electricsheepafrica/africa-idmc-event-data-for-nga

Nigeria - Internal Displacements Updates (IDU) (event data)

Publisher: Internal Displacement Monitoring Centre (IDMC) · Source: HDX · License: cc-by-igo · Updated: 2026-04-10

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Abstract

Conflict and disaster population movement (flows) data for Nigeria.

The IDU (Internal Displacement Updates) dataset, provided by the Internal Displacement Monitoring Centre (IDMC), offers timely event data and provisional information on new internal displacements caused by conflicts and disasters. Representing the most recent available information over a 180-day time period, the IDU is updated daily and focuses on "flows" (new displacements).

Internally displaced persons (IDPs) are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. The IDMC's event data, sourced from the IDU, provides initial assessments of these internal displacements, reflecting continually updated provisional information from various sources.

While the IDU offers early insights, the more thoroughly validated and curated "stock" (Total number of people leaving on internal displacement) and "flow" (population movements) estimates are available in the annual Global Internal Displacement Database (GIDD). Both datasets are accessible via API, with specific guidance on data access, structure, and limitations, including important preprocessing considerations for the IDU to ensure accurate analysis and avoid double-counting. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.

The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD). The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.

Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the displacement_date, displacement_start_date column(s). 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)	316
Columns	31 (6 numeric, 19 categorical, 5 datetime)
Train split	252 rows
Test split	63 rows
Geographic scope	NGA
Publisher	Internal Displacement Monitoring Centre (IDMC)
HDX last updated	2026-04-10

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Variables

Geographic — country (Nigeria), iso3 (NGA), latitude (range 4.8197–13.5076), longitude (range 3.7088–14.0507), displacement_type (Conflict, Disaster) and 11 others.

Temporal — event_start_date, event_end_date.

Identifier / Metadata — id (range 195243.0–243116.0), centroid ([11.4499473, 14.0506762], [8.02899895, 8.52271545], [11.51319, 14.02445]), event_id (range 33602.0–40811.0), event_name (Nigeria: Non-International armed conflict (NIAC) - Borno - 2025, Nigeria: Non-International armed conflict (NIAC) - Adamawa - 2025, Nigeria: Communal violence - Adamawa - 01/12/2025), event_codes (COMMUNAL_VIOLENCE, CRIME_RELATED, FLOOD) and 3 others.

Other — role (Recommended figure, Triangulation), qualifier (total, approximately), figure (range 2.0–51058.0), created_at, description.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-idmc-event-data-for-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
id	int64	0.0%	195243.0 – 243116.0 (mean 238086.9873)
country	object	0.0%	Nigeria
iso3	object	0.0%	NGA
latitude	float64	0.0%	4.8197 – 13.5076 (mean 10.4852)
longitude	float64	0.0%	3.7088 – 14.0507 (mean 11.4298)
centroid	object	0.0%	[11.4499473, 14.0506762], [8.02899895, 8.52271545], [11.51319, 14.02445]
role	object	0.0%	Recommended figure, Triangulation
displacement_type	object	0.0%	Conflict, Disaster
qualifier	object	0.0%	total, approximately
figure	int64	0.0%	2.0 – 51058.0 (mean 727.0095)
displacement_date	datetime64[ns]	0.0%
displacement_start_date	datetime64[ns]	0.0%
displacement_end_date	datetime64[ns]	0.0%
year	int64	0.0%	2025.0 – 2026.0 (mean 2025.0538)
event_id	int64	0.0%	33602.0 – 40811.0 (mean 36072.9367)
event_name	object	0.0%	Nigeria: Non-International armed conflict (NIAC) - Borno - 2025, Nigeria: Non-International armed conflict (NIAC) - Adamawa - 2025, Nigeria: Communal violence - Adamawa - 01/12/2025
event_codes	object	71.8%	COMMUNAL_VIOLENCE, CRIME_RELATED, FLOOD
event_code_types	object	71.8%	Local Identifier
event_start_date	datetime64[ns]	0.0%
event_end_date	datetime64[ns]	0.0%
sources	object	0.0%	IOM DTM - Nigeria, National Emergency Management Agency (NEMA), United Nations International Children's Emergency Fund (UNICEF)
locations_name	object	0.0%
locations_coordinates	object	0.0%
locations_accuracy	object	0.0%
locations_type	object	0.0%
displacement_occurred	object	0.0%
created_at	datetime64[ns, UTC]	0.0%
description	object	0.0%
combined_type	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	195243.0	243116.0	238086.9873	240216.5
latitude	4.8197	13.5076	10.4852	10.6565
longitude	3.7088	14.0507	11.4298	12.4154
figure	2.0	51058.0	727.0095	32.5
year	2025.0	2026.0	2025.0538	2025.0
event_id	33602.0	40811.0	36072.9367	33604.0

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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. 7 column(s) with >80% missing values were removed: category, subcategory, type, subtype, old_id, source_url.... 6 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 Internal Displacement Monitoring Centre (IDMC) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: event_codes, event_code_types.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_idmc_event_data_for_nga,
  title     = {Nigeria - Internal Displacements Updates (IDU) (event data)},
  author    = {Internal Displacement Monitoring Centre (IDMC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/idmc-event-data-for-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.