electricsheepafrica/africa-marocco-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
- mar
pretty_name: "Marocco-healthsites"
dataset_info:
splits:
- name: train
num_examples: 310
- name: test
num_examples: 77
---

# Marocco-healthsites

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

*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)** | 388 |
| **Columns** | 14 (3 numeric, 11 categorical, 0 datetime) |
| **Train split** | 310 rows |
| **Test split** | 77 rows |
| **Geographic scope** | MAR |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-04-25 |

---

## Variables

**Geographic** — `x` (range -10.1779–-1.0039), `y` (range 27.6749–35.8875), `type` (hospital, clinic).

**Temporal** — `date_modified` (2015/12/11 10:51:22.924+00, 2015/11/17 09:43:57.838+00, 2016/02/23 01:20:44+00).

**Identifier / Metadata** — `source_url` (http://www.openstreetmap.org/way/248718105, http://www.openstreetmap.org/way/475665966, http://www.openstreetmap.org/way/421536627), `name` (Polyclinique, Hôpital, Polyclinique CNSS), `uuid` (f43812b1905a4a1183180f3e1eca8af3, cafcfc2cc020419199e598c8bd344cb5, 2a4d1ae01a3148de87f72800c47c2853), `source` (OpenStreetMap), `esa_source` (HDX) and 1 others.

**Other** — `what3words` (leads.pixies.lamppost, users.eyelash.faster, worth.freshen.reaction), `upstream` (openstreetmap¶w248718105, OpenStreetMap¶w475665966, OpenStreetMap¶w421536627), `completeness` (35.29%, 29.41%, 41.18%), `version` (range 2.0–2.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-marocco-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% | -10.1779 – -1.0039 (mean -5.3959) |
| `y` | float64 | 0.0% | 27.6749 – 35.8875 (mean 33.5947) |
| `source_url` | object | 0.0% | http://www.openstreetmap.org/way/248718105, http://www.openstreetmap.org/way/475665966, http://www.openstreetmap.org/way/421536627 |
| `what3words` | object | 0.0% | leads.pixies.lamppost, users.eyelash.faster, worth.freshen.reaction |
| `upstream` | object | 0.0% | openstreetmap¶w248718105, OpenStreetMap¶w475665966, OpenStreetMap¶w421536627 |
| `name` | object | 0.0% | Polyclinique, Hôpital, Polyclinique CNSS |
| `completeness` | object | 0.0% | 35.29%, 29.41%, 41.18% |
| `uuid` | object | 0.0% | f43812b1905a4a1183180f3e1eca8af3, cafcfc2cc020419199e598c8bd344cb5, 2a4d1ae01a3148de87f72800c47c2853 |
| `date_modified` | object | 0.0% | 2015/12/11 10:51:22.924+00, 2015/11/17 09:43:57.838+00, 2016/02/23 01:20:44+00 |
| `source` | object | 0.0% | OpenStreetMap |
| `version` | int64 | 0.0% | 2.0 – 2.0 (mean 2.0) |
| `type` | object | 0.0% | hospital, clinic |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -10.1779 | -1.0039 | -5.3959 | -5.9989 |
| `y` | 27.6749 | 35.8875 | 33.5947 | 33.9869 |
| `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`. 4 column(s) with >80% missing values were removed: `physical_address`, `url`, `phone`, `email`. 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/marocco-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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