---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- facilities-infrastructure
- health
- health-facilities
- hxl
- som
pretty_name: "Somalia - Health Facilities"
dataset_info:
  splits:
    - name: train
      num_examples: 416
    - name: test
      num_examples: 104
---

# Somalia - Health Facilities

**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/somalia-health-facilities-data) · **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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/somalia-health-facilities-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*