electricsheepafrica/africa-aid-worker-security-database-nga

Nigeria - Aid Worker Security Database

Publisher: Humanitarian Outcomes · Source: HDX · License: cc-by · Updated: 2026-04-10

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Abstract

This dataset shows aid worker security incidents in Nigeria. Annually, the data for the previous year undergoes a verification process. Data for the current year is provisional. For incident descriptions, please download data directly from www.aidworkersecurity.org

Each row in this dataset represents discrete events or incidents. Data was last updated on HDX on 2026-04-10. Geographic scope: NGA.

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

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Dataset Characteristics

Domain	Conflict and security
Unit of observation	Discrete events or incidents
Rows (total)	99
Columns	46 (30 numeric, 16 categorical, 0 datetime)
Train split	79 rows
Test split	19 rows
Geographic scope	NGA
Publisher	Humanitarian Outcomes
HDX last updated	2026-04-10

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Variables

Geographic — year (range 2004.0–2026.0), day (range 1.0–31.0), country_code (NG), country (Nigeria), region (Borno, Zamfara, Federal Capital Territory) and 7 others.

Temporal — month (range 1.0–12.0).

Demographic — gender_male, gender_female, gender_unknown.

Outcome / Measurement — total_nationals (range 0.0–46.0), total_internationals (range 0.0–4.0), total_killed, total_wounded, total_kidnapped and 2 others.

Identifier / Metadata — incident_id (range 306.0–5809.0), nationals_kidnapped (range 0.0–6.0), internationals_kidnapped (range 0.0–3.0), actor_name, source and 2 others.

Other — un (range 0.0–46.0), ingo (range 0.0–10.0), icrc (range 0.0–1.0), nrcs_and_ifrc (range 0.0–14.0), nngo (range 0.0–3.0) and 11 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-aid-worker-security-database-nga")
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
incident_id	int64	0.0%	306.0 – 5809.0 (mean 3699.4141)
year	int64	0.0%	2004.0 – 2026.0 (mean 2021.4343)
month	int64	0.0%	1.0 – 12.0 (mean 6.5657)
day	float64	2.0%	1.0 – 31.0 (mean 15.3608)
country_code	object	0.0%	NG
country	object	0.0%	Nigeria
region	object	1.0%	Borno, Zamfara, Federal Capital Territory
district	object	5.1%	Maiduguri, Mobbar, Monguno
city	object	10.1%	Rann, Monguno, Damasak
un	int64	0.0%	0.0 – 46.0 (mean 0.8687)
ingo	int64	0.0%	0.0 – 10.0 (mean 1.0)
icrc	int64	0.0%	0.0 – 1.0 (mean 0.0101)
nrcs_and_ifrc	int64	0.0%	0.0 – 14.0 (mean 0.1818)
nngo	int64	0.0%	0.0 – 3.0 (mean 0.4141)
other	int64	0.0%	0.0 – 1.0 (mean 0.0101)
nationals_killed	int64	0.0%	0.0 – 9.0 (mean 0.7374)
nationals_wounded	int64	0.0%	0.0 – 37.0 (mean 0.8485)
nationals_kidnapped	int64	0.0%	0.0 – 6.0 (mean 0.7879)
nationals_detained	int64	0.0%	0.0 – 1.0 (mean 0.0303)
total_nationals	int64	0.0%	0.0 – 46.0 (mean 2.404)
internationals_killed	int64	0.0%	0.0 – 1.0 (mean 0.0303)
internationals_wounded	int64	0.0%	0.0 – 0.0 (mean 0.0)
internationals_kidnapped	int64	0.0%	0.0 – 3.0 (mean 0.0505)
internationals_detained	int64	0.0%	0.0 – 0.0 (mean 0.0)
total_internationals	int64	0.0%	0.0 – 4.0 (mean 0.0808)
total_killed	int64	0.0%
total_wounded	int64	0.0%
total_kidnapped	int64	0.0%
total_detained	int64	0.0%
total_affected	int64	0.0%
gender_male	int64	0.0%
gender_female	int64	0.0%
gender_unknown	int64	0.0%
means_of_attack	object	0.0%	Kidnapping, Shooting, Bodily assault
attack_context	object	0.0%	Ambush, Raid, Individual attack
location	object	0.0%	Road, Office/compound, Public location
latitude	float64	0.0%
longitude	float64	0.0%
motive	object	1.0%	Unknown, Economic, Political
actor_type	object	0.0%	Non-state armed group: Regional, Unknown, Criminal
actor_name	object	0.0%
details	object	0.0%
verified	object	0.0%
source	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
incident_id	306.0	5809.0	3699.4141	3855.0
year	2004.0	2026.0	2021.4343	2022.0
month	1.0	12.0	6.5657	7.0
day	1.0	31.0	15.3608	15.0
un	0.0	46.0	0.8687	0.0
ingo	0.0	10.0	1.0	0.0
icrc	0.0	1.0	0.0101	0.0
nrcs_and_ifrc	0.0	14.0	0.1818	0.0
nngo	0.0	3.0	0.4141	0.0
other	0.0	1.0	0.0101	0.0
nationals_killed	0.0	9.0	0.7374	0.0
nationals_wounded	0.0	37.0	0.8485	0.0
nationals_kidnapped	0.0	6.0	0.7879	0.0
nationals_detained	0.0	1.0	0.0303	0.0
total_nationals	0.0	46.0	2.404	1.0

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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. 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 Humanitarian Outcomes and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_aid_worker_security_database_nga,
  title     = {Nigeria - Aid Worker Security Database},
  author    = {Humanitarian Outcomes},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/aid-worker-security-database-nga},
  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.