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africa-health-facilities-djibouti

Tasks:
Tabular Classification
Tabular Regression
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africa
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open-data
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health
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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
  - 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 · 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.

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 

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 for the publisher's own methodology notes and caveats.

Citation 

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.