350000
90071    671700000
90088    671032000
90216    671158100
90270    671259100
           ...    
61456    671211100
71028    671218100
73071    671265000
76527    671662000
88155    671456000
Name: mmsi, Length: 61, dtype: int64

df.loc[
    df.mmsi == 671700000,
    [
        "message_type",
        "mmsi",
        "dt_insert_utc",
        "longitude",
        "latitude",
        "imo",
        "vessel_name",
        "callsign",
        "vessel_type",
        "vessel_type_code",
        "vessel_type_cargo",
        "vessel_class",
        "length",
        "width",
        "flag_country",
        "flag_code",
        "destination",
        "eta",
        "draught",
        "sog",
        "cog",
        "rot",
        "heading",
        "nav_status",
        "nav_status_code",
        "source",
        "dt_pos_utc",
        "dt_static_utc",
        "vessel_type_main",
        "vessel_type_sub",
        "eeid",
    ],
].head()
message_type mmsi dt_insert_utc longitude latitude imo vessel_name callsign vessel_type vessel_type_code vessel_type_cargo vessel_class length width flag_country flag_code destination eta draught sog cog rot heading nav_status nav_status_code source dt_pos_utc dt_static_utc vessel_type_main vessel_type_sub eeid
90071 27 671700000 2021-11-15 18:28:13 35.87 34.901667 7405089.0 PRINCESS HIYAM 5VDW8 Unknown 100.0 NaN A 79.0 13.0 Togo 671.0 NaN 2460.0 0.0 0.0 248.0 0.0 0.0 Moored 5.0 S-AIS 2021-11-15 18:27:55 2021-10-12 18:33:34 Specialized Cargo Ship Livestock Carrier 4.686739e+18
90072 27 671700000 2021-11-15 06:10:16 35.87 34.901667 7405089.0 PRINCESS HIYAM 5VDW8 Unknown 100.0 NaN A 79.0 13.0 Togo 671.0 NaN 2460.0 0.0 0.0 289.0 0.0 0.0 Moored 5.0 S-AIS 2021-11-15 06:09:57 2021-10-12 18:33:34 Specialized Cargo Ship Livestock Carrier 4.686739e+18
90073 27 671700000 2021-11-15 04:16:17 35.87 34.901667 7405089.0 PRINCESS HIYAM 5VDW8 Unknown 100.0 NaN A 79.0 13.0 Togo 671.0 NaN 2460.0 0.0 0.0 292.0 0.0 0.0 Moored 5.0 S-AIS 2021-11-15 04:15:54 2021-10-12 18:33:34 Specialized Cargo Ship Livestock Carrier 4.686739e+18
90074 27 671700000 2021-11-15 04:58:13 35.87 34.901667 7405089.0 PRINCESS HIYAM 5VDW8 Unknown 100.0 NaN A 79.0 13.0 Togo 671.0 NaN 2460.0 0.0 0.0 261.0 0.0 0.0 Moored 5.0 S-AIS 2021-11-15 04:57:56 2021-10-12 18:33:34 Specialized Cargo Ship Livestock Carrier 4.686739e+18
90477 27 671700000 2021-11-16 05:13:11 35.87 34.901667 7405089.0 PRINCESS HIYAM 5VDW8 Unknown 100.0 NaN A 79.0 13.0 Togo 671.0 NaN 2460.0 0.0 0.0 281.0 0.0 0.0 Moored 5.0 S-AIS 2021-11-16 05:12:58 2021-10-12 18:33:34 Specialized Cargo Ship Livestock Carrier 4.686739e+18 
df.loc[:, "dt_pos_utc"] = pd.to_datetime(df.dt_insert_utc)

df.loc[:, "day"] = df.loc[:, "dt_pos_utc"].dt.strftime("%Y-%m-%d")
df.loc[:, "month"] = df.loc[:, "dt_pos_utc"].dt.strftime("%Y-%m")

df[["mmsi", "day", "month"]].head()
mmsi day month
90000 355909000 2020-08-31 2020-08
90001 355909000 2020-08-31 2020-08
90002 355909000 2020-08-31 2020-08
90003 355909000 2020-08-31 2020-08
90004 355909000 2020-08-31 2020-08 
df_raw = df.copy()

