electricsheepafrica/africa-health-facilities-kenya

---
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.*

annotations_creators:
- 无注释
language_creators:
- 现有数据源采集
language:
- 英语（en）
license:
- 其他
multilinguality:
- 单语言（monolingual）
size_categories:
- 1000<样本数<10000
source_datasets:
- 原创数据集
task_categories:
- 表格分类（tabular-classification）
task_ids: []
tags:
- 非洲（africa）
- 人道主义（humanitarian）
- HDX（Humanitarian Data Exchange，人道主义数据交换平台）
- Electric Sheep Africa
- 卫生设施（health-facilities）
- HXL（Humanitarian eXchange Language）
- 肯尼亚（ken）
pretty_name: "肯尼亚卫生设施数据集"

dataset_info:
splits:
- name: train
num_examples: 2046
- name: test
num_examples: 511

# 肯尼亚卫生设施数据集

**发布方**：全球卫生设施测绘项目（Global Healthsites Mapping Project） · **数据源**：[HDX（Humanitarian Data Exchange，人道主义数据交换平台）](https://data.humdata.org/dataset/kenya-healthsites) · **许可协议**：`ODbL` · **更新时间**：2025-10-15

---

## 摘要
本数据集收录了肯尼亚境内运营中的卫生设施列表，包含以下属性：设施名称、设施性质、服务活动、纬度（Lat）、经度（Long）。
数据集中每一行均为一条表格化记录。该数据最后一次在HDX平台更新的时间为2025年10月15日，地理覆盖范围为**肯尼亚（KEN）**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。*

---

## 数据集特征
| | |
|---|---|
| **领域** | 公共卫生 |
| **观测单元** | 表格化记录 |
| **总样本行数** | 2,558 |
| **总列数** | 18（6列数值型、11列分类型、0列日期时间型） |
| **训练集拆分** | 2,046条记录 |
| **测试集拆分** | 511条记录 |
| **地理覆盖范围** | 肯尼亚（KEN） |
| **发布方** | 全球卫生设施测绘项目 |
| **HDX平台最后更新时间** | 2025-10-15 |

---

## 变量说明
**地理类变量**：`x`（取值范围33.9758–41.5247）、`y`（取值范围-4.4–3.953）、`osm_type`（节点、道路，node/way）、`amenity`（医院、药房、诊所）、`operator_type`（私立、公立、政府运营，private/public/government）。

**时间类变量**：`changeset_timestamp`（变更集时间戳）。

**标识符与元数据类变量**：`osm_id`（取值范围25473029.0–13202466836.0）、`name`（示例值：药剂师、信仰治疗师、药剂师）、`changeset_id`（取值范围3089987.0–173109831.0）、`uuid`（示例值：fa79ca7f10794c288995b0ff692e9a6d、ea6fc01726de45b6ad6c3cb054fb02c8、05f1aac2329f4b57b9b07bd473900252）、`esa_source`（来源为HDX）及其他1个字段。

**其他类变量**：`completeness`（取值范围6.25–59.375）、`healthcare`（医院、咨询服务、诊所）、`operator`（卫生部、私立机构、公立机构）、`operational_status`（运营中、非运营、开放）、`opening_hours`（24小时营业、周一至周五08:00-17:00、日出至日落）及其他1个字段。

---

## 快速上手
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()

---

## 数据结构
| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `x` | float64 | 27.1% | 33.9758 – 41.5247（均值36.9558） |
| `y` | float64 | 27.1% | -4.4 – 3.953（均值-0.8696） |
| `osm_id` | int64 | 0.0% | 25473029.0 – 13202466836.0（均值5067761548.5801） |
| `osm_type` | object | 0.0% | node（节点）、way（道路） |
| `completeness` | float64 | 0.0% | 6.25 – 59.375（均值17.025） |
| `amenity` | object | 1.1% | hospital（医院）、pharmacy（药房）、clinic（诊所） |
| `healthcare` | object | 47.4% | hospital（医院）、counselling（咨询服务）、clinic（诊所） |
| `name` | object | 4.5% | Chemist（药剂师）、Faith healer（信仰治疗师）、chemist（药剂师） |
| `operator` | object | 78.5% | Ministry of Health（卫生部）、private（私立）、public（公立） |
| `operator_type` | object | 56.8% | private（私立）、public（公立）、government（政府运营） |
| `operational_status` | object | 75.1% | operational（运营中）、non_operational（非运营）、open（开放） |
| `opening_hours` | object | 77.1% | 24/7（24小时营业）、Mo-Fr 08:00-17:00（周一至周五8:00-17:00）、sunrise-sunset（日出至日落） |
| `changeset_id` | int64 | 0.0% | 3089987.0 – 173109831.0（均值115881603.6591） |
| `changeset_version` | int64 | 0.0% | 1.0 – 16.0（均值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% | |

---

## 数值型变量统计
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `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 |

---

## 数据整理流程
原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。所有列名均转换为小写，并统一采用蛇形命名法（snake_case）进行标准化。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。移除了19列缺失率超过80%的字段：`source`、`speciality`、`beds`、`staff_doctors`、`staff_nurses`、`health_amenity_type`等。根据解析成功率（阈值>85%），将1列从字符串类型转换为数值型或日期时间型。本数据集以80/20的比例划分为训练集与测试集，采用固定随机种子（42）进行拆分，并保存为Snappy压缩的Parquet格式。

---

## 数据集局限性
- 数据源自全球卫生设施测绘项目，未由Electric Sheep Africa（ESA）进行独立验证。
- 自动化清洗流程无法修正原始数据集中的错报值、定义不一致或采样偏差问题。
- 以下列的缺失率超过20%，在建模过程中需谨慎使用：`x`、`y`、`healthcare`、`operator`、`operator_type`、`operational_status`、`opening_hours`。
- 如需查看发布方的官方方法说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/kenya-healthsites)。

---

## 引用格式
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) — 非洲机器学习数据集基础设施，尼日利亚拉各斯。*