electricsheepafrica/africa-health-facilities-tunisia

Tunisia 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: TUN.

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)	1,416
Columns	17 (6 numeric, 10 categorical, 0 datetime)
Train split	1,132 rows
Test split	283 rows
Geographic scope	TUN
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-10-15

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Variables

Geographic — x (range 8.1287–11.2181), y (range 32.8567–37.278), osm_type (node, way), loc_amenity (pharmacy, hospital, clinic), addr_city (صفاقس, تونس, قفصة).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 27944768.0–13160906727.0), loc_name (صيدلية الليل, pharmacie de nuit, Pharmacie de nuit), changeset_id (range 3101828.0–173251412.0), meta_id (1cd96ef5dd3b46de8da71ea48cba1449, a28114cb09df429dbb1e97fa6b505aca, a1bc0329b3bb4a0facadddc6388267a2), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–40.625), meta_healthcare (pharmacy, hospital, clinic), meta_dispensing (yes, no, صيدلية الزيتوني أيمن), addr_street (شارع الحبيب بورقيبة, شارع الجمهورية, شارع البيئة), changeset_version (range 1.0–22.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-facilities-tunisia")
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	21.7%	8.1287 – 11.2181 (mean 10.3474)
y	float64	21.7%	32.8567 – 37.278 (mean 35.8838)
osm_id	int64	0.0%	27944768.0 – 13160906727.0 (mean 4758789791.5332)
osm_type	object	0.0%	node, way
completeness	float64	0.0%	6.25 – 40.625 (mean 14.8393)
loc_amenity	object	2.8%	pharmacy, hospital, clinic
meta_healthcare	object	33.7%	pharmacy, hospital, clinic
loc_name	object	23.2%	صيدلية الليل, pharmacie de nuit, Pharmacie de nuit
meta_dispensing	object	75.1%	yes, no, صيدلية الزيتوني أيمن
addr_street	object	73.3%	شارع الحبيب بورقيبة, شارع الجمهورية, شارع البيئة
addr_city	object	72.1%	صفاقس, تونس, قفصة
changeset_id	int64	0.0%	3101828.0 – 173251412.0 (mean 104883194.9195)
changeset_version	int64	0.0%	1.0 – 22.0 (mean 3.3496)
changeset_timestamp	datetime64[ns, UTC]	0.0%
meta_id	object	0.0%	1cd96ef5dd3b46de8da71ea48cba1449, a28114cb09df429dbb1e97fa6b505aca, a1bc0329b3bb4a0facadddc6388267a2
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-21

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Numeric Summary

Column	Min	Max	Mean	Median
x	8.1287	11.2181	10.3474	10.4497
y	32.8567	37.278	35.8838	36.3995
osm_id	27944768.0	13160906727.0	4758789791.5332	4546695789.5
completeness	6.25	40.625	14.8393	12.5
changeset_id	3101828.0	173251412.0	104883194.9195	104785595.0
changeset_version	1.0	22.0	3.3496	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. 20 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, meta_dispensing, addr_street, 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_tunisia,
  title     = {Tunisia Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/tunisia-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.