--- 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 - hxl - sle pretty_name: "Sierra Leone Healthsites" dataset_info: splits: - name: train num_examples: 444 - name: test num_examples: 111 --- # Sierra Leone Healthsites **Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/sierra-leone-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: **SLE**. *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)** | 556 | | **Columns** | 23 (6 numeric, 16 categorical, 0 datetime) | | **Train split** | 444 rows | | **Test split** | 111 rows | | **Geographic scope** | SLE | | **Publisher** | Global Healthsites Mapping Project | | **HDX last updated** | 2025-10-15 | --- ## Variables **Geographic** — `x` (range -13.2713–-10.3167), `y` (range 6.9678–9.9737), `osm_type` (node, way), `amenity` (clinic, pharmacy, hospital), `speciality` (clinic, hospital, yes) and 2 others. **Temporal** — `changeset_timestamp`. **Identifier / Metadata** — `osm_id` (range 224729043.0–13130524315.0), `name` (Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP), `source` (Red Cross Field Survey, MSF-CH, MSFsurvey), `water_source`, `changeset_id` (range 19113513.0–172755228.0) and 3 others. **Other** — `completeness` (range 6.25–40.625), `healthcare` (hospital, pharmacy, clinic), `operator` (Ministry of Health and Sanitation, government, combination), `opening_hours` (24/7, 08:00-17:00, Mo-Fr 08:00-17:00), `dispensing` (yes, no) and 2 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-health-facilities-sierra-leone") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `x` | float64 | 30.6% | -13.2713 – -10.3167 (mean -11.8405) | | `y` | float64 | 30.6% | 6.9678 – 9.9737 (mean 8.4376) | | `osm_id` | int64 | 0.0% | 224729043.0 – 13130524315.0 (mean 4009847857.0971) | | `osm_type` | object | 0.0% | node, way | | `completeness` | float64 | 0.0% | 6.25 – 40.625 (mean 18.7837) | | `amenity` | object | 2.9% | clinic, pharmacy, hospital | | `healthcare` | object | 65.1% | hospital, pharmacy, clinic | | `name` | object | 11.9% | Ministry of Health and Sanitation Clinic, Matanal Children's Health Post Hospital Building, MCHP | | `operator` | object | 75.4% | Ministry of Health and Sanitation, government, combination | | `source` | object | 50.5% | Red Cross Field Survey, MSF-CH, MSFsurvey | | `speciality` | object | 74.5% | clinic, hospital, yes | | `opening_hours` | object | 76.3% | 24/7, 08:00-17:00, Mo-Fr 08:00-17:00 | | `dispensing` | object | 79.0% | yes, no | | `emergency` | object | 79.7% | yes, no | | `water_source` | object | 78.8% | | | `addr_street` | object | 75.4% | | | `addr_city` | object | 64.9% | | | `changeset_id` | int64 | 0.0% | 19113513.0 – 172755228.0 (mean 104872179.8903) | | `changeset_version` | int64 | 0.0% | 1.0 – 10.0 (mean 2.545) | | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | | | `uuid` | object | 0.0% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `x` | -13.2713 | -10.3167 | -11.8405 | -11.7458 | | `y` | 6.9678 | 9.9737 | 8.4376 | 8.4614 | | `osm_id` | 224729043.0 | 13130524315.0 | 4009847857.0971 | 4504611402.0 | | `completeness` | 6.25 | 40.625 | 18.7837 | 12.5 | | `changeset_id` | 19113513.0 | 172755228.0 | 104872179.8903 | 108431838.0 | | `changeset_version` | 1.0 | 10.0 | 2.545 | 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`. 14 column(s) with >80% missing values were removed: `operator_type`, `operational_status`, `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`, `source`, `speciality`, `opening_hours`, `dispensing`.... - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/sierra-leone-healthsites) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_health_facilities_sierra_leone, title = {Sierra Leone Healthsites}, author = {Global Healthsites Mapping Project}, year = {2025}, url = {https://data.humdata.org/dataset/sierra-leone-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.*