electricsheepafrica/africa-sierra-leone-health-sites

Sierra Leone Healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · License: other-pd-nr · 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: SLE.

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)	1,731
Columns	13 (2 numeric, 11 categorical, 0 datetime)
Train split	1,384 rows
Test split	346 rows
Geographic scope	SLE
Publisher	Global Healthsites Mapping Project
HDX last updated	2025-04-25

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Variables

Geographic — province (Northern, Southern, Western), district (Western Urban, Bo, Kenema), health_facility (Foindu, Kpetema, Bendu), nature_of_facility (Health Post, Health Centre, Hospital), lon (range -13.2949–-10.3066) and 2 others.

Identifier / Metadata — id (HF0233, HF0001, HF1163), source_of_info_name (SL-MOH, MOHS DHIS, Yellow Pages, Cybo), esa_source (HDX), esa_processed.

Other — address (Cline Town, Kenema Town, Tongi Tingi), activities (Maternal and Child Health Post, Community Health Post, Community Health Centre).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sierra-leone-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	0.0%	HF0233, HF0001, HF1163
province	object	0.1%	Northern, Southern, Western
district	object	0.1%	Western Urban, Bo, Kenema
address	object	76.7%	Cline Town, Kenema Town, Tongi Tingi
health_facility	object	0.0%	Foindu, Kpetema, Bendu
nature_of_facility	object	0.1%	Health Post, Health Centre, Hospital
activities	object	6.6%	Maternal and Child Health Post, Community Health Post, Community Health Centre
lon	float64	5.4%	-13.2949 – -10.3066 (mean -12.1294)
lat	float64	5.4%	6.9694 – 9.9745 (mean 8.3993)
source_of_info_name	object	0.1%	SL-MOH, MOHS DHIS, Yellow Pages, Cybo
raw_hdx_data_link	object	0.1%	https://data.humdata.org/dataset/sierra-leone-healthsites
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
lon	-13.2949	-10.3066	-12.1294	-12.0611
lat	6.9694	9.9745	8.3993	8.4725

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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. 8 column(s) with >80% missing values were removed: scope_of_services, ancillary_services, inpatient_services, ownership, staff, tags.... 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: address.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_sierra_leone_health_sites,
  title     = {Sierra Leone Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sierra-leone-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.