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africa-health-facilities-zimbabwe

Tasks:
Tabular Classification
Modalities:
Tabular
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English
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Tags:
africa
humanitarian
hdx
electric-sheep-africa
health-facilities
hxl
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metadata 
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
  - zwe
pretty_name: Zimbabwe Healthsites
dataset_info:
  splits:
    - name: train
      num_examples: 755
    - name: test
      num_examples: 188

Zimbabwe Healthsites

Publisher: Global Healthsites Mapping Project · Source: HDX · License: ODbL · Updated: 2025-04-15

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 2025-04-15. Geographic scope: ZWE.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics

Domain Public health
Unit of observation Tabular records
Rows (total) 944
Columns 13 (6 numeric, 6 categorical, 0 datetime)
Train split 755 rows
Test split 188 rows
Geographic scope ZWE
Publisher Global Healthsites Mapping Project
HDX last updated 2025-04-15

Variables

Geographic — x (range 25.837–32.9726), y (range -22.2136–-15.6681), osm_type (node, way), loc_amenity (clinic, hospital, pharmacy).

Temporal — changeset_timestamp.

Identifier / Metadata — osm_id (range -12600896.0–12480272510.0), loc_name (Clinic, Rural Health Care Centre, Gumbo Clinic), changeset_id (range 5988946.0–164035674.0), meta_id (7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788), esa_source (HDX) and 1 others.

Other — completeness (range 6.25–34.375), changeset_version (range 1.0–8.0).

Quick Start 

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
x float64 36.3% 25.837 – 32.9726 (mean 31.0282)
y float64 36.3% -22.2136 – -15.6681 (mean -18.3389)
osm_id int64 0.0% -12600896.0 – 12480272510.0 (mean 5784919390.7299)
osm_type object 0.0% node, way
completeness float64 0.0% 6.25 – 34.375 (mean 10.5667)
loc_amenity object 1.1% clinic, hospital, pharmacy
loc_name object 5.4% Clinic, Rural Health Care Centre, Gumbo Clinic
changeset_id float64 0.6% 5988946.0 – 164035674.0 (mean 101901878.4051)
changeset_version float64 0.6% 1.0 – 8.0 (mean 1.5448)
changeset_timestamp datetime64[ns, UTC] 0.6%
meta_id object 0.0% 7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-20

Numeric Summary

Column Min Max Mean Median
x 25.837 32.9726 31.0282 31.1257
y -22.2136 -15.6681 -18.3389 -17.928
osm_id -12600896.0 12480272510.0 5784919390.7299 6202614917.5
completeness 6.25 34.375 10.5667 9.375
changeset_id 5988946.0 164035674.0 101901878.4051 124558102.0
changeset_version 1.0 8.0 1.5448 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. 24 column(s) with >80% missing values were removed: meta_healthcare, meta_operator, geo_bounds_url, meta_speciality, meta_operator_type, contact_phone.... 1 exact duplicate rows were removed. 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.
  • Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation 

@dataset{hdx_africa_health_facilities_zimbabwe,
  title     = {Zimbabwe Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-healthsites},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.