electricsheepafrica/africa-gambia-healthsites

Gambia-healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · License: cc-by-igo · Updated: 2025-04-25

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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. Temporal coverage is indicated by the changeset_timestamp column(s). Geographic scope: GMB.

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)	73
Columns	14 (6 numeric, 7 categorical, 1 datetime)
Train split	58 rows
Test split	14 rows
Geographic scope	GMB
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-25

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Variables

Geographic — x (range -16.7964–-14.0158), y (range 13.0931–13.7792), osm_type (way, node), amenity (hospital, clinic, pharmacy).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 125548270.0–6576674985.0), changeset_id (range 18241309.0–75320524.0), uuid (09a60dadc51d45c4afd7d1078fc98c09, ff347d8b87e9443b98c1c2735ca12c7a, 0722456c720b47e59eb12df481597a4d), name (Medical Research Council, Diabugu Batapa Health Center, Manding Drug Store), esa_source (HDX) and 1 others.

Other — completeness (range 6.0–24.0), changeset_version (range 1.0–6.0), changeset_user (MorganJ14, cmsandquist, Caitlyn W).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-gambia-healthsites")
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	56.2%	-16.7964 – -14.0158 (mean -15.8398)
y	float64	56.2%	13.0931 – 13.7792 (mean 13.4176)
osm_id	int64	0.0%	125548270.0 – 6576674985.0 (mean 2403254561.2603)
osm_type	object	0.0%	way, node
completeness	int64	0.0%	6.0 – 24.0 (mean 10.3836)
amenity	object	0.0%	hospital, clinic, pharmacy
changeset_id	int64	0.0%	18241309.0 – 75320524.0 (mean 48948539.5342)
uuid	object	0.0%	09a60dadc51d45c4afd7d1078fc98c09, ff347d8b87e9443b98c1c2735ca12c7a, 0722456c720b47e59eb12df481597a4d
changeset_version	int64	0.0%	1.0 – 6.0 (mean 1.5068)
changeset_timestamp	datetime64[ns]	0.0%
name	object	24.7%	Medical Research Council, Diabugu Batapa Health Center, Manding Drug Store
changeset_user	object	0.0%	MorganJ14, cmsandquist, Caitlyn W
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
x	-16.7964	-14.0158	-15.8398	-16.0882
y	13.0931	13.7792	13.4176	13.4417
osm_id	125548270.0	6576674985.0	2403254561.2603	624734295.0
completeness	6.0	24.0	10.3836	10.0
changeset_id	18241309.0	75320524.0	48948539.5342	46475922.0
changeset_version	1.0	6.0	1.5068	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: is_in_health_zone, speciality, addr_full, operator, water_source, insurance.... 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, 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_gambia_healthsites,
  title     = {Gambia-healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/gambia-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.