electricsheepafrica/africa-health-facilities-togo
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--- annotations_creators: - no-annotation language_creators: - found language: - en license: other multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - health-facilities - tgo pretty_name: "Togo Healthsites" dataset_info: splits: - name: train num_examples: 772 - name: test num_examples: 193 --- # Togo Healthsites **Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/togo-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: **TGO**. *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)** | 965 | | **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) | | **Train split** | 772 rows | | **Test split** | 193 rows | | **Geographic scope** | TGO | | **Publisher** | Global Healthsites Mapping Project | | **HDX last updated** | 2025-10-15 | --- ## Variables **Geographic** — `x` (range -0.078–1.8027), `y` (range 6.1169–11.1156), `osm_type` (node, way), `amenity` (hospital, pharmacy, clinic). **Temporal** — `changeset_timestamp`. **Identifier / Metadata** — `osm_id` (range 102858259.0–13230239373.0), `name` (USP, Dépôt de pharmacie, Dispensaire), `changeset_id` (range 8196215.0–173222949.0), `uuid` (8bb275317f3149daa6e3e74bab88ae3c, 9d4b95002b1e4589b475720345f1cd2d, 923421275a3f4ce3858bf48ab601dcf3), `esa_source` (HDX) and 1 others. **Other** — `completeness` (range 6.25–28.125), `healthcare` (hospital, pharmacy, nurse), `changeset_version` (range 1.0–9.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-health-facilities-togo") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `x` | float64 | 14.2% | -0.078 – 1.8027 (mean 1.1127) | | `y` | float64 | 14.2% | 6.1169 – 11.1156 (mean 7.1282) | | `osm_id` | int64 | 0.0% | 102858259.0 – 13230239373.0 (mean 6808499430.9378) | | `osm_type` | object | 0.0% | node, way | | `completeness` | float64 | 0.0% | 6.25 – 28.125 (mean 11.3795) | | `amenity` | object | 2.9% | hospital, pharmacy, clinic | | `healthcare` | object | 72.2% | hospital, pharmacy, nurse | | `name` | object | 7.2% | USP, Dépôt de pharmacie, Dispensaire | | `changeset_id` | int64 | 0.0% | 8196215.0 – 173222949.0 (mean 102755340.8207) | | `changeset_version` | int64 | 0.0% | 1.0 – 9.0 (mean 1.4964) | | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | | | `uuid` | object | 0.0% | 8bb275317f3149daa6e3e74bab88ae3c, 9d4b95002b1e4589b475720345f1cd2d, 923421275a3f4ce3858bf48ab601dcf3 | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-21 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `x` | -0.078 | 1.8027 | 1.1127 | 1.1943 | | `y` | 6.1169 | 11.1156 | 7.1282 | 6.3179 | | `osm_id` | 102858259.0 | 13230239373.0 | 6808499430.9378 | 7626334870.0 | | `completeness` | 6.25 | 28.125 | 11.3795 | 9.375 | | `changeset_id` | 8196215.0 | 173222949.0 | 102755340.8207 | 106738868.0 | | `changeset_version` | 1.0 | 9.0 | 1.4964 | 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`. 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: `healthcare`. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/togo-healthsites) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_health_facilities_togo, title = {Togo Healthsites}, author = {Global Healthsites Mapping Project}, year = {2025}, url = {https://data.humdata.org/dataset/togo-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: - 公开资源爬取 language: - 英语 license: - 其他协议 multilinguality: - 单语言 size_categories: - 样本量不足1000 source_datasets: - 原生原创数据集 task_categories: - 表格分类 task_ids: [] tags: - 非洲 - 人道主义 - HDX - 电动绵羊非洲（Electric Sheep Africa） - 医疗设施 - 多哥（TGO） pretty_name: "多哥医疗设施（Togo Healthsites）" dataset_info: splits: - name: train num_examples: 772 - name: test num_examples: 193 --- # 多哥医疗设施数据集 **发布方**：全球医疗设施测绘项目（Global Healthsites Mapping Project） · **来源**：[HDX（人道主义数据交换平台）](https://data.humdata.org/dataset/togo-healthsites) · **许可协议**：`ODbL` · **最后更新**：2025-10-15 --- ## 摘要 本数据集收录了多哥境内运营中医疗设施的完整清单，包含以下属性：设施名称、设施类型、开展业务、纬度、经度。 