electricsheepafrica/africa-health-facilities-mali

---
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
- mli
pretty_name: "Mali Healthsites"
dataset_info:
splits:
- name: train
num_examples: 1206
- name: test
num_examples: 301
---

# Mali Healthsites

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

*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)** | 1,508 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 1,206 rows |
| **Test split** | 301 rows |
| **Geographic scope** | MLI |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -12.1965–2.4909), `y` (range 10.499–20.2068), `osm_type` (node, way), `amenity` (clinic, pharmacy, doctors).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 93195585.0–13208575102.0), `name` (CSCOM, Centre de Santé, Cabinet médical), `source` (OMS Survey, Cluster Nutrition, survey), `changeset_id` (range 6859513.0–173136650.0), `uuid` (1d7c09af43574e4788144a7e3baa7995, f1e68098dd144256a6c3a269bce4157b, 5e936b3283814c14951829d93fc1348d) and 2 others.

**Other** — `completeness` (range 6.25–43.75), `healthcare` (clinic, hospital, pharmacy), `changeset_version` (range 1.0–11.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 10.2% | -12.1965 – 2.4909 (mean -7.315) |
| `y` | float64 | 10.2% | 10.499 – 20.2068 (mean 13.0171) |
| `osm_id` | int64 | 0.0% | 93195585.0 – 13208575102.0 (mean 4712138502.1943) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 43.75 (mean 12.5373) |
| `amenity` | object | 1.7% | clinic, pharmacy, doctors |
| `healthcare` | object | 71.9% | clinic, hospital, pharmacy |
| `name` | object | 9.5% | CSCOM, Centre de Santé, Cabinet médical |
| `source` | object | 70.8% | OMS Survey, Cluster Nutrition, survey |
| `changeset_id` | int64 | 0.0% | 6859513.0 – 173136650.0 (mean 96886082.691) |
| `changeset_version` | int64 | 0.0% | 1.0 – 11.0 (mean 2.2573) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | 1d7c09af43574e4788144a7e3baa7995, f1e68098dd144256a6c3a269bce4157b, 5e936b3283814c14951829d93fc1348d |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -12.1965 | 2.4909 | -7.315 | -7.95 |
| `y` | 10.499 | 20.2068 | 13.0171 | 12.6478 |
| `osm_id` | 93195585.0 | 13208575102.0 | 4712138502.1943 | 4261924949.5 |
| `completeness` | 6.25 | 43.75 | 12.5373 | 12.5 |
| `changeset_id` | 6859513.0 | 173136650.0 | 96886082.691 | 95347947.0 |
| `changeset_version` | 1.0 | 11.0 | 2.2573 | 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`. 22 column(s) with >80% missing values were removed: `operator`, `speciality`, `operator_type`, `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: `healthcare`, `source`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/mali-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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