electricsheepafrica/africa-health-facilities-sierra-leone

Sierra Leone 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: SLE.

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)	556
Columns	23 (6 numeric, 16 categorical, 0 datetime)
Train split	444 rows
Test split	111 rows
Geographic scope	SLE
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range -13.2713–-10.3167), y (range 6.9678–9.9737), osm_type (node, way), amenity (clinic, pharmacy, hospital), speciality (clinic, hospital, yes) and 2 others.

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 224729043.0–13130524315.0), name (Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP), source (Red Cross Field Survey, MSF-CH, MSFsurvey), water_source, changeset_id (range 19113513.0–172755228.0) and 3 others.

Other — completeness (range 6.25–40.625), healthcare (hospital, pharmacy, clinic), operator (Ministry of Health and Sanitation, government, combination), opening_hours (24/7, 08:00-17:00, Mo-Fr 08:00-17:00), dispensing (yes, no) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-sierra-leone")
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	30.6%	-13.2713 – -10.3167 (mean -11.8405)
y	float64	30.6%	6.9678 – 9.9737 (mean 8.4376)
osm_id	int64	0.0%	224729043.0 – 13130524315.0 (mean 4009847857.0971)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 40.625 (mean 18.7837)
amenity	object	2.9%	clinic, pharmacy, hospital
healthcare	object	65.1%	hospital, pharmacy, clinic
name	object	11.9%	Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP
operator	object	75.4%	Ministry of Health and Sanitation, government, combination
source	object	50.5%	Red Cross Field Survey, MSF-CH, MSFsurvey
speciality	object	74.5%	clinic, hospital, yes
opening_hours	object	76.3%	24/7, 08:00-17:00, Mo-Fr 08:00-17:00
dispensing	object	79.0%	yes, no
emergency	object	79.7%	yes, no
water_source	object	78.8%
addr_street	object	75.4%
addr_city	object	64.9%
changeset_id	int64	0.0%	19113513.0 – 172755228.0 (mean 104872179.8903)
changeset_version	int64	0.0%	1.0 – 10.0 (mean 2.545)
changeset_timestamp	datetime64[ns, UTC]	0.0%
uuid	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
x	-13.2713	-10.3167	-11.8405	-11.7458
y	6.9678	9.9737	8.4376	8.4614
osm_id	224729043.0	13130524315.0	4009847857.0971	4504611402.0
completeness	6.25	40.625	18.7837	12.5
changeset_id	19113513.0	172755228.0	104872179.8903	108431838.0
changeset_version	1.0	10.0	2.545	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. 14 column(s) with >80% missing values were removed: operator_type, operational_status, beds, staff_doctors, staff_nurses, health_amenity_type.... 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, operator, source, speciality, opening_hours, dispensing....
• 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_sierra_leone,
  title     = {Sierra Leone Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sierra-leone-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.