df = df_raw.copy()

df_mmsi.vessel_type.unique()

array(['Cargo', 'Tug', 'Unknown', 'Tanker', 'Towing', 'UNAVAILABLE',
       'SAR', 'Other', 'Reserved', 'Passenger', 'Fishing', 'Dredging',
       'WIG', 'Diving', 'Law Enforcement', 'Port Tender',
       'Pleasure Craft', 'Pilot'], dtype=object)

df_mmsi.vessel_type_main.unique()

array(['Oil And Chemical Tanker', 'Container Ship', nan,
       'General Cargo Ship', 'Bulk Carrier', 'Specialized Cargo Ship',
       'Ro Ro Cargo Ship', 'Offshore Vessel', 'Other', 'Tug',
       'Other Tanker', 'Fishing Vessel', 'Service Ship', 'Gas Tanker',
       'Pleasure Craft'], dtype=object)

accepted = ["Cargo"]
# accepted = ['Oil And Chemical Tanker', 'Container Ship',
#        'General Cargo Ship', 'Bulk Carrier', 'Specialized Cargo Ship',
#        'Ro Ro Cargo Ship', 'Other Tanker', 'Gas Tanker']

df = df.loc[df.vessel_type.isin(accepted)].copy()
# df = df.loc[df.vessel_type_main.isin(accepted)].copy()

df.vessel_type_main.unique()

array(['Oil And Chemical Tanker', 'Container Ship', nan,
       'General Cargo Ship', 'Bulk Carrier', 'Ro Ro Cargo Ship',
       'Specialized Cargo Ship', 'Offshore Vessel', 'Other',
       'Service Ship', 'Fishing Vessel', 'Tug'], dtype=object)

df_raw.nav_status.value_counts()

Moored                        43015
Under Way Using Engine        27537
At Anchor                     22841
Not Under Command              2823
Underway Sailing               1653
Restricted Manoeuvrability      565
Not Defined                     160
Engaged In Fishing               41
Unknown                          27
Name: nav_status, dtype: int64

