electricsheepasia/asia-health-facilities-sri-lanka-healthsites

Sri Lanka Healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · 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: LKA.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — x (range 79.7–81.8654), y (range 5.9423–9.8235), osm_type (node, way), amenity (hospital, pharmacy, clinic).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range 99282980.0–13198260872.0), name (Medical Center, Base Hospital, Healthguard), changeset_id (range 5381749.0–173195844.0), uuid (c2c85f3c77ce4c7aa37c85a3e96b9168, aa51ec7bb15445f59f5c4ec9d25058d4, 253d37b866f34e9bb5ec6b638b85ef77), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–37.5), healthcare (hospital, pharmacy, clinic), changeset_version (range 1.0–24.0).

Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. 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`, `operatortype, operationalstatus`, `openinghours`.... 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, name.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_health_facilities_all,
  title     = {Sri Lanka Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sri-lanka-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.