electricsheepafrica/africa-climada-litpop-dataset

LitPop: Humanitarian Response Plan (HRP) Countries Exposure Data for Disaster Risk Assessment

Publisher: ETH Zürich - Weather and Climate Risks · Source: HDX · License: cc-by · Updated: 2025-09-02

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Abstract

A high-resolution asset exposure dataset produced using 'lit population' (LitPop), a globally consistent methodology to disaggregate asset value data proportional to a combination of nightlight intensity and geographical population data. Exposure data for population, asset values and productive capital at 4km spatial resolution globally, consistent across country borders. The dataset offers value for manifold use cases, including globally consistent economic disaster risk assessments and climate change adaptation studies, especially for larger regions, yet at considerably high resolution. The Climada Data API can be used to explore the full, original datasets.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-09-02. Geographic scope: AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 15 others.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	First-level administrative unit observations
Rows (total)	397
Columns	9 (3 numeric, 6 categorical, 0 datetime)
Train split	317 rows
Test split	79 rows
Geographic scope	AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 15 others
Publisher	ETH Zürich - Weather and Climate Risks
HDX last updated	2025-09-02

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Variables

Geographic — country_name (Nigeria, Afghanistan, Colombia), admin1_name (Centre, Sucre, Adamawa), latitude (range -25.9514–51.3497), longitude (range -81.3542–98.7639).

Outcome / Measurement — value (range 13619.0–223861232461.0).

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

Other — aggregation (sum), indicator (litpop, #indicator+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climada-litpop-dataset")
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_name	object	0.0%	Nigeria, Afghanistan, Colombia
admin1_name	object	0.0%	Centre, Sucre, Adamawa
latitude	float64	0.3%	-25.9514 – 51.3497 (mean 12.8935)
longitude	float64	0.3%	-81.3542 – 98.7639 (mean 13.9464)
aggregation	object	0.3%	sum
indicator	object	0.0%	litpop, #indicator+name
value	float64	0.3%	13619.0 – 223861232461.0 (mean 9849430841.6768)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	-25.9514	51.3497	12.8935	10.307
longitude	-81.3542	98.7639	13.9464	23.3802
value	13619.0	223861232461.0	9849430841.6768	449701108.0

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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. 3 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 ETH Zürich - Weather and Climate Risks 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 23 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_climada_litpop_dataset,
  title     = {LitPop: Humanitarian Response Plan (HRP) Countries Exposure Data for Disaster Risk Assessment},
  author    = {ETH Zürich - Weather and Climate Risks},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/climada-litpop-dataset},
  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.