electricsheepafrica/africa-west-and-central-africa-administrative-boundaries-levels

West and Central Africa - Administrative boundaries levels 0 - 2 and Settlements

Publisher: OCHA West and Central Africa (ROWCA) · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

West and Central Africa Administrative boundaries, administrative level 0 to 2. Notice: The boundaries and names shown and the designations used on these shapefiles do not imply official endorsement or acceptance by the United Nations. West and Central Africa settlements with administrative capitals

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the last_modif, date column(s). Geographic scope: BEN, BFA, CPV, CMR, CAF, TCD, COG, CIV, and 16 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	2,355
Columns	14 (3 numeric, 9 categorical, 2 datetime)
Train split	1,884 rows
Test split	471 rows
Geographic scope	BEN, BFA, CPV, CMR, CAF, TCD, COG, CIV, and 16 others
Publisher	OCHA West and Central Africa (ROWCA)
HDX last updated	2025-05-05

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Variables

Geographic — admin0name (Nigeria, Ghana, Democratic Republic of Congo), admin0pcod (NG, GH, CD), admin1name (Kano, Ashanti, Eastern), admin2name (Sao Joao Baptista, Nossa Senhora Da Luz, Dagana), admin1pcod (NG20, GH02, NG21) and 1 others.

Temporal — date.

Identifier / Metadata — objectid_1 (range 1.0–2356.0), source (OCHAfrom ctrylayers), esa_source (HDX), esa_processed (2026-04-08).

Other — last_modif, shape_leng (range 0.0462–26.1063), shape_area (range 0.0001–28.8161).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-west-and-central-africa-administrative-boundaries-levels")
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
objectid_1	int64	0.0%	1.0 – 2356.0 (mean 1178.6025)
admin0name	object	0.0%	Nigeria, Ghana, Democratic Republic of Congo
admin0pcod	object	0.0%	NG, GH, CD
admin1name	object	0.0%	Kano, Ashanti, Eastern
admin2name	object	0.0%	Sao Joao Baptista, Nossa Senhora Da Luz, Dagana
admin1pcod	object	0.0%	NG20, GH02, NG21
admin2pcod	object	0.0%	CD10, CI0903, LR0402
last_modif	datetime64[ns]	0.0%
source	object	0.0%	OCHAfrom ctrylayers
date	datetime64[ns]	0.0%
shape_leng	float64	0.0%	0.0462 – 26.1063 (mean 2.5904)
shape_area	float64	0.0%	0.0001 – 28.8161 (mean 0.4021)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
objectid_1	1.0	2356.0	1178.6025	1179.0
shape_leng	0.0462	26.1063	2.5904	1.777
shape_area	0.0001	28.8161	0.4021	0.1037

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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 OCHA West and Central Africa (ROWCA) 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 24 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_west_and_central_africa_administrative_boundaries_levels,
  title     = {West and Central Africa - Administrative boundaries levels 0 - 2 and Settlements},
  author    = {OCHA West and Central Africa (ROWCA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/west-and-central-africa-administrative-boundaries-levels},
  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.