electricsheepafrica/africa-ivory-coast-health-facilities

Cote 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 time-series observations. Data was last updated on HDX on 2025-04-25. 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	Time-series observations
Rows (total)	204
Columns	12 (1 numeric, 11 categorical, 0 datetime)
Train split	163 rows
Test split	40 rows
Geographic scope	CIV
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-25

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Variables

Geographic — type (hospital, clinic).

Temporal — date_modified (2015-11-17T09:43:57.838Z, 2015-12-11T10:51:22.924Z, 2016-11-05T15:18:27Z).

Identifier / Metadata — source_url (http://www.standbytaskforce.org/, http://www.openstreetmap.org/node/3007144564, http://www.openstreetmap.org/node/4019808596), uuid (6f6148282b654930a1f2bd9feb9b378e, 383ae58ff82c4f608a53c1a65c1dcc1d, 98efbc22d80f4d30b2cdb7683ca18239), name (Hôpital général de Bingerville, Hôpital psychiatrique de Bingerville, Hopital General d'Alépé), source (OpenStreetMap, Standby Task Force), esa_source (HDX) and 1 others.

Other — upstream (openstreetmap¶n3007144564, OpenStreetMap¶n4019808596, OpenStreetMap¶w340252445), completeness (35.29%, 29.41%, 41.18%), what3words (tonal.spell.myth, education.highbrow.ticket, drip.innocence.wriggled), version (range 2.0–2.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ivory-coast-health-facilities")
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
upstream	object	0.0%	openstreetmap¶n3007144564, OpenStreetMap¶n4019808596, OpenStreetMap¶w340252445
date_modified	object	0.0%	2015-11-17T09:43:57.838Z, 2015-12-11T10:51:22.924Z, 2016-11-05T15:18:27Z
source_url	object	0.0%	http://www.standbytaskforce.org/, http://www.openstreetmap.org/node/3007144564, http://www.openstreetmap.org/node/4019808596
completeness	object	0.0%	35.29%, 29.41%, 41.18%
uuid	object	0.0%	6f6148282b654930a1f2bd9feb9b378e, 383ae58ff82c4f608a53c1a65c1dcc1d, 98efbc22d80f4d30b2cdb7683ca18239
name	object	0.0%	Hôpital général de Bingerville, Hôpital psychiatrique de Bingerville, Hopital General d'Alépé
type	object	1.5%	hospital, clinic
source	object	0.0%	OpenStreetMap, Standby Task Force
what3words	object	0.0%	tonal.spell.myth, education.highbrow.ticket, drip.innocence.wriggled
version	int64	0.0%	2.0 – 2.0 (mean 2.0)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
version	2.0	2.0	2.0	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. 5 column(s) with >80% missing values were removed: physical_address, url, email, phone, raw_source. 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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ivory_coast_health_facilities,
  title     = {Cote d'Ivoire healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ivory-coast-health-facilities},
  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.