electricsheepafrica/africa-health-facilities-cote-divoire

---
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
- hxl
- civ
pretty_name: "Ivory Coast Healthsites"
dataset_info:
splits:
- name: train
num_examples: 1962
- name: test
num_examples: 490
---

# Ivory Coast Healthsites

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

*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)** | 2,453 |
| **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) |
| **Train split** | 1,962 rows |
| **Test split** | 490 rows |
| **Geographic scope** | CIV |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -8.4174–-2.7927), `y` (range 4.4813–10.4403), `osm_type` (node, way), `amenity` (pharmacy, doctors, hospital).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 91273250.0–13208372320.0), `name` (Centre de Santé, Clinique Chinoise, Infirmerie), `changeset_id` (range 38766946.0–173129934.0), `uuid` (9826c52b8fe24c14961de7fc97f952d2, 338310d6c11b4f23a805285632508da1, 00561e6f04594f989136d2c82a839977), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–43.75), `healthcare` (pharmacy, doctor, hospital), `changeset_version` (range 1.0–17.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 11.9% | -8.4174 – -2.7927 (mean -4.7185) |
| `y` | float64 | 11.9% | 4.4813 – 10.4403 (mean 6.0531) |
| `osm_id` | int64 | 0.0% | 91273250.0 – 13208372320.0 (mean 5999124547.2776) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 43.75 (mean 14.7192) |
| `amenity` | object | 1.5% | pharmacy, doctors, hospital |
| `healthcare` | object | 0.9% | pharmacy, doctor, hospital |
| `name` | object | 5.5% | Centre de Santé, Clinique Chinoise, Infirmerie |
| `changeset_id` | int64 | 0.0% | 38766946.0 – 173129934.0 (mean 149602352.2646) |
| `changeset_version` | int64 | 0.0% | 1.0 – 17.0 (mean 2.6669) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | 9826c52b8fe24c14961de7fc97f952d2, 338310d6c11b4f23a805285632508da1, 00561e6f04594f989136d2c82a839977 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -8.4174 | -2.7927 | -4.7185 | -4.074 |
| `y` | 4.4813 | 10.4403 | 6.0531 | 5.391 |
| `osm_id` | 91273250.0 | 13208372320.0 | 5999124547.2776 | 5923687543.0 |
| `completeness` | 6.25 | 43.75 | 14.7192 | 12.5 |
| `changeset_id` | 38766946.0 | 173129934.0 | 149602352.2646 | 156431851.0 |
| `changeset_version` | 1.0 | 17.0 | 2.6669 | 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`. 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ivory-coast-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_cote_divoire,
title = {Ivory Coast Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/ivory-coast-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.*