df.sort_values("day").head()
H3_int_index_6 message_type mmsi dt_insert_utc longitude latitude imo vessel_name callsign vessel_type vessel_type_code vessel_type_cargo vessel_class length width flag_country flag_code destination eta draught sog cog rot heading nav_status nav_status_code source dt_pos_utc dt_static_utc vessel_type_main vessel_type_sub eeid source_filename H3index_0 H3_int_index_0 H3_int_index_1 H3_int_index_2 H3_int_index_3 H3_int_index_4 H3_int_index_5 H3_int_index_7 H3_int_index_8 H3_int_index_9 H3_int_index_10 H3_int_index_11 H3_int_index_12 H3_int_index_13 H3_int_index_14 H3_int_index_15 polygon_name hex_resolution day month
0 604285235818921983 1 312945000 2018-12-02 19:31:21 35.759750 35.526542 9197935.0 TRUEBORN V3XY4 Cargo NaN NaN A 171.0 27.0 Belize NaN ODESA 12271800.0 6.0 0.1 241.3 0.0 78.0 Under Way Using Engine 0.0 S-AIS 2018-12-02 19:31:21 2018-12-02 19:31:21 Bulk Carrier NaN NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835681173503 613292435304349695 617796034930147327 622299634557386751 626803234184744959 631306833812112895 635810433439485183 640314033066853679 644817632694224173 AL LADHIQIYAH 6 2018-12-02 2018-12
1 604285235818921983 1 312945000 2018-12-03 07:09:27 35.759750 35.526500 9197935.0 TRUEBORN V3XY4 Cargo NaN NaN A 171.0 27.0 Belize NaN POR SAID 12271800.0 6.0 0.0 309.0 0.0 78.0 Under Way Using Engine 0.0 S-AIS 2018-12-03 07:09:27 2018-12-03 07:09:27 Bulk Carrier NaN NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835681173503 613292435304349695 617796034930147327 622299634557386751 626803234184744959 631306833812112895 635810433439483199 640314033066853639 644817632694224134 AL LADHIQIYAH 6 2018-12-03 2018-12
3 604285236087357439 1 312945000 2018-12-05 10:00:35 35.767700 35.517750 9197935.0 TRUEBORN V3XY4 Cargo NaN NaN A 171.0 27.0 Belize NaN POR SAID 12271800.0 6.0 0.0 141.0 0.0 198.0 Under Way Using Engine 0.0 S-AIS 2018-12-05 10:00:35 2018-12-05 10:00:35 Bulk Carrier NaN NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835681173503 613292435295961087 617796034922020863 622299634549161983 626803234176507903 631306833803876863 635810433431246911 640314033058617391 644817632685987886 AL LADHIQIYAH 6 2018-12-05 2018-12
2 604285235818921983 3 636092669 2018-12-05 18:37:52 35.768033 35.526600 9430870.0 HANSA NEUBURG D5KB8 Cargo NaN NaN A 175.0 27.0 Liberia NaN PIRAEUS 12281400.0 7.9 0.0 101.0 0.0 228.0 Moored 5.0 S-AIS 2018-12-05 18:37:52 2018-12-05 18:37:52 Container Ship NaN NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835362406399 613292434987679743 617796034613739519 622299634240913407 626803233868279807 631306833495649279 635810433123019391 640314032750389855 644817632377760348 AL LADHIQIYAH 6 2018-12-05 2018-12
4 604285235818921983 1 271044633 2018-12-06 23:16:32 35.735950 35.536378 9106479.0 MED CORLU TCA4362 Cargo NaN NaN A 0.0 0.0 Turkey NaN LBBEY 12302200.0 6.1 3.9 74.6 0.0 92.0 Under Way Using Engine 0.0 S-AIS 2018-12-06 23:16:32 2018-12-06 23:16:32 Container Ship NaN NaN s3a://ungp-ais-data-historical-backup/exact-ea... 802dfffffffffff 577269992861466623 581769194442326015 586271144802254847 590774469551718399 595278043409285119 599781636594204671 608788835429515263 613292435054788607 617796034681634815 622299634308775935 626803233936117759 631306833563487231 635810433190857535 640314032818227999 644817632445598494 AL LADHIQIYAH 6 2018-12-06 2018-12 
# df_port = df[['mmsi', 'day', 'polygon_name']].groupby(['polygon_name', 'day']).nunique()
df_port = (
    df[["mmsi", "month", "polygon_name"]].groupby(["polygon_name", "month"]).nunique()
)

df_port.reset_index(inplace=True)

# df_port.loc[:, 'date'] = pd.to_datetime(df_port.day)
df_port.loc[:, "date"] = pd.to_datetime(df_port.month)

df_port = df_port.loc[df_port.polygon_name != "BANIYAS"]

# sns.set(font_scale = 1.5) # sns.set_style("white")
sns.set_theme(style="whitegrid", font_scale=1.5)

fig, ax = plt.subplots(figsize=(11, 6))
ax = sns.lineplot(data=df_port, x="date", y="mmsi", hue="polygon_name", palette="tab10")
ax.set(xlabel="", ylabel="Unique MMSI", title="Number of unique vessels per month")
plt.legend(title="")  # loc='upper right', labels=['Raw Data', 'Interpolated']
# plt.savefig("../docs/images/Output_Mining_Gas.png", facecolor='white', dpi=300)

<matplotlib.legend.Legend at 0x7f5614034f10>
../../_images/4ac806138c650d9a00a693412b6bd7d55ed6629e15be7f8f66935f1bb9555422.png 
fig, ax = plt.subplots(figsize=(11, 6))
ax = sns.lineplot(data=df_port, x="date", y="mmsi", hue="polygon_name", palette="tab10")
ax.set(xlabel="", ylabel="Unique MMSI", title="Number of unique vessels per month")
plt.legend(title="")  # loc='upper right', labels=['Raw Data', 'Interpolated']
# plt.savefig("../docs/images/Output_Mining_Gas.png", facecolor='white', dpi=300)

<matplotlib.legend.Legend at 0x7f5614d77280>
../../_images/7695fdffbc1d19686d3f22c3a0c3eab20fdc7f0456068e5241b26b9d8469ee23.png