electricsheepafrica/africa-health-facilities-liberia

---
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
- lbr
pretty_name: "Liberia Healthsites"
dataset_info:
splits:
- name: train
num_examples: 940
- name: test
num_examples: 235
---

# Liberia Healthsites

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

*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)** | 1,175 |
| **Columns** | 19 (6 numeric, 12 categorical, 0 datetime) |
| **Train split** | 940 rows |
| **Test split** | 235 rows |
| **Geographic scope** | LBR |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range -11.4132–-7.5084), `y` (range 4.3637–8.496), `osm_type` (node, way), `loc_amenity` (clinic, pharmacy, hospital), `addr_city` (Monrovia, Gbarnga, Ganta).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 94957267.0–12875885201.0), `loc_name` (Lucky Pharmacy, Red Hill Medical Clinic, Tuzon Clinic), `changeset_id` (range 27026152.0–172132881.0), `meta_id` (bce41dd53df44a1faea4c78ab589fd2d, f2a8ba98e8364b97987987c6d53cf2d7, 6fbd17ee27a046449be9738e013aceea), `esa_source` and 1 others.

**Other** — `completeness` (range 6.25–46.875), `meta_healthcare` (clinic, pharmacy, hospital), `meta_operator` (Government, private_profit, government), `geo_bounds_url` (Ministry of Health and National Public Health Institute of Liberia, MOH & NPHIL, Red Cross Field Survey), `status_operational_status` (functional, Functional, non-functional) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 27.3% | -11.4132 – -7.5084 (mean -9.776) |
| `y` | float64 | 27.3% | 4.3637 – 8.496 (mean 6.6547) |
| `osm_id` | int64 | 0.0% | 94957267.0 – 12875885201.0 (mean 5038457318.5387) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 46.875 (mean 21.0931) |
| `loc_amenity` | object | 0.7% | clinic, pharmacy, hospital |
| `meta_healthcare` | object | 24.0% | clinic, pharmacy, hospital |
| `loc_name` | object | 5.6% | Lucky Pharmacy, Red Hill Medical Clinic, Tuzon Clinic |
| `meta_operator` | object | 68.3% | Government, private_profit, government |
| `geo_bounds_url` | object | 21.7% | Ministry of Health and National Public Health Institute of Liberia, MOH & NPHIL, Red Cross Field Survey |
| `status_operational_status` | object | 58.4% | functional, Functional, non-functional |
| `access_hours` | object | 76.6% | Mo-Sa 08:00-18:00, 24/7, Mo-Fr 08:00-17:00 |
| `addr_city` | object | 56.5% | Monrovia, Gbarnga, Ganta |
| `changeset_id` | int64 | 0.0% | 27026152.0 – 172132881.0 (mean 124405093.5838) |
| `changeset_version` | int64 | 0.0% | 1.0 – 10.0 (mean 3.4426) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | bce41dd53df44a1faea4c78ab589fd2d, f2a8ba98e8364b97987987c6d53cf2d7, 6fbd17ee27a046449be9738e013aceea |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | -11.4132 | -7.5084 | -9.776 | -9.7191 |
| `y` | 4.3637 | 8.496 | 6.6547 | 6.5939 |
| `osm_id` | 94957267.0 | 12875885201.0 | 5038457318.5387 | 5102115273.0 |
| `completeness` | 6.25 | 46.875 | 21.0931 | 18.75 |
| `changeset_id` | 27026152.0 | 172132881.0 | 124405093.5838 | 125657972.0 |
| `changeset_version` | 1.0 | 10.0 | 3.4426 | 3.0 |

---

## 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: `meta_speciality`, `meta_operator_type`, `contact_phone`, `capacity_beds`, `capacity_staff`, `meta_health_amenity_type`.... 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`, `meta_operator`, `geo_bounds_url`, `status_operational_status`, `access_hours`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/liberia-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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