数据集中每一行均为一条标准表格记录。本数据集最后于2025年10月15日在HDX平台完成更新。地理覆盖范围：**多哥（TGO）**。 *本数据集已由[电动绵羊非洲（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 | 分类项 | 详情 | |---|---| | **应用领域** | 公共卫生 | | **观测单元** | 表格记录 | | **总记录数** | 965 | | **字段总数** | 14（6个数值型字段、7个分类型字段、0个日期时间型字段） | | **训练集划分** | 772条记录 | | **测试集划分** | 193条记录 | | **地理覆盖范围** | 多哥（TGO） | | **发布方** | 全球医疗设施测绘项目 | | **HDX平台最后更新时间** | 2025-10-15 | --- ## 变量说明 ### 地理类变量 `x`（经度，取值范围：-0.078~1.8027）、`y`（纬度，取值范围：6.1169~11.1156）、`osm_type`（开放街道地图类型，可选值：节点、道路）、`amenity`（公共服务设施类型，可选值：医院、药房、诊所）。 ### 时间类变量 `changeset_timestamp`（变更集时间戳）。 ### 标识与元数据类变量 `osm_id`（开放街道地图唯一标识，取值范围：102858259.0~13230239373.0）、`name`（设施名称，示例值：USP、药房仓库、诊疗所）、`changeset_id`（变更集标识，取值范围：8196215.0~173222949.0）、`uuid`（通用唯一识别码，示例值：8bb275317f3149daa6e3e74bab88ae3c、9d4b95002b1e4589b475720345f1cd2d、923421275a3f4ce3858bf48ab601dcf3）、`esa_source`（数据来源，取值：HDX）及其他1个附属字段。 ### 其他类变量 `completeness`（数据完整度，取值范围：6.25~28.125）、`healthcare`（医疗服务类型，可选值：医院、药房、护士站）、`changeset_version`（变更集版本，取值范围：1.0~9.0）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-health-facilities-togo") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据Schema | 列名 | 数据类型 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `x` | float64 | 14.2% | -0.078 – 1.8027（均值：1.1127） | | `y` | float64 | 14.2% | 6.1169 – 11.1156（均值：7.1282） | | `osm_id` | int64 | 0.0% | 102858259.0 – 13230239373.0（均值：6808499430.9378） | | `osm_type` | object | 0.0% | 节点、道路 | | `completeness` | float64 | 0.0% | 6.25 – 28.125（均值：11.3795） | | `amenity` | object | 2.9% | 医院、药房、诊所 | | `healthcare` | object | 72.2% | 医院、药房、护士站 | | `name` | object | 7.2% | USP、药房仓库、诊疗所 | | `changeset_id` | int64 | 0.0% | 8196215.0 – 173222949.0（均值：102755340.8207） | | `changeset_version` | int64 | 0.0% | 1.0 – 9.0（均值：1.4964） | | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | 无 | | `uuid` | object | 0.0% | 8bb275317f3149daa6e3e74bab88ae3c、9d4b95002b1e4589b475720345f1cd2d、923421275a3f4ce3858bf48ab601dcf3 | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-21 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `x` | -0.078 | 1.8027 | 1.1127 | 1.1943 | | `y` | 6.1169 | 11.1156 | 7.1282 | 6.3179 | | `osm_id` | 102858259.0 | 13230239373.0 | 6808499430.9378 | 7626334870.0 | | `completeness` | 6.25 | 28.125 | 11.3795 | 9.375 | | `changeset_id` | 8196215.0 | 173222949.0 | 102755340.8207 | 106738868.0 | | `changeset_version` | 1.0 | 9.0 | 1.4964 | 1.0 | --- ## 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。对所有列名进行小写转换，并统一为蛇形命名规范。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为标准缺失值标记`NaN`。移除了23列缺失值占比超过80%的冗余字段，包括`operator`（运营方）、`source`（数据来源）、`speciality`（专科科室）、`operator_type`（运营方类型）、`operational_status`（运营状态）、`opening_hours`（开放时间）等。根据字段解析成功率（阈值设定为85%），将1列从字符串类型转换为数值型或日期时间型。本数据集采用固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件存储。 --- ## 局限性说明 1. 本数据集源自全球医疗设施测绘项目，未经过电动绵羊非洲团队的独立验证。 2. 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，或原始采集阶段存在的采样偏差。 3. 以下字段的缺失值占比超过20%，在建模任务中需谨慎使用：`healthcare`（医疗服务类型）。 4. 如需了解发布方提供的原始方法学说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/togo-healthsites)。 --- ## 引用格式 bibtex @dataset{hdx_africa_health_facilities_togo, title = {Togo Healthsites}, author = {Global Healthsites Mapping Project}, year = {2025}, url = {https://data.humdata.org/dataset/togo-healthsites}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[电动绵羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商，总部位于尼日利亚拉各斯。*
