electricsheepafrica/africa-health-facilities-guinea-bissau

---
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
- gnb
pretty_name: "Guinea-Bissau Healthsites"
dataset_info:
splits:
- name: train
num_examples: 48
- name: test
num_examples: 12
---

# Guinea-Bissau Healthsites

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

*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)** | 61 |
| **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) |
| **Train split** | 48 rows |
| **Test split** | 12 rows |
| **Geographic scope** | GNB |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -16.4478–-14.2196), `y` (range 11.1587–12.2791), `osm_type` (node, way), `loc_amenity` (pharmacy, clinic, hospital), `meta_operator_type` (government, public).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 129946595.0–11355743273.0), `loc_name` (Hospital do setor de Tite, Hospital Marcelino Banca, Soga Centro de Saude), `changeset_id` (range 14808474.0–158831641.0), `meta_id` (9c5bf557f0bc4526bda595c8bd8f89c4, e0905bc30137497fabb78560f5523909, 7c38b797986c490ba8e12aeda7f7017d), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–43.75), `meta_healthcare` (pharmacy, hospital, clinic), `meta_operator` (moh, Ministerio di Saude, Ministerio de Saude), `changeset_version` (range 1.0–7.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 41.0% | -16.4478 – -14.2196 (mean -15.6912) |
| `y` | float64 | 41.0% | 11.1587 – 12.2791 (mean 11.7196) |
| `osm_id` | int64 | 0.0% | 129946595.0 – 11355743273.0 (mean 3605081740.3115) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 43.75 (mean 16.9057) |
| `loc_amenity` | object | 0.0% | pharmacy, clinic, hospital |
| `meta_healthcare` | object | 18.0% | pharmacy, hospital, clinic |
| `loc_name` | object | 26.2% | Hospital do setor de Tite, Hospital Marcelino Banca, Soga Centro de Saude |
| `meta_operator` | object | 72.1% | moh, Ministerio di Saude, Ministerio de Saude |
| `meta_operator_type` | object | 72.1% | government, public |
| `changeset_id` | int64 | 0.0% | 14808474.0 – 158831641.0 (mean 105094050.082) |
| `changeset_version` | int64 | 0.0% | 1.0 – 7.0 (mean 2.2787) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 9c5bf557f0bc4526bda595c8bd8f89c4, e0905bc30137497fabb78560f5523909, 7c38b797986c490ba8e12aeda7f7017d |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -16.4478 | -14.2196 | -15.6912 | -15.599 |
| `y` | 11.1587 | 12.2791 | 11.7196 | 11.8582 |
| `osm_id` | 129946595.0 | 11355743273.0 | 3605081740.3115 | 2130831642.0 |
| `completeness` | 6.25 | 43.75 | 16.9057 | 12.5 |
| `changeset_id` | 14808474.0 | 158831641.0 | 105094050.082 | 107880729.0 |
| `changeset_version` | 1.0 | 7.0 | 2.2787 | 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: `geo_bounds_url`, `meta_speciality`, `contact_phone`, `status_operational_status`, `access_hours`, `capacity_beds`.... 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`, `loc_name`, `meta_operator`, `meta_operator_type`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/guinea-bissau-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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