electricsheepafrica/africa-health-facilities-angola

---
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
- ago
pretty_name: "Angola Healthsites"
dataset_info:
splits:
- name: train
num_examples: 631
- name: test
num_examples: 157
---

# Angola Healthsites

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

*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)** | 789 |
| **Columns** | 17 (6 numeric, 10 categorical, 0 datetime) |
| **Train split** | 631 rows |
| **Test split** | 157 rows |
| **Geographic scope** | AGO |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 11.8581–20.8358), `y` (range -17.0688–-5.5439), `osm_type` (node, way), `loc_amenity` (pharmacy, hospital, clinic), `addr_city` (Luanda, Lubango, Kilamba Kiaxi).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 60355326.0–13228105402.0), `loc_name` (Farmácia, Posto de saúde, Centro Médico), `changeset_id` (range 15111467.0–173192363.0), `meta_id` (84ae409609cb4d059a5b5b455c0db24a, 0a5291da85ae4068a7c5e5396f0e4ed1, b353cabd467147e493401fcf20f3ccdb), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–34.375), `meta_healthcare` (pharmacy, hospital, clinic), `meta_dispensing` (yes), `addr_street` (Avenida Pedro de C. Vandunem-Loy, Avenida 21 de Janeiro, Estrada da Samba), `changeset_version` (range 1.0–13.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 35.7% | 11.8581 – 20.8358 (mean 13.3967) |
| `y` | float64 | 35.7% | -17.0688 – -5.5439 (mean -10.2885) |
| `osm_id` | int64 | 0.0% | 60355326.0 – 13228105402.0 (mean 4797721438.839) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 34.375 (mean 16.6469) |
| `loc_amenity` | object | 2.0% | pharmacy, hospital, clinic |
| `meta_healthcare` | object | 12.9% | pharmacy, hospital, clinic |
| `loc_name` | object | 14.1% | Farmácia, Posto de saúde, Centro Médico |
| `meta_dispensing` | object | 76.2% | yes |
| `addr_street` | object | 61.1% | Avenida Pedro de C. Vandunem-Loy, Avenida 21 de Janeiro, Estrada da Samba |
| `addr_city` | object | 48.3% | Luanda, Lubango, Kilamba Kiaxi |
| `changeset_id` | int64 | 0.0% | 15111467.0 – 173192363.0 (mean 104417238.6413) |
| `changeset_version` | int64 | 0.0% | 1.0 – 13.0 (mean 2.0507) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 84ae409609cb4d059a5b5b455c0db24a, 0a5291da85ae4068a7c5e5396f0e4ed1, b353cabd467147e493401fcf20f3ccdb |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 11.8581 | 20.8358 | 13.3967 | 13.2486 |
| `y` | -17.0688 | -5.5439 | -10.2885 | -8.9223 |
| `osm_id` | 60355326.0 | 13228105402.0 | 4797721438.839 | 5148614726.0 |
| `completeness` | 6.25 | 34.375 | 16.6469 | 15.625 |
| `changeset_id` | 15111467.0 | 173192363.0 | 104417238.6413 | 106622918.0 |
| `changeset_version` | 1.0 | 13.0 | 2.0507 | 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`. 20 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`, `meta_dispensing`, `addr_street`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/angola-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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