electricsheepafrica/africa-health-facilities-zimbabwe

Zimbabwe Healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · License: ODbL · Updated: 2025-04-15

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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 2025-04-15. Geographic scope: ZWE.

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)	944
Columns	13 (6 numeric, 6 categorical, 0 datetime)
Train split	755 rows
Test split	188 rows
Geographic scope	ZWE
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-15

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Variables

Geographic — x (range 25.837–32.9726), y (range -22.2136–-15.6681), osm_type (node, way), loc_amenity (clinic, hospital, pharmacy).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range -12600896.0–12480272510.0), loc_name (Clinic, Rural Health Care Centre, Gumbo Clinic), changeset_id (range 5988946.0–164035674.0), meta_id (7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–34.375), changeset_version (range 1.0–8.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-zimbabwe")
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	36.3%	25.837 – 32.9726 (mean 31.0282)
y	float64	36.3%	-22.2136 – -15.6681 (mean -18.3389)
osm_id	int64	0.0%	-12600896.0 – 12480272510.0 (mean 5784919390.7299)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 34.375 (mean 10.5667)
loc_amenity	object	1.1%	clinic, hospital, pharmacy
loc_name	object	5.4%	Clinic, Rural Health Care Centre, Gumbo Clinic
changeset_id	float64	0.6%	5988946.0 – 164035674.0 (mean 101901878.4051)
changeset_version	float64	0.6%	1.0 – 8.0 (mean 1.5448)
changeset_timestamp	datetime64[ns, UTC]	0.6%
meta_id	object	0.0%	7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788
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	25.837	32.9726	31.0282	31.1257
y	-22.2136	-15.6681	-18.3389	-17.928
osm_id	-12600896.0	12480272510.0	5784919390.7299	6202614917.5
completeness	6.25	34.375	10.5667	9.375
changeset_id	5988946.0	164035674.0	101901878.4051	124558102.0
changeset_version	1.0	8.0	1.5448	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. 24 column(s) with >80% missing values were removed: meta_healthcare, meta_operator, geo_bounds_url, meta_speciality, meta_operator_type, contact_phone.... 1 exact duplicate rows were removed. 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.
• 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_zimbabwe,
  title     = {Zimbabwe Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-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.