electricsheepafrica/africa-world-bank-urban-development-indicators-for-liberia

Liberia - Urban Development

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: LBR.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	626
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	500 rows
Test split	125 rows
Geographic scope	LBR
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Liberia), country_iso3 (LBR), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -9.0635–3117291.0).

Identifier / Metadata — indicator_name (Population in largest city, Population in the largest city (% of urban population), Population in urban agglomerations of more than 1 million), indicator_code (EN.URB.LCTY, EN.URB.LCTY.UR.ZS, EN.URB.MCTY), esa_source (HDX), esa_processed (2026-04-13).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-urban-development-indicators-for-liberia")
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%	Liberia
country_iso3	object	0.0%	LBR
year	int64	0.0%	1960.0 – 2025.0 (mean 1994.984)
indicator_name	object	0.0%	Population in largest city, Population in the largest city (% of urban population), Population in urban agglomerations of more than 1 million
indicator_code	object	0.0%	EN.URB.LCTY, EN.URB.LCTY.UR.ZS, EN.URB.MCTY
value	float64	0.0%	-9.0635 – 3117291.0 (mean 288374.6391)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-13

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	1994.984	1997.0
value	-9.0635	3117291.0	288374.6391	46.2962

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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 World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_world_bank_urban_development_indicators_for_liberia,
  title     = {Liberia - Urban Development},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-liberia},
  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.