electricsheepafrica/africa-health-facilities-nigeria

---
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
- nga
pretty_name: "Nigeria Healthsites"
dataset_info:
splits:
- name: train
num_examples: 5743
- name: test
num_examples: 1435
---

# Nigeria Healthsites

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

*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)** | 7,179 |
| **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) |
| **Train split** | 5,743 rows |
| **Test split** | 1,435 rows |
| **Geographic scope** | NGA |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 2.7316–14.6365), `y` (range 4.3858–13.8652), `osm_type` (node, way), `loc_amenity` (hospital, pharmacy, doctors), `meta_operator_type` (public, private, government).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 35220563.0–13208413657.0), `loc_name` (PHC, Health Post, Primary Health Clinic), `changeset_id` (range 1362798.0–173131596.0), `meta_id` (affec0a58ccb437db48265510c768025, 74e5577b52b042ebab5d0858c4d7c440, 39e520d1162b4ecdb4ed3bdaa9736a02), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–65.625), `meta_healthcare` (hospital, clinic, doctor), `geo_bounds_url` (nigeriase4all.gov.ng, ehealthafrica.org, MSFsurvey), `changeset_version` (range 1.0–16.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 41.1% | 2.7316 – 14.6365 (mean 7.1276) |
| `y` | float64 | 41.1% | 4.3858 – 13.8652 (mean 9.0385) |
| `osm_id` | int64 | 0.0% | 35220563.0 – 13208413657.0 (mean 5383004472.178) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 65.625 (mean 13.973) |
| `loc_amenity` | object | 2.1% | hospital, pharmacy, doctors |
| `meta_healthcare` | object | 47.8% | hospital, clinic, doctor |
| `loc_name` | object | 26.0% | PHC, Health Post, Primary Health Clinic |
| `geo_bounds_url` | object | 49.9% | nigeriase4all.gov.ng, ehealthafrica.org, MSFsurvey |
| `meta_operator_type` | object | 76.6% | public, private, government |
| `changeset_id` | int64 | 0.0% | 1362798.0 – 173131596.0 (mean 103154509.2128) |
| `changeset_version` | int64 | 0.0% | 1.0 – 16.0 (mean 1.4499) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `meta_id` | object | 0.0% | affec0a58ccb437db48265510c768025, 74e5577b52b042ebab5d0858c4d7c440, 39e520d1162b4ecdb4ed3bdaa9736a02 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 2.7316 | 14.6365 | 7.1276 | 7.1675 |
| `y` | 4.3858 | 13.8652 | 9.0385 | 9.1811 |
| `osm_id` | 35220563.0 | 13208413657.0 | 5383004472.178 | 3680835285.0 |
| `completeness` | 6.25 | 65.625 | 13.973 | 12.5 |
| `changeset_id` | 1362798.0 | 173131596.0 | 103154509.2128 | 90945612.0 |
| `changeset_version` | 1.0 | 16.0 | 1.4499 | 1.0 |

---

## 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`. 21 column(s) with >80% missing values were removed: `meta_operator`, `meta_speciality`, `contact_phone`, `status_operational_status`, `access_hours`, `capacity_beds`.... 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`, `loc_name`, `geo_bounds_url`, `meta_operator_type`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/nigeria-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_nigeria,
title = {Nigeria Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-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:
- 公开采集
language:
- 英语
license:
- 其他
multilinguality:
- 单语言
size_categories:
- 1K<n<10K
source_datasets:
- 原创数据集
task_categories:
- 表格分类
task_ids: []
tags:
- 非洲
- 人道主义
- HDX（Humanitarian Data Exchange，人道主义数据交换平台）
- Electric Sheep Africa
- 医疗设施
- HXL（Humanitarian Exchange Language，人道主义交换语言）
- NGA（尼日利亚，Nigeria）
pretty_name: "尼日利亚医疗设施点"
dataset_info:
splits:
- name: train
num_examples: 5743
- name: test
num_examples: 1435

---

# 尼日利亚医疗设施点

**发布方：** 全球医疗设施映射项目（Global Healthsites Mapping Project） · **来源：** [HDX（Humanitarian Data Exchange，人道主义数据交换平台）](https://data.humdata.org/dataset/nigeria-healthsites) · **许可证：** `ODbL（Open Data Commons Open Database License，开放数据共同体开放数据库许可证）` · **最后更新时间：** 2025-10-15

---

## 数据集摘要

本数据集收录了尼日利亚境内运营中的医疗设施清单，包含属性包括：设施名称、设施性质、开展活动、纬度、经度。

数据集中每一行代表一条表格记录。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**NGA（尼日利亚，Nigeria）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 公共卫生 |
| **观测单元** | 表格记录 |
| **总行数** | 7,179 |
| **列数** | 16（6个数值型、9个分类型、0个日期时间型） |
| **训练集划分** | 5,743行 |
| **测试集划分** | 1,435行 |
| **地理覆盖范围** | NGA（尼日利亚，Nigeria） |
| **发布方** | 全球医疗设施映射项目 |
| **HDX最后更新时间** | 2025-10-15 |

