electricsheepafrica/africa-lake-chad-basin-baseline-population

Lake Chad Basin Baseline Population

Publisher: OCHA West and Central Africa (ROWCA) · Source: HDX · License: other-pd-nr · Updated: 2024-05-24

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Abstract

The data contains the latest estimated population of each administrative level 1 unit in the Lake Chad Basin. Estimation is based on input from UNFPA and the most recently available census for each country. Data is encoded as utf-8. The second row of the CSV contains HXL tags.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the asofdate column(s). Geographic scope: CMR, TCD, NER, NGA.

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

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Dataset Characteristics

Domain	Conflict and security
Unit of observation	Country-level aggregates
Rows (total)	79
Columns	6 (1 numeric, 4 categorical, 1 datetime)
Train split	63 rows
Test split	15 rows
Geographic scope	CMR, TCD, NER, NGA
Publisher	OCHA West and Central Africa (ROWCA)
HDX last updated	2024-05-24

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Variables

Geographic — country (Nigeria, Chad, Cameroon), reportedlocation (#adm1+name, Cross River, Gombe).

Temporal — asofdate.

Outcome / Measurement — totaltotal (range 38913.0–12452097.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-06).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-lake-chad-basin-baseline-population")
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
country	object	0.0%	Nigeria, Chad, Cameroon
reportedlocation	object	0.0%	#adm1+name, Cross River, Gombe
totaltotal	float64	1.3%	38913.0 – 12452097.0 (mean 3016332.9359)
asofdate	datetime64[ns]	1.3%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
totaltotal	38913.0	12452097.0	3016332.9359	2875695.5

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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. 2 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 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 4 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_lake_chad_basin_baseline_population,
  title     = {Lake Chad Basin Baseline Population},
  author    = {OCHA West and Central Africa (ROWCA)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/lake-chad-basin-baseline-population},
  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.