electricsheepafrica/africa-health-facilities-algeria

---
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
- dza
pretty_name: "Algeria Healthsites"
dataset_info:
splits:
- name: train
num_examples: 7473
- name: test
num_examples: 1868
---

# Algeria Healthsites

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

*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)** | 9,342 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 7,473 rows |
| **Test split** | 1,868 rows |
| **Geographic scope** | DZA |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -8.1396–9.5745), `y` (range 19.5733–37.0651), `osm_type` (node, way), `amenity` (pharmacy, doctors, clinic), `addr_city` (Oum El Bouaghi, وهران, kolea القليعة).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 24505936.0–13231886213.0), `name` (Polyclinique, Dispensaire, مستوصف), `changeset_id` (range 7875079.0–173285658.0), `uuid` (e21dced4a0e247f49068415ac25af92a, 6dcc29bc90f04a88bda9e82749c8f1b7, 6934e300fb7d4cc29f02e2e0e07263a0), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–37.5), `healthcare` (pharmacy, doctor, clinic), `changeset_version` (range 1.0–26.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 20.3% | -8.1396 – 9.5745 (mean 3.3049) |
| `y` | float64 | 20.3% | 19.5733 – 37.0651 (mean 35.81) |
| `osm_id` | int64 | 0.0% | 24505936.0 – 13231886213.0 (mean 4601805327.0866) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 37.5 (mean 12.6913) |
| `amenity` | object | 1.4% | pharmacy, doctors, clinic |
| `healthcare` | object | 49.4% | pharmacy, doctor, clinic |
| `name` | object | 32.2% | Polyclinique, Dispensaire, مستوصف |
| `addr_city` | object | 74.7% | Oum El Bouaghi, وهران, kolea القليعة |
| `changeset_id` | int64 | 0.0% | 7875079.0 – 173285658.0 (mean 96693460.3157) |
| `changeset_version` | int64 | 0.0% | 1.0 – 26.0 (mean 3.0932) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | e21dced4a0e247f49068415ac25af92a, 6dcc29bc90f04a88bda9e82749c8f1b7, 6934e300fb7d4cc29f02e2e0e07263a0 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -8.1396 | 9.5745 | 3.3049 | 3.0796 |
| `y` | 19.5733 | 37.0651 | 35.81 | 36.2546 |
| `osm_id` | 24505936.0 | 13231886213.0 | 4601805327.0866 | 4526847941.0 |
| `completeness` | 6.25 | 37.5 | 12.6913 | 12.5 |
| `changeset_id` | 7875079.0 | 173285658.0 | 96693460.3157 | 94401597.0 |
| `changeset_version` | 1.0 | 26.0 | 3.0932 | 3.0 |

---

## 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: `operator`, `source`, `speciality`, `operator_type`, `operational_status`, `opening_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`, `healthcare`, `name`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/algeria-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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