electricsheepafrica/africa-health-facilities-morocco

---
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
- mar
pretty_name: "Morocco Healthsites"
dataset_info:
splits:
- name: train
num_examples: 6644
- name: test
num_examples: 1661
---

# Morocco Healthsites

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

*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)** | 8,306 |
| **Columns** | 19 (7 numeric, 11 categorical, 0 datetime) |
| **Train split** | 6,644 rows |
| **Test split** | 1,661 rows |
| **Geographic scope** | MAR |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -16.7518–-1.231), `y` (range 22.0535–35.9094), `osm_type` (node, way), `amenity` (pharmacy, doctors, hospital), `addr_city` (Marrakech, Fès, Oujda).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 31573143.0–13206702257.0), `name` (Pharmacie Centrale الصيدلية المركزية, Pharmacie Ibn Sina صيدلية ابن سينا, Pharmacie Al Qods صيدلية القدس), `addr_postcode` (range 8630.0–93200.0), `changeset_id` (range 3740052.0–173116224.0), `uuid` (ecca498881c64d4684d13ae7e1ecd27c, 6cb16612caba4044b414c673b0d6aa6c, 0a006c6dde89410397b41839495a18db) and 2 others.

**Other** — `completeness` (range 6.25–37.5), `healthcare` (pharmacy, hospital, doctor), `operator` (Ph PAM, Ph PM, Ph PS), `dispensing` (yes, no, Pharmacie La Province), `addr_street` (Avenue Mohamed V, Avenue Hassan II, Avenue Mohamed es Saoui) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 7.3% | -16.7518 – -1.231 (mean -6.6133) |
| `y` | float64 | 7.3% | 22.0535 – 35.9094 (mean 33.233) |
| `osm_id` | int64 | 0.0% | 31573143.0 – 13206702257.0 (mean 6900429713.6275) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 37.5 (mean 21.2313) |
| `amenity` | object | 1.0% | pharmacy, doctors, hospital |
| `healthcare` | object | 12.5% | pharmacy, hospital, doctor |
| `name` | object | 5.1% | Pharmacie Centrale الصيدلية المركزية, Pharmacie Ibn Sina صيدلية ابن سينا, Pharmacie Al Qods صيدلية القدس |
| `operator` | object | 79.3% | Ph PAM, Ph PM, Ph PS |
| `dispensing` | object | 29.3% | yes, no, Pharmacie La Province |
| `addr_street` | object | 62.9% | Avenue Mohamed V, Avenue Hassan II, Avenue Mohamed es Saoui |
| `addr_postcode` | float64 | 30.6% | 8630.0 – 93200.0 (mean 45508.7205) |
| `addr_city` | object | 24.4% | Marrakech, Fès, Oujda |
| `changeset_id` | int64 | 0.0% | 3740052.0 – 173116224.0 (mean 135699321.9115) |
| `changeset_version` | int64 | 0.0% | 1.0 – 18.0 (mean 2.414) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | ecca498881c64d4684d13ae7e1ecd27c, 6cb16612caba4044b414c673b0d6aa6c, 0a006c6dde89410397b41839495a18db |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -16.7518 | -1.231 | -6.6133 | -6.741 |
| `y` | 22.0535 | 35.9094 | 33.233 | 33.7647 |
| `osm_id` | 31573143.0 | 13206702257.0 | 6900429713.6275 | 8422798165.0 |
| `completeness` | 6.25 | 37.5 | 21.2313 | 21.875 |
| `addr_postcode` | 8630.0 | 93200.0 | 45508.7205 | 40000.0 |
| `changeset_id` | 3740052.0 | 173116224.0 | 135699321.9115 | 167934833.0 |
| `changeset_version` | 1.0 | 18.0 | 2.414 | 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`. 18 column(s) with >80% missing values were removed: `source`, `speciality`, `operator_type`, `operational_status`, `opening_hours`, `beds`.... 2 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: `operator`, `dispensing`, `addr_street`, `addr_postcode`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/morocco-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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