electricsheepafrica/africa-health-facilities-madagascar

---
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
- mdg
pretty_name: "Madagascar Healthsites"
dataset_info:
splits:
- name: train
num_examples: 1256
- name: test
num_examples: 314
---

# Madagascar Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/madagascar-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: **MDG**.

*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)** | 1,571 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 1,256 rows |
| **Test split** | 314 rows |
| **Geographic scope** | MDG |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 43.2403–50.453), `y` (range -25.4983–-12.2685), `osm_type` (node, way), `amenity` (doctors, pharmacy, hospital), `operator_type` (public, private, religious).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 37800948.0–13103564831.0), `name` (Dépôt de médicaments, Mahasoa, CSB II Vohilava), `changeset_id` (range 3374143.0–172661771.0), `uuid` (b642eb5a442247049c40f760a12add48, ba6e568b63604395b1c00431078d3527, c8c46bdc6b694987afb2155b20a3e8d8), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–37.5), `healthcare` (doctor, hospital, pharmacy), `changeset_version` (range 1.0–18.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 22.3% | 43.2403 – 50.453 (mean 47.5876) |
| `y` | float64 | 22.3% | -25.4983 – -12.2685 (mean -19.0397) |
| `osm_id` | int64 | 0.0% | 37800948.0 – 13103564831.0 (mean 6454701438.0777) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 37.5 (mean 13.0411) |
| `amenity` | object | 2.1% | doctors, pharmacy, hospital |
| `healthcare` | object | 34.4% | doctor, hospital, pharmacy |
| `name` | object | 16.4% | Dépôt de médicaments, Mahasoa, CSB II Vohilava |
| `operator_type` | object | 65.7% | public, private, religious |
| `changeset_id` | int64 | 0.0% | 3374143.0 – 172661771.0 (mean 118597343.7931) |
| `changeset_version` | int64 | 0.0% | 1.0 – 18.0 (mean 1.944) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | b642eb5a442247049c40f760a12add48, ba6e568b63604395b1c00431078d3527, c8c46bdc6b694987afb2155b20a3e8d8 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 43.2403 | 50.453 | 47.5876 | 47.5318 |
| `y` | -25.4983 | -12.2685 | -19.0397 | -18.9107 |
| `osm_id` | 37800948.0 | 13103564831.0 | 6454701438.0777 | 7578354260.0 |
| `completeness` | 6.25 | 37.5 | 13.0411 | 12.5 |
| `changeset_id` | 3374143.0 | 172661771.0 | 118597343.7931 | 127580026.0 |
| `changeset_version` | 1.0 | 18.0 | 1.944 | 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: `operator`, `source`, `speciality`, `operational_status`, `opening_hours`, `beds`.... 1 exact duplicate rows were removed. 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`, `healthcare`, `operator_type`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/madagascar-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_madagascar,
title = {Madagascar Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/madagascar-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.*