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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - central-africa
  - geodata
  - populated-places-settlements
  - west-africa
  - ben
  - bfa
  - cpv
  - cmr
  - caf
pretty_name: >-
  West and Central Africa - Administrative boundaries levels 0 - 2 and
  Settlements
dataset_info:
  splits:
    - name: train
      num_examples: 1884
    - name: test
      num_examples: 471

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

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.

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

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).

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()

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

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

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.

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.

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)}
}

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