electricsheepafrica/africa-health-facilities-liberia

Liberia Healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · License: ODbL · Updated: 2025-10-15

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Abstract

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-10-15. 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	Tabular records
Rows (total)	1,175
Columns	19 (6 numeric, 12 categorical, 0 datetime)
Train split	940 rows
Test split	235 rows
Geographic scope	LBR
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range -11.4132–-7.5084), y (range 4.3637–8.496), osm_type (node, way), loc_amenity (clinic, pharmacy, hospital), addr_city (Monrovia, Gbarnga, Ganta).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 94957267.0–12875885201.0), loc_name (Lucky Pharmacy, Red Hill Medical Clinic, Tuzon Clinic), changeset_id (range 27026152.0–172132881.0), meta_id (bce41dd53df44a1faea4c78ab589fd2d, f2a8ba98e8364b97987987c6d53cf2d7, 6fbd17ee27a046449be9738e013aceea), esa_source and 1 others.

Other — completeness (range 6.25–46.875), meta_healthcare (clinic, pharmacy, hospital), meta_operator (Government, private_profit, government), geo_bounds_url (Ministry of Health and National Public Health Institute of Liberia, MOH & NPHIL, Red Cross Field Survey), status_operational_status (functional, Functional, non-functional) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-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
x	float64	27.3%	-11.4132 – -7.5084 (mean -9.776)
y	float64	27.3%	4.3637 – 8.496 (mean 6.6547)
osm_id	int64	0.0%	94957267.0 – 12875885201.0 (mean 5038457318.5387)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 46.875 (mean 21.0931)
loc_amenity	object	0.7%	clinic, pharmacy, hospital
meta_healthcare	object	24.0%	clinic, pharmacy, hospital
loc_name	object	5.6%	Lucky Pharmacy, Red Hill Medical Clinic, Tuzon Clinic
meta_operator	object	68.3%	Government, private_profit, government
geo_bounds_url	object	21.7%	Ministry of Health and National Public Health Institute of Liberia, MOH & NPHIL, Red Cross Field Survey
status_operational_status	object	58.4%	functional, Functional, non-functional
access_hours	object	76.6%	Mo-Sa 08:00-18:00, 24/7, Mo-Fr 08:00-17:00
addr_city	object	56.5%	Monrovia, Gbarnga, Ganta
changeset_id	int64	0.0%	27026152.0 – 172132881.0 (mean 124405093.5838)
changeset_version	int64	0.0%	1.0 – 10.0 (mean 3.4426)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	bce41dd53df44a1faea4c78ab589fd2d, f2a8ba98e8364b97987987c6d53cf2d7, 6fbd17ee27a046449be9738e013aceea
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
x	-11.4132	-7.5084	-9.776	-9.7191
y	4.3637	8.496	6.6547	6.5939
osm_id	94957267.0	12875885201.0	5038457318.5387	5102115273.0
completeness	6.25	46.875	21.0931	18.75
changeset_id	27026152.0	172132881.0	124405093.5838	125657972.0
changeset_version	1.0	10.0	3.4426	3.0

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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. 18 column(s) with >80% missing values were removed: meta_speciality, meta_operator_type, contact_phone, capacity_beds, capacity_staff, meta_health_amenity_type.... 1 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 Global Healthsites Mapping Project and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: x, y, meta_healthcare, meta_operator, geo_bounds_url, status_operational_status, access_hours, addr_city.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_health_facilities_liberia,
  title     = {Liberia Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/liberia-healthsites},
  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.