electricsheepafrica/africa-health-facilities-equatorial-guinea

Equatorial Guinea 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: GNQ.

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)	21
Columns	15 (6 numeric, 8 categorical, 0 datetime)
Train split	16 rows
Test split	4 rows
Geographic scope	GNQ
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range 8.7799–10.6145), y (range 1.8167–3.7639), osm_type (node, way), loc_amenity (hospital, pharmacy, clinic), addr_city (Malabo, Luba, Ebebiyín).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 265308137.0–12491930116.0), loc_name (Farmacia, Centro Médico La Paz, Super Pharm), changeset_id (range 72365664.0–162866917.0), meta_id (20572f095e5e4578a3789290e09f9820, dd1ce5e28cb44796bcbf9738596a3ff1, 0b7b4b100d064997be169c6972476c36), esa_source (HDX) and 1 others.

Other — completeness (range 9.375–25.0), meta_healthcare (hospital, pharmacy, clinic), changeset_version (range 1.0–9.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-equatorial-guinea")
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	47.6%	8.7799 – 10.6145 (mean 9.1397)
y	float64	47.6%	1.8167 – 3.7639 (mean 3.2529)
osm_id	int64	0.0%	265308137.0 – 12491930116.0 (mean 4801565710.5714)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	9.375 – 25.0 (mean 14.5833)
loc_amenity	object	0.0%	hospital, pharmacy, clinic
meta_healthcare	object	4.8%	hospital, pharmacy, clinic
loc_name	object	14.3%	Farmacia, Centro Médico La Paz, Super Pharm
addr_city	object	76.2%	Malabo, Luba, Ebebiyín
changeset_id	int64	0.0%	72365664.0 – 162866917.0 (mean 127785138.381)
changeset_version	int64	0.0%	1.0 – 9.0 (mean 3.381)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	20572f095e5e4578a3789290e09f9820, dd1ce5e28cb44796bcbf9738596a3ff1, 0b7b4b100d064997be169c6972476c36
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	8.7799	10.6145	9.1397	8.7837
y	1.8167	3.7639	3.2529	3.7438
osm_id	265308137.0	12491930116.0	4801565710.5714	4790712822.0
completeness	9.375	25.0	14.5833	12.5
changeset_id	72365664.0	162866917.0	127785138.381	133764423.0
changeset_version	1.0	9.0	3.381	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. 22 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, 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_equatorial_guinea,
  title     = {Equatorial Guinea Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/equatorial-guinea-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.