| --- |
| 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 |
| - dji |
| pretty_name: "Djibouti Healthsites" |
| dataset_info: |
| splits: |
| - name: train |
| num_examples: 38 |
| - name: test |
| num_examples: 9 |
| --- |
| |
| # Djibouti Healthsites |
|
|
| **Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/djibouti-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: **DJI**. |
|
|
| *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)** | 48 | |
| | **Columns** | 16 (6 numeric, 9 categorical, 0 datetime) | |
| | **Train split** | 38 rows | |
| | **Test split** | 9 rows | |
| | **Geographic scope** | DJI | |
| | **Publisher** | Global Healthsites Mapping Project | |
| | **HDX last updated** | 2025-10-15 | |
|
|
| --- |
|
|
| ## Variables |
|
|
| **Geographic** — `x` (range 43.0808–43.1525), `y` (range 11.5427–11.5964), `osm_type` (node, way), `amenity` (hospital, pharmacy, clinic), `addr_city` (جيبوتي, Ali Sabieh علي صبيح, بلبالا). |
|
|
| **Temporal** — `changeset_timestamp`. |
|
|
| **Identifier / Metadata** — `osm_id` (range 228050145.0–9371609717.0), `name` (Laboratoire d'analyses médicales Abdan, Service de Santé des Armées خدمة الصحة العسكرية, Pharmacie BSH صيدلية بي إس أيتش), `changeset_id` (range 37442253.0–172509033.0), `uuid` (fd8a4954019143ac8f7a404b73ad2845, 12967dd31dc348828ce7f356252aa4b4, 7efbe13bc2df4074bdafd8035a91dfd5), `esa_source` (HDX) and 1 others. |
|
|
| **Other** — `completeness` (range 6.25–25.0), `healthcare` (hospital, pharmacy, clinic), `operator` (Ministère de la Santé, Dr Guillard, Dr Dell'Aquila, Nicolas Gorgalis), `changeset_version` (range 1.0–8.0). |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("electricsheepafrica/africa-health-facilities-djibouti") |
| train = ds["train"].to_pandas() |
| test = ds["test"].to_pandas() |
| |
| print(train.shape) |
| train.head() |
| ``` |
|
|
| --- |
|
|
| ## Schema |
|
|
| | Column | Type | Null % | Range / Sample Values | |
| |---|---|---|---| |
| | `x` | float64 | 45.8% | 43.0808 – 43.1525 (mean 43.1366) | |
| | `y` | float64 | 45.8% | 11.5427 – 11.5964 (mean 11.5778) | |
| | `osm_id` | int64 | 0.0% | 228050145.0 – 9371609717.0 (mean 3015709806.1458) | |
| | `osm_type` | object | 0.0% | node, way | |
| | `completeness` | float64 | 0.0% | 6.25 – 25.0 (mean 15.7552) | |
| | `amenity` | object | 6.2% | hospital, pharmacy, clinic | |
| | `healthcare` | object | 4.2% | hospital, pharmacy, clinic | |
| | `name` | object | 10.4% | Laboratoire d'analyses médicales Abdan, Service de Santé des Armées خدمة الصحة العسكرية, Pharmacie BSH صيدلية بي إس أيتش | |
| | `operator` | object | 72.9% | Ministère de la Santé, Dr Guillard, Dr Dell'Aquila, Nicolas Gorgalis | |
| | `addr_city` | object | 47.9% | جيبوتي, Ali Sabieh علي صبيح, بلبالا | |
| | `changeset_id` | int64 | 0.0% | 37442253.0 – 172509033.0 (mean 100585750.0208) | |
| | `changeset_version` | int64 | 0.0% | 1.0 – 8.0 (mean 3.4792) | |
| | `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | | |
| | `uuid` | object | 0.0% | fd8a4954019143ac8f7a404b73ad2845, 12967dd31dc348828ce7f356252aa4b4, 7efbe13bc2df4074bdafd8035a91dfd5 | |
| | `esa_source` | object | 0.0% | HDX | |
| | `esa_processed` | object | 0.0% | 2026-04-20 | |
|
|
| --- |
|
|
| ## Numeric Summary |
|
|
| | Column | Min | Max | Mean | Median | |
| |---|---|---|---|---| |
| | `x` | 43.0808 | 43.1525 | 43.1366 | 43.146 | |
| | `y` | 11.5427 | 11.5964 | 11.5778 | 11.59 | |
| | `osm_id` | 228050145.0 | 9371609717.0 | 3015709806.1458 | 3691941118.5 | |
| | `completeness` | 6.25 | 25.0 | 15.7552 | 15.625 | |
| | `changeset_id` | 37442253.0 | 172509033.0 | 100585750.0208 | 83020145.5 | |
| | `changeset_version` | 1.0 | 8.0 | 3.4792 | 3.0 | |
|
|
| --- |
|
|
| ## 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: `source`, `speciality`, `operator_type`, `operational_status`, `opening_hours`, `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`, `operator`, `addr_city`. |
| - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/djibouti-healthsites) for the publisher's own methodology notes and caveats. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{hdx_africa_health_facilities_djibouti, |
| title = {Djibouti Healthsites}, |
| author = {Global Healthsites Mapping Project}, |
| year = {2025}, |
| url = {https://data.humdata.org/dataset/djibouti-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.* |
