electricsheepafrica/africa-liberia-health-sites

Liberia healthsites

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

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Abstract

This data set shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Each row in this dataset represents facility or site records. Data was last updated on HDX on 2025-04-25. Geographic scope: LBR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Public health
Unit of observation	Facility or site records
Rows (total)	786
Columns	10 (2 numeric, 8 categorical, 0 datetime)
Train split	628 rows
Test split	157 rows
Geographic scope	LBR
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-25

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Variables

Geographic — health_facility (Juduken, Edward Memorial, Bardnersville Health Center), nature_of_facility (Clinic, Health Center, Hospital), lon (range -101.7067–6.854), lat (range 4.3637–633658.0).

Identifier / Metadata — id (UN005, LibWes, MTI105), source_of_info_name (List from: Medical Teams International, http://www.tlcafrica.com/lisgis/lisgis.htm, http://yellowpages.cybo.com/SL/bomi,_sierra_leone/hospitals/), source_of_info_url (List from: Medical Teams International, http://www.tlcafrica.com/lisgis/lisgis.htm, http://yellowpages.cybo.com/SL/bomi,_sierra_leone/hospitals/), esa_source (HDX), esa_processed (2026-04-13).

Other — address (District 3, Duport Road, Gardnesville).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-liberia-health-sites")
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
id	object	0.4%	UN005, LibWes, MTI105
address	object	78.1%	District 3, Duport Road, Gardnesville
health_facility	object	0.4%	Juduken, Edward Memorial, Bardnersville Health Center
nature_of_facility	object	16.3%	Clinic, Health Center, Hospital
lon	float64	0.0%	-101.7067 – 6.854 (mean -10.0025)
lat	float64	0.0%	4.3637 – 633658.0 (mean 812.6158)
source_of_info_name	object	76.2%	List from: Medical Teams International, http://www.tlcafrica.com/lisgis/lisgis.htm, http://yellowpages.cybo.com/SL/bomi,_sierra_leone/hospitals/
source_of_info_url	object	76.2%	List from: Medical Teams International, http://www.tlcafrica.com/lisgis/lisgis.htm, http://yellowpages.cybo.com/SL/bomi,_sierra_leone/hospitals/
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-13

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Numeric Summary

Column	Min	Max	Mean	Median
lon	-101.7067	6.854	-10.0025	-10.0896
lat	4.3637	633658.0	812.6158	6.3613

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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. 12 column(s) with >80% missing values were removed: province, district, scope_of_services, ancillary_services, activities, inpatient_services.... 3 exact duplicate rows were removed. 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: address, source_of_info_name, source_of_info_url.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_liberia_health_sites,
  title     = {Liberia healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/liberia-health-sites},
  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.