electricsheepafrica/africa-health-facilities-tanzania

---
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
- tza
pretty_name: "Tanzania Healthsites"
dataset_info:
splits:
- name: train
num_examples: 5435
- name: test
num_examples: 1358
---

# Tanzania Healthsites

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

*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)** | 6,794 |
| **Columns** | 15 (6 numeric, 8 categorical, 0 datetime) |
| **Train split** | 5,435 rows |
| **Test split** | 1,358 rows |
| **Geographic scope** | TZA |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 29.6275–40.1966), `y` (range -11.2897–-1.0002), `osm_type` (node, way), `amenity` (clinic, pharmacy, hospital), `addr_city` (Dar es salaam, Dar es salaaam, Mwanza).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 43757839.0–13227399086.0), `name` (Duka la Dawa, Duka la dawa, Pharmacy), `changeset_id` (range 9718216.0–173170052.0), `uuid` (be6e570e963d47029794c24cd02cdc42, 5f9a9177f58c4b18abeece2aa3524a87, b38bdb9e0fc247c797adb16cd6cb8585), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–59.375), `healthcare` (hospital, pharmacy, clinic), `changeset_version` (range 1.0–27.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 39.9% | 29.6275 – 40.1966 (mean 36.3442) |
| `y` | float64 | 39.9% | -11.2897 – -1.0002 (mean -5.4772) |
| `osm_id` | int64 | 0.0% | 43757839.0 – 13227399086.0 (mean 4059717204.387) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 59.375 (mean 12.8988) |
| `amenity` | object | 1.6% | clinic, pharmacy, hospital |
| `healthcare` | object | 76.8% | hospital, pharmacy, clinic |
| `name` | object | 15.2% | Duka la Dawa, Duka la dawa, Pharmacy |
| `addr_city` | object | 74.3% | Dar es salaam, Dar es salaaam, Mwanza |
| `changeset_id` | int64 | 0.0% | 9718216.0 – 173170052.0 (mean 87367574.8179) |
| `changeset_version` | int64 | 0.0% | 1.0 – 27.0 (mean 2.0321) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% | |
| `uuid` | object | 0.0% | be6e570e963d47029794c24cd02cdc42, 5f9a9177f58c4b18abeece2aa3524a87, b38bdb9e0fc247c797adb16cd6cb8585 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 29.6275 | 40.1966 | 36.3442 | 36.6874 |
| `y` | -11.2897 | -1.0002 | -5.4772 | -6.2431 |
| `osm_id` | 43757839.0 | 13227399086.0 | 4059717204.387 | 3842264467.0 |
| `completeness` | 6.25 | 59.375 | 12.8988 | 9.375 |
| `changeset_id` | 9718216.0 | 173170052.0 | 87367574.8179 | 84460483.0 |
| `changeset_version` | 1.0 | 27.0 | 2.0321 | 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`. 22 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`, `addr_city`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/tanzania-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_tanzania,
title = {Tanzania Healthsites},
author = {Global Healthsites Mapping Project},
year = {2025},
url = {https://data.humdata.org/dataset/tanzania-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.*