electricsheepafrica/africa-health-facilities-ghana
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--- 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 - gha pretty_name: "Ghana Healthsites" dataset_info: splits: - name: train num_examples: 1734 - name: test num_examples: 433 --- # Ghana Healthsites **Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/ghana-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: **GHA**. *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)** | 2,168 | | **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) | | **Train split** | 1,734 rows | | **Test split** | 433 rows | | **Geographic scope** | GHA | | **Publisher** | Global Healthsites Mapping Project | | **HDX last updated** | 2025-10-15 | --- ## Variables **Geographic** — `x` (range -2.9733–1.1949), `y` (range 4.8838–10.9796), `osm_type` (node, way), `amenity` (pharmacy, hospital, clinic). **Temporal** — `changeset_timestamp`. **Identifier / Metadata** — `osm_id` (range 154173029.0–13208372240.0), `name` (Ernest Chemist, Drug Store, Clinic), `changeset_id` (range 7911073.0–173278687.0), `uuid` (4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75), `esa_source` (HDX) and 1 others. **Other** — `completeness` (range 6.25–46.875), `healthcare` (pharmacy, hospital, clinic), `changeset_version` (range 1.0–11.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-health-facilities-ghana") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `x` | float64 | 24.2% | -2.9733 – 1.1949 (mean -0.721) | | `y` | float64 | 24.2% | 4.8838 – 10.9796 (mean 6.2805) | | `osm_id` | int64 | 0.0% | 154173029.0 – 13208372240.0 (mean 5928268646.9723) | | `osm_type` | object | 0.0% | node, way | | `completeness` | float64 | 0.0% | 6.25 – 46.875 (mean 12.8142) | | `amenity` | object | 2.5% | pharmacy, hospital, clinic | | `healthcare` | object | 47.6% | pharmacy, hospital, clinic | | `name` | object | 15.3% | Ernest Chemist, Drug Store, Clinic | | `changeset_id` | int64 | 0.0% | 7911073.0 – 173278687.0 (mean 110439476.5807) | | `changeset_version` | int64 | 0.0% | 1.0 – 11.0 (mean 1.7911) | | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | | | `uuid` | object | 0.0% | 4697c2eae21643758a915ac72eced9e9, 659326089e434fde945d521520622643, 9c69dfb7516b4d4293399c199043db75 | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-20 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `x` | -2.9733 | 1.1949 | -0.721 | -0.2449 | | `y` | 4.8838 | 10.9796 | 6.2805 | 5.6588 | | `osm_id` | 154173029.0 | 13208372240.0 | 5928268646.9723 | 6542893586.5 | | `completeness` | 6.25 | 46.875 | 12.8142 | 12.5 | | `changeset_id` | 7911073.0 | 173278687.0 | 110439476.5807 | 119505116.0 | | `changeset_version` | 1.0 | 11.0 | 1.7911 | 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`. 23 column(s) with >80% missing values were removed: `operator`, `source`, `speciality`, `operator_type`, `operational_status`, `opening_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`, `healthcare`. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ghana-healthsites) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_health_facilities_ghana, title = {Ghana Healthsites}, author = {Global Healthsites Mapping Project}, year = {2025}, url = {https://data.humdata.org/dataset/ghana-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.*
