electricsheepafrica/africa-cape-verde-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
- cpv
pretty_name: "Cape Verde-healthsites"
dataset_info:
splits:
- name: train
num_examples: 13
- name: test
num_examples: 3
---

# Cape Verde-healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/cape-verde-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 time-series observations. Data was last updated on HDX on 2025-04-25. Geographic scope: **CPV**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 17 |
| **Columns** | 15 (3 numeric, 12 categorical, 0 datetime) |
| **Train split** | 13 rows |
| **Test split** | 3 rows |
| **Geographic scope** | CPV |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-04-25 |

---

## Variables

**Geographic** — `x` (range -25.3109–-22.9342), `y` (range 14.917–17.1798), `physical_address` (CL, Murdeira, Rua Pedro Azancot, Praia, Rua Borjona de Freitas, Praia), `type` (hospital, clinic).

**Temporal** — `date_modified` (2015/11/17 09:43:57.838+00, 2014/01/07 15:50:56+00, 2013/09/01 18:05:00+00).

**Identifier / Metadata** — `source_url` (http://www.openstreetmap.org/way/170595424, http://www.openstreetmap.org/way/236852997, http://www.openstreetmap.org/node/4596256400), `name` (Centro Médico, Hospital Ramiro Figueira, Unidad Sanitaria de Base), `uuid` (d2face85e05f4eae9a5ad8cbad5f5e26, 809cfd2725cc46dc8e261c50ee46e363, 6e9f1219949941eeb6c25d6aa96cbaac), `source` (OpenStreetMap), `esa_source` and 1 others.

**Other** — `what3words` (headwear.complicit.supplied, steeps.unsafe.coyote, dour.utterance.raggedly), `upstream` (OpenStreetMap¶w170595424, OpenStreetMap¶w236852997, OpenStreetMap¶n4596256400), `completeness` (35.29%, 29.41%), `version` (range 2.0–2.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-cape-verde-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 | 0.0% | -25.3109 – -22.9342 (mean -24.2231) |
| `y` | float64 | 0.0% | 14.917 – 17.1798 (mean 16.1917) |
| `source_url` | object | 0.0% | http://www.openstreetmap.org/way/170595424, http://www.openstreetmap.org/way/236852997, http://www.openstreetmap.org/node/4596256400 |
| `what3words` | object | 0.0% | headwear.complicit.supplied, steeps.unsafe.coyote, dour.utterance.raggedly |
| `upstream` | object | 0.0% | OpenStreetMap¶w170595424, OpenStreetMap¶w236852997, OpenStreetMap¶n4596256400 |
| `name` | object | 0.0% | Centro Médico, Hospital Ramiro Figueira, Unidad Sanitaria de Base |
| `completeness` | object | 0.0% | 35.29%, 29.41% |
| `uuid` | object | 0.0% | d2face85e05f4eae9a5ad8cbad5f5e26, 809cfd2725cc46dc8e261c50ee46e363, 6e9f1219949941eeb6c25d6aa96cbaac |
| `date_modified` | object | 0.0% | 2015/11/17 09:43:57.838+00, 2014/01/07 15:50:56+00, 2013/09/01 18:05:00+00 |
| `source` | object | 0.0% | OpenStreetMap |
| `version` | int64 | 0.0% | 2.0 – 2.0 (mean 2.0) |
| `physical_address` | object | 70.6% | CL, Murdeira, Rua Pedro Azancot, Praia, Rua Borjona de Freitas, Praia |
| `type` | object | 0.0% | hospital, clinic |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -25.3109 | -22.9342 | -24.2231 | -23.7535 |
| `y` | 14.917 | 17.1798 | 16.1917 | 16.7488 |
| `version` | 2.0 | 2.0 | 2.0 | 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`. 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: `physical_address`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/cape-verde-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_cape_verde_healthsites,
title = {Cape Verde-healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/cape-verde-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.*