electricsheepafrica/africa-health-facilities-congo-dem-rep

---
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
- cod
pretty_name: "Democratic Republic of the Congo Healthsites"
dataset_info:
splits:
- name: train
num_examples: 3165
- name: test
num_examples: 791
---

# Democratic Republic of the Congo Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/democratic-republic-of-the-congo-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: **COD**.

*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)** | 3,957 |
| **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) |
| **Train split** | 3,165 rows |
| **Test split** | 791 rows |
| **Geographic scope** | COD |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 12.3431–31.1278), `y` (range -11.7058–3.5055), `osm_type` (node, way), `loc_amenity` (doctors, clinic, hospital).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 52173208.0–13180731996.0), `loc_name` (Centre de santé, Centre de Santé, Spital Wamba-Luadi), `changeset_id` (range 5147571.0–172854454.0), `meta_id` (a74c4fad7bc7458f966dc283d470ad9e, 8ff3afb9d489488ea92934818f935c85, 657026b1313845cfbca745f67c9e4e76), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–37.5), `meta_healthcare` (alternative, hospital, doctor), `geo_bounds_url` (MSFsurvey, OMS-DSNIS, MSF), `addr_street` (Université, Nsanga, Kimayala), `changeset_version` (range 1.0–17.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 21.7% | 12.3431 – 31.1278 (mean 22.9484) |
| `y` | float64 | 21.7% | -11.7058 – 3.5055 (mean -2.7734) |
| `osm_id` | int64 | 0.0% | 52173208.0 – 13180731996.0 (mean 6085918002.9078) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 37.5 (mean 13.8607) |
| `loc_amenity` | object | 1.8% | doctors, clinic, hospital |
| `meta_healthcare` | object | 67.3% | alternative, hospital, doctor |
| `loc_name` | object | 4.3% | Centre de santé, Centre de Santé, Spital Wamba-Luadi |
| `geo_bounds_url` | object | 71.6% | MSFsurvey, OMS-DSNIS, MSF |
| `addr_street` | object | 73.7% | Université, Nsanga, Kimayala |
| `changeset_id` | int64 | 0.0% | 5147571.0 – 172854454.0 (mean 104638381.4549) |
| `changeset_version` | int64 | 0.0% | 1.0 – 17.0 (mean 3.2302) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | a74c4fad7bc7458f966dc283d470ad9e, 8ff3afb9d489488ea92934818f935c85, 657026b1313845cfbca745f67c9e4e76 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 12.3431 | 31.1278 | 22.9484 | 27.4834 |
| `y` | -11.7058 | 3.5055 | -2.7734 | -4.0631 |
| `osm_id` | 52173208.0 | 13180731996.0 | 6085918002.9078 | 6557833167.0 |
| `completeness` | 6.25 | 37.5 | 13.8607 | 12.5 |
| `changeset_id` | 5147571.0 | 172854454.0 | 104638381.4549 | 110787049.0 |
| `changeset_version` | 1.0 | 17.0 | 3.2302 | 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/democratic-republic-of-the-congo-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_congo_dem_rep,
title = {Democratic Republic of the Congo Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/democratic-republic-of-the-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.*