electricsheepafrica/africa-maritime-passageway-transits-during-disruptions

Suez Canal, Bosporus Strait, Bab el-Mandeb Strait: Transits During Disruptions

Publisher: Asian Development Bank · Source: HDX · License: cc-by · Updated: 2025-07-17

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Abstract

This dataset contains the daily count of transits on select maritime passageways during the following events of disruption: i) Suez Canal blockage, ii) Russian Invasion of Ukraine for Bosporus Strait, and iii) the Houthi attacks for Bab el-Mandeb Strait. The counts are derived from Automatic Identification System (AIS) data--high frequency ship signals during navigation--using the framework developed in the publication Analyzing anomalous events in passageways with high-frequency ship signals. Data to validate the transits and other supplementary dataof the publication are also included.

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the arrival_date column(s). Geographic scope: DJI, EGY, ERI, TUR, YEM.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Time-series observations
Rows (total)	61
Columns	7 (4 numeric, 2 categorical, 1 datetime)
Train split	48 rows
Test split	12 rows
Geographic scope	DJI, EGY, ERI, TUR, YEM
Publisher	Asian Development Bank
HDX last updated	2025-07-17

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Variables

Geographic — transit_per_arrival_day (range 5.0–117.0), complete_transit_per_arrival_day (range 4.0–91.0).

Temporal — arrival_date, complete_transit_dwell_time (range 13.091–196.7221), complete_transit_idle_time (range 1.474–176.9839).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-maritime-passageway-transits-during-disruptions")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
arrival_date	datetime64[ns]	0.0%
transit_per_arrival_day	int64	0.0%	5.0 – 117.0 (mean 50.1475)
complete_transit_per_arrival_day	int64	0.0%	4.0 – 91.0 (mean 45.623)
complete_transit_dwell_time	float64	0.0%	13.091 – 196.7221 (mean 47.9847)
complete_transit_idle_time	float64	0.0%	1.474 – 176.9839 (mean 32.8913)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
transit_per_arrival_day	5.0	117.0	50.1475	49.0
complete_transit_per_arrival_day	4.0	91.0	45.623	46.0
complete_transit_dwell_time	13.091	196.7221	47.9847	27.0432
complete_transit_idle_time	1.474	176.9839	32.8913	11.8608

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Asian Development Bank and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 5 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_maritime_passageway_transits_during_disruptions,
  title     = {Suez Canal, Bosporus Strait, Bab el-Mandeb Strait: Transits During Disruptions},
  author    = {Asian Development Bank},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/maritime-passageway-transits-during-disruptions},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.