electricsheepafrica/africa-health-facilities-niger

---
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
- ner
pretty_name: "Niger Healthsites"
dataset_info:
splits:
- name: train
num_examples: 501
- name: test
num_examples: 125
---

# Niger Healthsites

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

*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)** | 627 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 501 rows |
| **Test split** | 125 rows |
| **Geographic scope** | NER |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 0.7453–13.2501), `y` (range 12.7084–18.6702), `osm_type` (node, way), `amenity` (clinic, pharmacy, hospital).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 221472038.0–12730463009.0), `name` (Pharmacie Populaire, CSI Nayi Lawan, Clinique Sawki), `source` (survey, MSFsurvey, bing), `changeset_id` (range 17454430.0–173291204.0), `uuid` (a08ca81c91a14314a67d67707a9cb781, 46d76e7d5f35439180f9b832667f6ecf, 825517c68ed24c15b2f0131a2f1b0d20) and 2 others.

**Other** — `completeness` (range 6.25–31.25), `healthcare` (pharmacy, hospital, clinic), `changeset_version` (range 1.0–15.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 22.0% | 0.7453 – 13.2501 (mean 5.2027) |
| `y` | float64 | 22.0% | 12.7084 – 18.6702 (mean 13.7356) |
| `osm_id` | int64 | 0.0% | 221472038.0 – 12730463009.0 (mean 5389210819.3987) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 31.25 (mean 12.6944) |
| `amenity` | object | 1.9% | clinic, pharmacy, hospital |
| `healthcare` | object | 50.2% | pharmacy, hospital, clinic |
| `name` | object | 8.6% | Pharmacie Populaire, CSI Nayi Lawan, Clinique Sawki |
| `source` | object | 64.0% | survey, MSFsurvey, bing |
| `changeset_id` | int64 | 0.0% | 17454430.0 – 173291204.0 (mean 103356952.6427) |
| `changeset_version` | int64 | 0.0% | 1.0 – 15.0 (mean 2.1882) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | a08ca81c91a14314a67d67707a9cb781, 46d76e7d5f35439180f9b832667f6ecf, 825517c68ed24c15b2f0131a2f1b0d20 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 0.7453 | 13.2501 | 5.2027 | 3.203 |
| `y` | 12.7084 | 18.6702 | 13.7356 | 13.5343 |
| `osm_id` | 221472038.0 | 12730463009.0 | 5389210819.3987 | 4420153449.0 |
| `completeness` | 6.25 | 31.25 | 12.6944 | 12.5 |
| `changeset_id` | 17454430.0 | 173291204.0 | 103356952.6427 | 101177411.0 |
| `changeset_version` | 1.0 | 15.0 | 2.1882 | 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: `operator`, `speciality`, `operator_type`, `operational_status`, `opening_hours`, `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`, `healthcare`, `source`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/niger-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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