electricsheepafrica/africa-health-facilities-lesotho

Lesotho 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: LSO.

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)	181
Columns	15 (6 numeric, 8 categorical, 0 datetime)
Train split	144 rows
Test split	36 rows
Geographic scope	LSO
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range 27.1659–28.7174), y (range -30.4077–-28.748), osm_type (way, node), loc_amenity (clinic, hospital, pharmacy).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 129710013.0–12916034569.0), loc_name (Karabo Family Clinic, Popa Clinic, Husteds Pharmacy), changeset_id (range 39806115.0–173117379.0), meta_id (59ddca88440546baa54d6d4413014632, 3664f15e5db44931819402007e696c68, 8cb3bb80dd0941d49dc8587aa6b491cb), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–37.5), meta_healthcare (clinic, hospital, pharmacy), addr_street (Kingsway, Kofi Annan Road, Main North 1), 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-lesotho")
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	61.3%	27.1659 – 28.7174 (mean 27.647)
y	float64	61.3%	-30.4077 – -28.748 (mean -29.4544)
osm_id	int64	0.0%	129710013.0 – 12916034569.0 (mean 3026620081.768)
osm_type	object	0.0%	way, node
completeness	float64	0.0%	6.25 – 37.5 (mean 16.5228)
loc_amenity	object	0.0%	clinic, hospital, pharmacy
meta_healthcare	object	8.8%	clinic, hospital, pharmacy
loc_name	object	14.9%	Karabo Family Clinic, Popa Clinic, Husteds Pharmacy
addr_street	object	77.3%	Kingsway, Kofi Annan Road, Main North 1
changeset_id	int64	0.0%	39806115.0 – 173117379.0 (mean 130116271.7238)
changeset_version	int64	0.0%	1.0 – 12.0 (mean 3.4088)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	59ddca88440546baa54d6d4413014632, 3664f15e5db44931819402007e696c68, 8cb3bb80dd0941d49dc8587aa6b491cb
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	27.1659	28.7174	27.647	27.4966
y	-30.4077	-28.748	-29.4544	-29.3241
osm_id	129710013.0	12916034569.0	3026620081.768	646375306.0
completeness	6.25	37.5	16.5228	12.5
changeset_id	39806115.0	173117379.0	130116271.7238	138949533.0
changeset_version	1.0	12.0	3.4088	3.0

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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_street.
• 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_lesotho,
  title     = {Lesotho Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/lesotho-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.