electricsheepafrica/africa-health-facilities-chad

---
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
- tcd
pretty_name: "Chad Healthsites"
dataset_info:
splits:
- name: train
num_examples: 351
- name: test
num_examples: 87
---

# Chad Healthsites

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

*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)** | 439 |
| **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) |
| **Train split** | 351 rows |
| **Test split** | 87 rows |
| **Geographic scope** | TCD |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 13.6115–22.4276), `y` (range 7.8181–21.3537), `osm_type` (node, way), `loc_amenity` (hospital, clinic, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 185761188.0–13230377601.0), `loc_name` (Centre de Santé, Centre de santé de Baltram, Centre Medical SOS المركز الطبي إس أو إس), `changeset_id` (range 13700524.0–173226047.0), `meta_id` (1e4c31d2f8c34d3e8a7070ed58fbec42, 90cc106548f04f6b808fb903e8bd625c, 6ca8904c16bb4877b1d6104670e4f73a), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–31.25), `meta_healthcare` (hospital, pharmacy, clinic), `changeset_version` (range 1.0–10.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 25.1% | 13.6115 – 22.4276 (mean 16.5927) |
| `y` | float64 | 25.1% | 7.8181 – 21.3537 (mean 12.0856) |
| `osm_id` | int64 | 0.0% | 185761188.0 – 13230377601.0 (mean 5652766068.5786) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 31.25 (mean 11.6387) |
| `loc_amenity` | object | 0.7% | hospital, clinic, pharmacy |
| `meta_healthcare` | object | 63.6% | hospital, pharmacy, clinic |
| `loc_name` | object | 20.0% | Centre de Santé, Centre de santé de Baltram, Centre Medical SOS المركز الطبي إس أو إس |
| `changeset_id` | int64 | 0.0% | 13700524.0 – 173226047.0 (mean 97122658.9339) |
| `changeset_version` | int64 | 0.0% | 1.0 – 10.0 (mean 1.7084) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 1e4c31d2f8c34d3e8a7070ed58fbec42, 90cc106548f04f6b808fb903e8bd625c, 6ca8904c16bb4877b1d6104670e4f73a |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 13.6115 | 22.4276 | 16.5927 | 15.3645 |
| `y` | 7.8181 | 21.3537 | 12.0856 | 12.1274 |
| `osm_id` | 185761188.0 | 13230377601.0 | 5652766068.5786 | 4543039509.0 |
| `completeness` | 6.25 | 31.25 | 11.6387 | 9.375 |
| `changeset_id` | 13700524.0 | 173226047.0 | 97122658.9339 | 88480599.0 |
| `changeset_version` | 1.0 | 10.0 | 1.7084 | 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: `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_healthcare`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/chad-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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