---

## 变量说明

**地理相关变量** — `x`（取值范围2.7316–14.6365）、`y`（取值范围4.3858–13.8652）、`osm_type（OpenStreetMap类型）`（节点、路径）、`loc_amenity`（医院、药房、诊所）、`meta_operator_type`（公立、私立、政府运营）。

**时间相关变量** — `changeset_timestamp`（变更集时间戳）。

**标识符与元数据变量** — `osm_id`（取值范围35220563.0–13208413657.0）、`loc_name`（PHC（初级卫生保健，Primary Health Care）、卫生所、初级卫生保健诊所）、`changeset_id`（取值范围1362798.0–173131596.0）、`meta_id`（示例值：affec0a58ccb437db48265510c768025、74e5577b52b042ebab5d0858c4d7c440、39e520d1162b4ecdb4ed3bdaa9736a02）、`esa_source`（HDX）及其他1项。

**其他变量** — `completeness`（取值范围6.25–65.625）、`meta_healthcare`（医院、诊所、医生）、`geo_bounds_url`（示例值：nigeriase4all.gov.ng、ehealthafrica.org、MSFsurvey）、`changeset_version`（取值范围1.0–16.0）。

---

## 快速上手

python
from datasets import load_dataset

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

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `x` | float64 | 41.1% | 2.7316 – 14.6365（均值7.1276） |
| `y` | float64 | 41.1% | 4.3858 – 13.8652（均值9.0385） |
| `osm_id` | int64 | 0.0% | 35220563.0 – 13208413657.0（均值5383004472.178） |
| `osm_type` | object | 0.0% | node, way（节点、路径） |
| `completeness` | float64 | 0.0% | 6.25 – 65.625（均值13.973） |
| `loc_amenity` | object | 2.1% | hospital, pharmacy, doctors（医院、药房、诊所） |
| `meta_healthcare` | object | 47.8% | hospital, clinic, doctor（医院、诊所、医生） |
| `loc_name` | object | 26.0% | PHC, Health Post, Primary Health Clinic（PHC、卫生所、初级卫生保健诊所） |
| `geo_bounds_url` | object | 49.9% | nigeriase4all.gov.ng, ehealthafrica.org, MSFsurvey |
| `meta_operator_type` | object | 76.6% | public, private, government（公立、私立、政府运营） |
| `changeset_id` | int64 | 0.0% | 1362798.0 – 173131596.0（均值103154509.2128） |
| `changeset_version` | int64 | 0.0% | 1.0 – 16.0（均值1.4499） |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | 无 |
| `meta_id` | object | 0.0% | affec0a58ccb437db48265510c768025, 74e5577b52b042ebab5d0858c4d7c440, 39e520d1162b4ecdb4ed3bdaa9736a02 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `x` | 2.7316 | 14.6365 | 7.1276 | 7.1675 |
| `y` | 4.3858 | 13.8652 | 9.0385 | 9.1811 |
| `osm_id` | 35220563.0 | 13208413657.0 | 5383004472.178 | 3680835285.0 |
| `completeness` | 6.25 | 65.625 | 13.973 | 12.5 |
| `changeset_id` | 1362798.0 | 173131596.0 | 103154509.2128 | 90945612.0 |
| `changeset_version` | 1.0 | 16.0 | 1.4499 | 1.0 |

---

## 数据整理流程

原始数据通过CKAN（Comprehensive Knowledge Archive Network，综合知识存档网络）API从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并标准化为蛇形命名法（snake_case）。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。移除了21列缺失值占比超过80%的字段：`meta_operator`、`meta_speciality`、`contact_phone`、`status_operational_status`、`access_hours`、`capacity_beds`等。依据解析成功率（阈值为85%），将1列从字符串类型转换为数值型或日期时间型。采用固定随机种子（42）将数据集按80/20比例划分为训练集与测试集，并以Snappy压缩格式保存为Parquet文件。

---

## 数据集局限性

- 数据源自全球医疗设施映射项目，未经过Electric Sheep Africa（以下简称ESA）的独立验证。
- 自动化清洗流程无法修正原始数据集中的错误上报值、定义不一致问题或采集阶段的抽样偏差。
- 以下列的缺失值占比超过20%，在建模过程中需谨慎使用：`x`、`y`、`meta_healthcare`、`loc_name`、`geo_bounds_url`、`meta_operator_type`。
- 如需了解发布方的方法论说明与相关注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/nigeria-healthsites)。

---

## 引用格式

bibtex
@dataset{hdx_africa_health_facilities_nigeria,
title = {Nigeria Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-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。*