Datasets:

electricsheepafrica
/

africa-education-djibouti

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - education
  - health-facilities
  - transportation
  - dji
pretty_name: Djibouti - