electricsheepafrica/africa-health-facilities-equatorial-guinea

---
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
- gnq
pretty_name: "Equatorial Guinea Healthsites"
dataset_info:
splits:
- name: train
num_examples: 16
- name: test
num_examples: 4
---

# Equatorial Guinea Healthsites

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

*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)** | 21 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 16 rows |
| **Test split** | 4 rows |
| **Geographic scope** | GNQ |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 8.7799–10.6145), `y` (range 1.8167–3.7639), `osm_type` (node, way), `loc_amenity` (hospital, pharmacy, clinic), `addr_city` (Malabo, Luba, Ebebiyín).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 265308137.0–12491930116.0), `loc_name` (Farmacia, Centro Médico La Paz, Super Pharm), `changeset_id` (range 72365664.0–162866917.0), `meta_id` (20572f095e5e4578a3789290e09f9820, dd1ce5e28cb44796bcbf9738596a3ff1, 0b7b4b100d064997be169c6972476c36), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 9.375–25.0), `meta_healthcare` (hospital, pharmacy, clinic), `changeset_version` (range 1.0–9.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 47.6% | 8.7799 – 10.6145 (mean 9.1397) |
| `y` | float64 | 47.6% | 1.8167 – 3.7639 (mean 3.2529) |
| `osm_id` | int64 | 0.0% | 265308137.0 – 12491930116.0 (mean 4801565710.5714) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 9.375 – 25.0 (mean 14.5833) |
| `loc_amenity` | object | 0.0% | hospital, pharmacy, clinic |
| `meta_healthcare` | object | 4.8% | hospital, pharmacy, clinic |
| `loc_name` | object | 14.3% | Farmacia, Centro Médico La Paz, Super Pharm |
| `addr_city` | object | 76.2% | Malabo, Luba, Ebebiyín |
| `changeset_id` | int64 | 0.0% | 72365664.0 – 162866917.0 (mean 127785138.381) |
| `changeset_version` | int64 | 0.0% | 1.0 – 9.0 (mean 3.381) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 20572f095e5e4578a3789290e09f9820, dd1ce5e28cb44796bcbf9738596a3ff1, 0b7b4b100d064997be169c6972476c36 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 8.7799 | 10.6145 | 9.1397 | 8.7837 |
| `y` | 1.8167 | 3.7639 | 3.2529 | 3.7438 |
| `osm_id` | 265308137.0 | 12491930116.0 | 4801565710.5714 | 4790712822.0 |
| `completeness` | 9.375 | 25.0 | 14.5833 | 12.5 |
| `changeset_id` | 72365664.0 | 162866917.0 | 127785138.381 | 133764423.0 |
| `changeset_version` | 1.0 | 9.0 | 3.381 | 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: `meta_operator`, `geo_bounds_url`, `meta_speciality`, `meta_operator_type`, `contact_phone`, `status_operational_status`.... 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`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/equatorial-guinea-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_equatorial_guinea,
title = {Equatorial Guinea Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/equatorial-guinea-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.*