electricsheepafrica/africa-congo-healthsites

---
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
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- geodata
- health
- health-facilities
- cog
pretty_name: "Congo-healthsites"
dataset_info:
splits:
- name: train
num_examples: 242
- name: test
num_examples: 60
---

# Congo-healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/congo-healthsites) · **License:** `cc-by-igo` · **Updated:** 2025-04-25

---

## 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. Temporal coverage is indicated by the `changeset_timestamp` column(s). Geographic scope: **COG**.

*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)** | 303 |
| **Columns** | 14 (6 numeric, 7 categorical, 1 datetime) |
| **Train split** | 242 rows |
| **Test split** | 60 rows |
| **Geographic scope** | COG |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-04-25 |

---

## Variables

**Geographic** — `x` (range 11.6649–18.6236), `y` (range -4.967–3.1824), `osm_type` (node, way), `amenity` (pharmacy, hospital, doctors).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 261075036.0–6856957760.0), `changeset_id` (range 13080934.0–75364371.0), `uuid` (429aff24ec644ff7ae597ac5426150c0, 37c57195ddb34e8ca547515b9152aaeb, 3d01c8da8f2b4afa938683dce723c7c8), `name` (Hopital Militaire, Pharmacie Maria, Netcare), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.0–17.0), `changeset_version` (range 1.0–3.0), `changeset_user` (ludarej, rich malonda, IMMERGIS CAMEROUN SAS).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-congo-healthsites")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 16.2% | 11.6649 – 18.6236 (mean 12.3591) |
| `y` | float64 | 16.2% | -4.967 – 3.1824 (mean -4.6494) |
| `osm_id` | int64 | 0.0% | 261075036.0 – 6856957760.0 (mean 4806108729.1386) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | int64 | 0.0% | 6.0 – 17.0 (mean 9.9274) |
| `amenity` | object | 11.2% | pharmacy, hospital, doctors |
| `changeset_id` | int64 | 0.0% | 13080934.0 – 75364371.0 (mean 60319138.4818) |
| `uuid` | object | 0.0% | 429aff24ec644ff7ae597ac5426150c0, 37c57195ddb34e8ca547515b9152aaeb, 3d01c8da8f2b4afa938683dce723c7c8 |
| `changeset_version` | int64 | 0.0% | 1.0 – 3.0 (mean 1.198) |
| `changeset_timestamp` | datetime64[ns] | 0.0% | |
| `name` | object | 12.2% | Hopital Militaire, Pharmacie Maria, Netcare |
| `changeset_user` | object | 0.0% | ludarej, rich malonda, IMMERGIS CAMEROUN SAS |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 11.6649 | 18.6236 | 12.3591 | 11.8754 |
| `y` | -4.967 | 3.1824 | -4.6494 | -4.7857 |
| `osm_id` | 261075036.0 | 6856957760.0 | 4806108729.1386 | 6214531587.0 |
| `completeness` | 6.0 | 17.0 | 9.9274 | 10.0 |
| `changeset_id` | 13080934.0 | 75364371.0 | 60319138.4818 | 66903706.0 |
| `changeset_version` | 1.0 | 3.0 | 1.198 | 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: `is_in_health_zone`, `speciality`, `addr_full`, `operator`, `water_source`, `insurance`.... 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/congo-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_congo_healthsites,
title = {Congo-healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/congo-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.*