electricsheepafrica/africa-health-facilities-mozambique

---
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
- moz
pretty_name: "Mozambique Healthsites"
dataset_info:
splits:
- name: train
num_examples: 890
- name: test
num_examples: 222
---

# Mozambique Healthsites

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

*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)** | 1,113 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 890 rows |
| **Test split** | 222 rows |
| **Geographic scope** | MOZ |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 31.9895–40.7359), `y` (range -26.8423–-10.5929), `osm_type` (node, way), `loc_amenity` (clinic, hospital, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 60659889.0–13013675696.0), `loc_name` (International SOS, Centro de Saúde, Farmácia), `changeset_id` (range 5725389.0–171241834.0), `meta_id` (896db3c9fc6c44f48f607059e5142f59, d9f15b98d15849d3b8b8fbbc249031ad, b66a4f2d749c4bdda6146239a4e871f9), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–28.125), `meta_healthcare` (clinic, hospital, pharmacy), `geo_bounds_url` (MSFsurvey, #ProjectoDeReduçãoDeRiscoDeDesastresUrbano #UrbanDisasterRiskReductionProject #CruzVermelhadeMoçambique  #CruzVermelhaAlemã #MozambicanRedCross #GermanRedCross, ASF-#mmb AMB, Mobility and Gender Mapping Project 2020, Rota 101), `changeset_version` (range 1.0–13.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 20.7% | 31.9895 – 40.7359 (mean 36.0457) |
| `y` | float64 | 20.7% | -26.8423 – -10.5929 (mean -18.1931) |
| `osm_id` | int64 | 0.0% | 60659889.0 – 13013675696.0 (mean 5287836870.867) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 28.125 (mean 12.7499) |
| `loc_amenity` | object | 1.0% | clinic, hospital, pharmacy |
| `meta_healthcare` | object | 73.6% | clinic, hospital, pharmacy |
| `loc_name` | object | 8.4% | International SOS, Centro de Saúde, Farmácia |
| `geo_bounds_url` | object | 33.1% | MSFsurvey, #ProjectoDeReduçãoDeRiscoDeDesastresUrbano #UrbanDisasterRiskReductionProject #CruzVermelhadeMoçambique  #CruzVermelhaAlemã #MozambicanRedCross #GermanRedCross, ASF-#mmb AMB, Mobility and Gender Mapping Project 2020, Rota 101 |
| `changeset_id` | int64 | 0.0% | 5725389.0 – 171241834.0 (mean 126550742.0395) |
| `changeset_version` | int64 | 0.0% | 1.0 – 13.0 (mean 2.0881) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | 896db3c9fc6c44f48f607059e5142f59, d9f15b98d15849d3b8b8fbbc249031ad, b66a4f2d749c4bdda6146239a4e871f9 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-21 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 31.9895 | 40.7359 | 36.0457 | 35.726 |
| `y` | -26.8423 | -10.5929 | -18.1931 | -17.1171 |
| `osm_id` | 60659889.0 | 13013675696.0 | 5287836870.867 | 6476197767.0 |
| `completeness` | 6.25 | 28.125 | 12.7499 | 12.5 |
| `changeset_id` | 5725389.0 | 171241834.0 | 126550742.0395 | 152119362.0 |
| `changeset_version` | 1.0 | 13.0 | 2.0881 | 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`. 22 column(s) with >80% missing values were removed: `meta_operator`, `meta_speciality`, `meta_operator_type`, `contact_phone`, `status_operational_status`, `access_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.
- The following columns have >20% missing values and should be treated with caution in modelling: `x`, `y`, `meta_healthcare`, `geo_bounds_url`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/mozambique-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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