electricsheepafrica/africa-cote-divoire-healthsites

Côte d'Ivoire-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: CIV.

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)	1,668
Columns	15 (6 numeric, 8 categorical, 1 datetime)
Train split	1,334 rows
Test split	333 rows
Geographic scope	CIV
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-25

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Variables

Geographic — x (range -8.2954–-2.7927), y (range 4.6541–9.6108), osm_type (node, way), amenity (pharmacy, doctors, hospital), operator_type (private, public, ngo).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range -4034977.0–6781232575.0), changeset_id (range 4955551.0–75313842.0), uuid (ee57a432a8d94e5fabd7e7c7a29748c7, 1c9dae47930d4967ba489353569439de, c1e434e2fdd74f45aa8cbd089cd83c84), name (Clinique Chinoise, Infirmerie, Clinique), esa_source (HDX) and 1 others.

Other — completeness (range 6.0–31.0), changeset_version (range 1.0–10.0), changeset_user (sommerluk, anebophil, ulrichm).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cote-divoire-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	4.5%	-8.2954 – -2.7927 (mean -4.7813)
y	float64	4.5%	4.6541 – 9.6108 (mean 5.8709)
osm_id	int64	0.0%	-4034977.0 – 6781232575.0 (mean 4949679221.6535)
osm_type	object	0.0%	node, way
completeness	int64	0.0%	6.0 – 31.0 (mean 12.5929)
amenity	object	0.6%	pharmacy, doctors, hospital
changeset_id	float64	0.1%	4955551.0 – 75313842.0 (mean 58441148.0978)
uuid	object	0.0%	ee57a432a8d94e5fabd7e7c7a29748c7, 1c9dae47930d4967ba489353569439de, c1e434e2fdd74f45aa8cbd089cd83c84
changeset_version	float64	0.1%	1.0 – 10.0 (mean 1.904)
changeset_timestamp	datetime64[ns]	0.1%
name	object	4.5%	Clinique Chinoise, Infirmerie, Clinique
changeset_user	object	0.1%	sommerluk, anebophil, ulrichm
operator_type	object	76.5%	private, public, ngo
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-07

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Numeric Summary

Column	Min	Max	Mean	Median
x	-8.2954	-2.7927	-4.7813	-4.0811
y	4.6541	9.6108	5.8709	5.3684
osm_id	-4034977.0	6781232575.0	4949679221.6535	5568888182.5
completeness	6.0	31.0	12.5929	10.0
changeset_id	4955551.0	75313842.0	58441148.0978	62691687.0
changeset_version	1.0	10.0	1.904	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. 21 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: operator_type.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cote_divoire_healthsites,
  title     = {Côte d'Ivoire-healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cote-divoire-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.