electricsheepafrica/africa-health-facilities-ethiopia

Ethiopia 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: ETH.

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)	1,115
Columns	14 (6 numeric, 7 categorical, 0 datetime)
Train split	892 rows
Test split	223 rows
Geographic scope	ETH
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range 34.2391–46.5313), y (range 3.4557–14.2886), osm_type (node, way), amenity (hospital, clinic, pharmacy).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 42656243.0–13146838802.0), name (Solo Da Pharmacy, Health Center, Wojel Health Center), changeset_id (range 3394188.0–172004469.0), uuid (e04ec30211664b3fac0627c03bc2e367, 076df684db92445e9fe560aa50688c58, 4c176eb2efac4ee9b44814211bf5b491), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–34.375), 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-ethiopia")
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	29.3%	34.2391 – 46.5313 (mean 38.7375)
y	float64	29.3%	3.4557 – 14.2886 (mean 9.52)
osm_id	int64	0.0%	42656243.0 – 13146838802.0 (mean 5248194832.8717)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 34.375 (mean 10.3924)
amenity	object	1.0%	hospital, clinic, pharmacy
healthcare	object	66.0%	hospital, pharmacy, clinic
name	object	37.1%	Solo Da Pharmacy, Health Center, Wojel Health Center
changeset_id	int64	0.0%	3394188.0 – 172004469.0 (mean 94709134.8233)
changeset_version	int64	0.0%	1.0 – 9.0 (mean 1.5901)
changeset_timestamp	datetime64[ns, UTC]	0.0%
uuid	object	0.0%	e04ec30211664b3fac0627c03bc2e367, 076df684db92445e9fe560aa50688c58, 4c176eb2efac4ee9b44814211bf5b491
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	34.2391	46.5313	38.7375	38.7431
y	3.4557	14.2886	9.52	9.0188
osm_id	42656243.0	13146838802.0	5248194832.8717	5671866221.0
completeness	6.25	34.375	10.3924	9.375
changeset_id	3394188.0	172004469.0	94709134.8233	85566006.0
changeset_version	1.0	9.0	1.5901	1.0

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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, name.
• 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_ethiopia,
  title     = {Ethiopia Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ethiopia-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.