---
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
- ken
pretty_name: "Kenya Healthsites"
dataset_info:
  splits:
    - name: train
      num_examples: 2046
    - name: test
      num_examples: 511
---

# Kenya Healthsites

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

*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)** | 2,558 |
| **Columns** | 18 (6 numeric, 11 categorical, 0 datetime) |
| **Train split** | 2,046 rows |
| **Test split** | 511 rows |
| **Geographic scope** | KEN |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 33.9758–41.5247), `y` (range -4.4–3.953), `osm_type` (node, way), `amenity` (hospital, pharmacy, clinic), `operator_type` (private, public, government).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 25473029.0–13202466836.0), `name` (Chemist, Faith healer, chemist), `changeset_id` (range 3089987.0–173109831.0), `uuid` (fa79ca7f10794c288995b0ff692e9a6d, ea6fc01726de45b6ad6c3cb054fb02c8, 05f1aac2329f4b57b9b07bd473900252), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–59.375), `healthcare` (hospital, counselling, clinic), `operator` (Ministry of Health, private, public), `operational_status` (operational, non_operational, open), `opening_hours` (24/7, Mo-Fr 08:00-17:00, sunrise-sunset) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 27.1% | 33.9758 – 41.5247 (mean 36.9558) |
| `y` | float64 | 27.1% | -4.4 – 3.953 (mean -0.8696) |
| `osm_id` | int64 | 0.0% | 25473029.0 – 13202466836.0 (mean 5067761548.5801) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 59.375 (mean 17.025) |
| `amenity` | object | 1.1% | hospital, pharmacy, clinic |
| `healthcare` | object | 47.4% | hospital, counselling, clinic |
| `name` | object | 4.5% | Chemist, Faith healer, chemist |
| `operator` | object | 78.5% | Ministry of Health, private, public |
| `operator_type` | object | 56.8% | private, public, government |
| `operational_status` | object | 75.1% | operational, non_operational, open |
| `opening_hours` | object | 77.1% | 24/7, Mo-Fr 08:00-17:00, sunrise-sunset |
| `changeset_id` | int64 | 0.0% | 3089987.0 – 173109831.0 (mean 115881603.6591) |
| `changeset_version` | int64 | 0.0% | 1.0 – 16.0 (mean 2.2455) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% |  |
| `uuid` | object | 0.0% | fa79ca7f10794c288995b0ff692e9a6d, ea6fc01726de45b6ad6c3cb054fb02c8, 05f1aac2329f4b57b9b07bd473900252 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 33.9758 | 41.5247 | 36.9558 | 36.8451 |
| `y` | -4.4 | 3.953 | -0.8696 | -1.265 |
| `osm_id` | 25473029.0 | 13202466836.0 | 5067761548.5801 | 4546095039.5 |
| `completeness` | 6.25 | 59.375 | 17.025 | 15.625 |
| `changeset_id` | 3089987.0 | 173109831.0 | 115881603.6591 | 134436534.0 |
| `changeset_version` | 1.0 | 16.0 | 2.2455 | 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`. 19 column(s) with >80% missing values were removed: `source`, `speciality`, `beds`, `staff_doctors`, `staff_nurses`, `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`, `healthcare`, `operator`, `operator_type`, `operational_status`, `opening_hours`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/kenya-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

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