electricsheepafrica/africa-health-facilities-mauritania

Mauritania 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: MRT.

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)	414
Columns	15 (6 numeric, 8 categorical, 0 datetime)
Train split	331 rows
Test split	82 rows
Geographic scope	MRT
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range -17.0464–-8.9939), y (range 15.084–20.9767), osm_type (node, way), loc_amenity (pharmacy, hospital, clinic), addr_city (Nouakchott نواكشوط, نواكشوط, Nouakchott).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 137006238.0–13072385469.0), loc_name (Centre de santé مركز الصحة, Cabinet Medical, D. Pharmacie), changeset_id (range 19516581.0–172714258.0), meta_id (9f9e859b756c4a888f839896cc6e8094, 274b7f9ea9114520ac5b2c23442f723f, 214e016545cf40c5bc9df8d383a290f8), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–37.5), meta_healthcare (pharmacy, hospital, clinic), changeset_version (range 1.0–12.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-mauritania")
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%	-17.0464 – -8.9939 (mean -15.7582)
y	float64	24.2%	15.084 – 20.9767 (mean 18.02)
osm_id	int64	0.0%	137006238.0 – 13072385469.0 (mean 6238082004.6932)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 37.5 (mean 10.7563)
loc_amenity	object	1.0%	pharmacy, hospital, clinic
meta_healthcare	object	66.9%	pharmacy, hospital, clinic
loc_name	object	32.9%	Centre de santé مركز الصحة, Cabinet Medical, D. Pharmacie
addr_city	object	74.6%	Nouakchott نواكشوط, نواكشوط, Nouakchott
changeset_id	int64	0.0%	19516581.0 – 172714258.0 (mean 113476533.314)
changeset_version	int64	0.0%	1.0 – 12.0 (mean 2.0314)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	9f9e859b756c4a888f839896cc6e8094, 274b7f9ea9114520ac5b2c23442f723f, 214e016545cf40c5bc9df8d383a290f8
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	-17.0464	-8.9939	-15.7582	-15.9512
y	15.084	20.9767	18.02	18.0756
osm_id	137006238.0	13072385469.0	6238082004.6932	8657017950.0
completeness	6.25	37.5	10.7563	9.375
changeset_id	19516581.0	172714258.0	113476533.314	104300393.0
changeset_version	1.0	12.0	2.0314	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. 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, meta_healthcare, loc_name, 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_mauritania,
  title     = {Mauritania Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mauritania-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.