electricsheepafrica/africa-gambia-healthsites

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- geodata
- health
- health-facilities
- gmb
pretty_name: "Gambia-healthsites"
dataset_info:
splits:
- name: train
num_examples: 58
- name: test
num_examples: 14
---

# Gambia-healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/gambia-healthsites) · **License:** `cc-by-igo` · **Updated:** 2025-04-25

---

## 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. Temporal coverage is indicated by the `changeset_timestamp` column(s). Geographic scope: **GMB**.

*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)** | 73 |
| **Columns** | 14 (6 numeric, 7 categorical, 1 datetime) |
| **Train split** | 58 rows |
| **Test split** | 14 rows |
| **Geographic scope** | GMB |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-04-25 |

---

## Variables

**Geographic** — `x` (range -16.7964–-14.0158), `y` (range 13.0931–13.7792), `osm_type` (way, node), `amenity` (hospital, clinic, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 125548270.0–6576674985.0), `changeset_id` (range 18241309.0–75320524.0), `uuid` (09a60dadc51d45c4afd7d1078fc98c09, ff347d8b87e9443b98c1c2735ca12c7a, 0722456c720b47e59eb12df481597a4d), `name` (Medical Research Council, Diabugu Batapa Health Center, Manding Drug Store), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.0–24.0), `changeset_version` (range 1.0–6.0), `changeset_user` (MorganJ14, cmsandquist, Caitlyn W).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-gambia-healthsites")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 56.2% | -16.7964 – -14.0158 (mean -15.8398) |
| `y` | float64 | 56.2% | 13.0931 – 13.7792 (mean 13.4176) |
| `osm_id` | int64 | 0.0% | 125548270.0 – 6576674985.0 (mean 2403254561.2603) |
| `osm_type` | object | 0.0% | way, node |
| `completeness` | int64 | 0.0% | 6.0 – 24.0 (mean 10.3836) |
| `amenity` | object | 0.0% | hospital, clinic, pharmacy |
| `changeset_id` | int64 | 0.0% | 18241309.0 – 75320524.0 (mean 48948539.5342) |
| `uuid` | object | 0.0% | 09a60dadc51d45c4afd7d1078fc98c09, ff347d8b87e9443b98c1c2735ca12c7a, 0722456c720b47e59eb12df481597a4d |
| `changeset_version` | int64 | 0.0% | 1.0 – 6.0 (mean 1.5068) |
| `changeset_timestamp` | datetime64[ns] | 0.0% | |
| `name` | object | 24.7% | Medical Research Council, Diabugu Batapa Health Center, Manding Drug Store |
| `changeset_user` | object | 0.0% | MorganJ14, cmsandquist, Caitlyn W |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -16.7964 | -14.0158 | -15.8398 | -16.0882 |
| `y` | 13.0931 | 13.7792 | 13.4176 | 13.4417 |
| `osm_id` | 125548270.0 | 6576674985.0 | 2403254561.2603 | 624734295.0 |
| `completeness` | 6.0 | 24.0 | 10.3836 | 10.0 |
| `changeset_id` | 18241309.0 | 75320524.0 | 48948539.5342 | 46475922.0 |
| `changeset_version` | 1.0 | 6.0 | 1.5068 | 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`. 22 column(s) with >80% missing values were removed: `is_in_health_zone`, `speciality`, `addr_full`, `operator`, `water_source`, `insurance`.... 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`, `name`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/gambia-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_gambia_healthsites,
title = {Gambia-healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/gambia-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.*