electricsheepafrica/africa-health-facilities-ghana

Ghana 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: GHA.

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)	2,168
Columns	14 (6 numeric, 7 categorical, 0 datetime)
Train split	1,734 rows
Test split	433 rows
Geographic scope	GHA
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range -2.9733–1.1949), y (range 4.8838–10.9796), osm_type (node, way), amenity (pharmacy, hospital, clinic).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 154173029.0–13208372240.0), name (Ernest Chemist, Drug Store, Clinic), changeset_id (range 7911073.0–173278687.0), uuid (4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–46.875), healthcare (pharmacy, hospital, clinic), changeset_version (range 1.0–11.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-ghana")
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	24.2%	-2.9733 – 1.1949 (mean -0.721)
y	float64	24.2%	4.8838 – 10.9796 (mean 6.2805)
osm_id	int64	0.0%	154173029.0 – 13208372240.0 (mean 5928268646.9723)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 46.875 (mean 12.8142)
amenity	object	2.5%	pharmacy, hospital, clinic
healthcare	object	47.6%	pharmacy, hospital, clinic
name	object	15.3%	Ernest Chemist, Drug Store, Clinic
changeset_id	int64	0.0%	7911073.0 – 173278687.0 (mean 110439476.5807)
changeset_version	int64	0.0%	1.0 – 11.0 (mean 1.7911)
changeset_timestamp	datetime64[ns, UTC]	0.0%
uuid	object	0.0%	4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75
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	-2.9733	1.1949	-0.721	-0.2449
y	4.8838	10.9796	6.2805	5.6588
osm_id	154173029.0	13208372240.0	5928268646.9723	6542893586.5
completeness	6.25	46.875	12.8142	12.5
changeset_id	7911073.0	173278687.0	110439476.5807	119505116.0
changeset_version	1.0	11.0	1.7911	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: operator, source, speciality, operator_type, operational_status, opening_hours.... 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, 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_ghana,
  title     = {Ghana Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ghana-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.