electricsheepafrica/africa-health-facilities-burkina-faso

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health-facilities
- hxl
- bfa
pretty_name: "Burkina Faso Healthsites"
dataset_info:
splits:
- name: train
num_examples: 966
- name: test
num_examples: 241
---

# Burkina Faso Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/burkina-faso-healthsites) · **License:** `ODbL` · **Updated:** 2025-10-15

---

## 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 tabular records. Data was last updated on HDX on 2025-10-15. Geographic scope: **BFA**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 1,208 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 966 rows |
| **Test split** | 241 rows |
| **Geographic scope** | BFA |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -5.3114–2.016), `y` (range 9.9042–14.7395), `osm_type` (node, way), `loc_amenity` (hospital, doctors, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 27570857.0–13230254219.0), `loc_name` (CSPS, Dispensaire, Centre Médical), `changeset_id` (range 310248.0–173222949.0), `meta_id` (dc6176c624ac42749c94988b065f5574, 70f4e38fb2844e27a5ee41a4b20efe28, 9e2caa6a5a7e4bcf9ef80ab50f95f16b), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–34.375), `meta_healthcare` (hospital, pharmacy, doctor), `meta_operator` (Public, Ministère de la Santé du Burkina Faso, MS), `changeset_version` (range 1.0–13.0).

---

## Quick Start

```python
from datasets import load_dataset

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

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 25.9% | -5.3114 – 2.016 (mean -1.9488) |
| `y` | float64 | 25.9% | 9.9042 – 14.7395 (mean 12.4306) |
| `osm_id` | int64 | 0.0% | 27570857.0 – 13230254219.0 (mean 5376431054.5869) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 34.375 (mean 12.3163) |
| `loc_amenity` | object | 0.5% | hospital, doctors, pharmacy |
| `meta_healthcare` | object | 75.2% | hospital, pharmacy, doctor |
| `loc_name` | object | 10.4% | CSPS, Dispensaire, Centre Médical |
| `meta_operator` | object | 60.4% | Public, Ministère de la Santé du Burkina Faso, MS |
| `changeset_id` | int64 | 0.0% | 310248.0 – 173222949.0 (mean 97979094.5099) |
| `changeset_version` | int64 | 0.0% | 1.0 – 13.0 (mean 1.7243) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | dc6176c624ac42749c94988b065f5574, 70f4e38fb2844e27a5ee41a4b20efe28, 9e2caa6a5a7e4bcf9ef80ab50f95f16b |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -5.3114 | 2.016 | -1.9488 | -1.8376 |
| `y` | 9.9042 | 14.7395 | 12.4306 | 12.3703 |
| `osm_id` | 27570857.0 | 13230254219.0 | 5376431054.5869 | 4340343677.0 |
| `completeness` | 6.25 | 34.375 | 12.3163 | 12.5 |
| `changeset_id` | 310248.0 | 173222949.0 | 97979094.5099 | 99665215.0 |
| `changeset_version` | 1.0 | 13.0 | 1.7243 | 1.0 |

---

## 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`. 22 column(s) with >80% missing values were removed: `geo_bounds_url`, `meta_speciality`, `meta_operator_type`, `contact_phone`, `status_operational_status`, `access_hours`.... 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.

---

## 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: `x`, `y`, `meta_healthcare`, `meta_operator`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/burkina-faso-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_burkina_faso,
title = {Burkina Faso Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/burkina-faso-healthsites},
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.*