electricsheepafrica/africa-health-facilities-togo

---
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
- tgo
pretty_name: "Togo Healthsites"
dataset_info:
splits:
- name: train
num_examples: 772
- name: test
num_examples: 193
---

# Togo Healthsites

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

*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)** | 965 |
| **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) |
| **Train split** | 772 rows |
| **Test split** | 193 rows |
| **Geographic scope** | TGO |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -0.078–1.8027), `y` (range 6.1169–11.1156), `osm_type` (node, way), `amenity` (hospital, pharmacy, clinic).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 102858259.0–13230239373.0), `name` (USP, Dépôt de pharmacie, Dispensaire), `changeset_id` (range 8196215.0–173222949.0), `uuid` (8bb275317f3149daa6e3e74bab88ae3c, 9d4b95002b1e4589b475720345f1cd2d, 923421275a3f4ce3858bf48ab601dcf3), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–28.125), `healthcare` (hospital, pharmacy, nurse), `changeset_version` (range 1.0–9.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 14.2% | -0.078 – 1.8027 (mean 1.1127) |
| `y` | float64 | 14.2% | 6.1169 – 11.1156 (mean 7.1282) |
| `osm_id` | int64 | 0.0% | 102858259.0 – 13230239373.0 (mean 6808499430.9378) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 28.125 (mean 11.3795) |
| `amenity` | object | 2.9% | hospital, pharmacy, clinic |
| `healthcare` | object | 72.2% | hospital, pharmacy, nurse |
| `name` | object | 7.2% | USP, Dépôt de pharmacie, Dispensaire |
| `changeset_id` | int64 | 0.0% | 8196215.0 – 173222949.0 (mean 102755340.8207) |
| `changeset_version` | int64 | 0.0% | 1.0 – 9.0 (mean 1.4964) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | 8bb275317f3149daa6e3e74bab88ae3c, 9d4b95002b1e4589b475720345f1cd2d, 923421275a3f4ce3858bf48ab601dcf3 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -0.078 | 1.8027 | 1.1127 | 1.1943 |
| `y` | 6.1169 | 11.1156 | 7.1282 | 6.3179 |
| `osm_id` | 102858259.0 | 13230239373.0 | 6808499430.9378 | 7626334870.0 |
| `completeness` | 6.25 | 28.125 | 11.3795 | 9.375 |
| `changeset_id` | 8196215.0 | 173222949.0 | 102755340.8207 | 106738868.0 |
| `changeset_version` | 1.0 | 9.0 | 1.4964 | 1.0 |

---

## 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`. 23 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: `healthcare`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/togo-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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