electricsheepafrica/africa-health-facilities-libya

Libya 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: LBY.

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)	2,945
Columns	14 (6 numeric, 7 categorical, 0 datetime)
Train split	2,356 rows
Test split	589 rows
Geographic scope	LBY
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range 9.4886–25.0499), y (range 24.1942–32.9511), osm_type (node, way), loc_amenity (pharmacy, hospital, clinic).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 95248345.0–13203310815.0), loc_name (صيدلية, صيدلية العافية, مستوصف), changeset_id (range 7383079.0–173017752.0), meta_id (be789d1f90674d6b8d766dd22c0600bf, a7da5963d8f048888777b246f2834637, 2195df6cb88e4cb3a4d3870800036914), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–40.625), meta_healthcare (hospital, pharmacy, clinic), changeset_version (range 1.0–28.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-libya")
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	26.7%	9.4886 – 25.0499 (mean 15.0269)
y	float64	26.7%	24.1942 – 32.9511 (mean 32.1864)
osm_id	int64	0.0%	95248345.0 – 13203310815.0 (mean 4648923270.1019)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 40.625 (mean 11.7774)
loc_amenity	object	0.6%	pharmacy, hospital, clinic
meta_healthcare	object	54.0%	hospital, pharmacy, clinic
loc_name	object	15.9%	صيدلية, صيدلية العافية, مستوصف
changeset_id	int64	0.0%	7383079.0 – 173017752.0 (mean 100336788.5929)
changeset_version	int64	0.0%	1.0 – 28.0 (mean 2.5053)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	be789d1f90674d6b8d766dd22c0600bf, a7da5963d8f048888777b246f2834637, 2195df6cb88e4cb3a4d3870800036914
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-20

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Numeric Summary

Column	Min	Max	Mean	Median
x	9.4886	25.0499	15.0269	13.262
y	24.1942	32.9511	32.1864	32.6775
osm_id	95248345.0	13203310815.0	4648923270.1019	4663357790.0
completeness	6.25	40.625	11.7774	9.375
changeset_id	7383079.0	173017752.0	100336788.5929	94670743.0
changeset_version	1.0	28.0	2.5053	2.0

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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. 23 column(s) with >80% missing values were removed: meta_operator, geo_bounds_url, meta_speciality, meta_operator_type, contact_phone, status_operational_status.... 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.
• 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_libya,
  title     = {Libya Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/libya-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.