electricsheepafrica/africa-somalia-health-facilities-data

Somalia - Health Facilities

Publisher: World Health Organization · Source: HDX · License: cc-by-igo · Updated: 2025-04-10

---

Abstract

Somalia location of health facilities by region and district.

Each row in this dataset represents facility or site records. Data was last updated on HDX on 2025-04-10. Geographic scope: SOM.

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

---

Dataset Characteristics

Domain	Public health
Unit of observation	Facility or site records
Rows (total)	520
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	416 rows
Test split	104 rows
Geographic scope	SOM
Publisher	World Health Organization
HDX last updated	2025-04-10

---

Variables

Geographic — region ( Bari , Lower Juba , Bay ), district ( Baydhaba, Qardho, Bossaso), health_facility_name (Buulo Jadiid MCH, Kismayo General Hospital, Kahandho PHU), type (Health Center (HC), Primary Health Unit (PHU), Mobile Clinic), latiitude (range -1.636–973351.0) and 1 others.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-17).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-somalia-health-facilities-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
region	object	2.1%	Bari , Lower Juba , Bay
district	object	2.1%	Baydhaba, Qardho, Bossaso
health_facility_name	object	0.0%	Buulo Jadiid MCH, Kismayo General Hospital, Kahandho PHU
type	object	4.8%	Health Center (HC), Primary Health Unit (PHU), Mobile Clinic
latiitude	float64	45.2%	-1.636 – 973351.0 (mean 7381.9517)
longititude	float64	45.2%	40.5953 – 45538580.0 (mean 490619.4983)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

---

Numeric Summary

Column	Min	Max	Mean	Median
latiitude	-1.636	973351.0	7381.9517	7.1501
longititude	40.5953	45538580.0	490619.4983	47.4149

---

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. 2 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.

---

Limitations

• Data originates from World Health Organization 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: latiitude, longititude.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_somalia_health_facilities_data,
  title     = {Somalia - Health Facilities},
  author    = {World Health Organization},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/somalia-health-facilities-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.