electricsheepafrica/africa-health-facilities-mauritius

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health-facilities
- hxl
- mus
pretty_name: "Mauritius Healthsites"
dataset_info:
splits:
- name: train
num_examples: 272
- name: test
num_examples: 68
---

# Mauritius Healthsites

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

*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)** | 341 |
| **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) |
| **Train split** | 272 rows |
| **Test split** | 68 rows |
| **Geographic scope** | MUS |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 57.3631–63.4532), `y` (range -20.4948–-19.6804), `osm_type` (node, way), `loc_amenity` (pharmacy, clinic, hospital).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 224268361.0–12202000989.0), `loc_name` (Cabinet Medical, Green Cross, Clinique du Nord), `changeset_id` (range 11638347.0–172616014.0), `meta_id` (83c1e1d6c1a04705aa510cb34f311c5b, efec2221e7204e619dc78b220f6dcd5d, 799d663c9e0c440e9315e532b47cc9e5), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–28.125), `meta_healthcare` (pharmacy, clinic, dentist), `geo_bounds_url` (Kaart Ground Survey 2017, Bing), `addr_street` (Royal Road Rose Hill, Candos Vacoas Road, Saint Jean Road), `changeset_version` (range 1.0–9.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 27.6% | 57.3631 – 63.4532 (mean 57.6241) |
| `y` | float64 | 27.6% | -20.4948 – -19.6804 (mean -20.2296) |
| `osm_id` | int64 | 0.0% | 224268361.0 – 12202000989.0 (mean 4235113942.7713) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 28.125 (mean 15.1301) |
| `loc_amenity` | object | 7.0% | pharmacy, clinic, hospital |
| `meta_healthcare` | object | 24.0% | pharmacy, clinic, dentist |
| `loc_name` | object | 13.5% | Cabinet Medical, Green Cross, Clinique du Nord |
| `geo_bounds_url` | object | 61.9% | Kaart Ground Survey 2017, Bing |
| `addr_street` | object | 48.7% | Royal Road Rose Hill, Candos Vacoas Road, Saint Jean Road |
| `changeset_id` | int64 | 0.0% | 11638347.0 – 172616014.0 (mean 106441759.5044) |
| `changeset_version` | int64 | 0.0% | 1.0 – 9.0 (mean 2.3607) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 83c1e1d6c1a04705aa510cb34f311c5b, efec2221e7204e619dc78b220f6dcd5d, 799d663c9e0c440e9315e532b47cc9e5 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 57.3631 | 63.4532 | 57.6241 | 57.5051 |
| `y` | -20.4948 | -19.6804 | -20.2296 | -20.2444 |
| `osm_id` | 224268361.0 | 12202000989.0 | 4235113942.7713 | 4978159864.0 |
| `completeness` | 6.25 | 28.125 | 15.1301 | 15.625 |
| `changeset_id` | 11638347.0 | 172616014.0 | 106441759.5044 | 118259623.0 |
| `changeset_version` | 1.0 | 9.0 | 2.3607 | 2.0 |

---

## 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`. 21 column(s) with >80% missing values were removed: `meta_operator`, `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`, `geo_bounds_url`, `addr_street`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/mauritius-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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