yaml 注释创建者: - 无注释 语言创建者: - 现有资源采集 语言: - 英语 许可协议: - 其他许可 多语言属性: - 单语种 数据规模分类: - 1000<n<10000条 源数据集: - 原始数据集 任务类别: - 表格分类 任务子类别: - 无 标签: - 非洲 - 人道主义 - HDX（人道主义数据交换平台） - 电羊非洲（Electric Sheep Africa） - 医疗设施 - HXL（人道主义交换语言） - GHA（加纳国家代码） 数据集展示名: "加纳医疗站点" 数据集信息: 数据划分: - 名称: 训练集 样本数量: 1734 - 名称: 测试集 样本数量: 433 # 加纳医疗站点 **发布方**: 全球医疗站点测绘项目 · **来源**: [HDX（人道主义数据交换平台）](https://data.humdata.org/dataset/ghana-healthsites) · **许可协议**: `ODbL` · **更新时间**: 2025-10-15 --- ## 摘要 本数据集收录了所有运营中的医疗设施列表，包含的属性包括：设施名称、设施性质、开展业务、纬度、经度。 数据集中每一行均代表一条标准化表格记录。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：**GHA（加纳）**。 *本数据集已由[电羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)整理为适配机器学习使用的Parquet（列式数据存储格式）格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 公共卫生 | | **观测单元** | 表格记录 | | **总数据行数** | 2168条 | | **列数** | 14列（6个数值型、7个分类型、0个时间型） | | **训练集划分** | 1734条数据 | | **测试集划分** | 433条数据 | | **地理覆盖范围** | GHA（加纳） | | **发布方** | 全球医疗站点测绘项目 | | **HDX平台最后更新时间** | 2025-10-15 | --- ## 变量说明 ### 地理变量 `x`（取值范围：-2.9733–1.1949）、`y`（取值范围：4.8838–10.9796）、`osm_type`（可选值：node、way）、`amenity`（可选值：药房、医院、诊所）。 ### 时间变量 `changeset_timestamp`。 ### 标识符与元数据变量 `osm_id`（取值范围：154173029.0–13208372240.0）、`name`（示例值：Ernest Chemist、药房、诊所）、`changeset_id`（取值范围：7911073.0–173278687.0）、`uuid`（示例值：4697c2eae21643758a915ac72eced9e9、659326089e434fde945d521520622643、9c69dfb7516b4d4293399c199043db75）、`esa_source`（取值为HDX）及其他1个字段。 ### 其他变量 `completeness`（取值范围：6.25–46.875）、`healthcare`（可选值：药房、医院、诊所）、`changeset_version`（取值范围：1.0–11.0）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-health-facilities-ghana") 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 | 24.2% | -2.9733 – 1.1949（均值：-0.721） | | `y` | float64 | 24.2% | 4.8838 – 10.9796（均值：6.2805） | | `osm_id` | int64 | 0.0% | 154173029.0 – 13208372240.0（均值：5928268646.9723） | | `osm_type` | object | 0.0% | node、way | | `completeness` | float64 | 0.0% | 6.25 – 46.875（均值：12.8142） | | `amenity` | object | 2.5% | 药房、医院、诊所 | | `healthcare` | object | 47.6% | 药房、医院、诊所 | | `name` | object | 15.3% | Ernest Chemist、药房、诊所 | | `changeset_id` | int64 | 0.0% | 7911073.0 – 173278687.0（均值：110439476.5807） | | `changeset_version` | int64 | 0.0% | 1.0 – 11.0（均值：1.7911） | | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | 无 | | `uuid` | object | 0.0% | 4697c2eae21643758a915ac72eced9e9、659326089e434fde945d521520622643、9c69dfb7516b4d4293399c199043db75 | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-20 | --- ## 数值型变量统计 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `x` | -2.9733 | 1.1949 | -0.721 | -0.2449 | | `y` | 4.8838 | 10.9796 | 6.2805 | 5.6588 | | `osm_id` | 154173029.0 | 13208372240.0 | 5928268646.9723 | 6542893586.5 | | `completeness` | 6.25 | 46.875 | 12.8142 | 12.5 | | `changeset_id` | 7911073.0 | 173278687.0 | 110439476.5807 | 119505116.0 | | `changeset_version` | 1.0 | 11.0 | 1.7911 | 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。将列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。删除了23列缺失值占比超过80%的字段，包括`operator`、`source`、`speciality`、`operator_type`、`operational_status`、`opening_hours`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时间型。本数据集采用固定随机种子（42）以80/20的比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式。 --- ## 数据集局限性 - 数据源自全球医疗站点测绘项目，并未经过电羊非洲（Electric Sheep Africa）的独立验证。 - 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。 - 以下列的缺失值占比超过20%，在建模过程中需谨慎使用：`x`、`y`、`healthcare`。 - 如需了解发布方的方法论说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/ghana-healthsites)。 --- ## 引用格式 bibtex @dataset{hdx_africa_health_facilities_ghana, title = {加纳医疗站点}, author = {全球医疗站点测绘项目}, year = {2025}, url = {https://data.humdata.org/dataset/ghana-healthsites}, note = {由电羊非洲（Electric Sheep Africa）重新打包以适配机器学习使用（https://huggingface.co/electricsheepafrica）} } --- *[电羊非洲（Electric Sheep Africa）—— 非洲的机器学习数据集基础设施。尼日利亚拉各斯。]*
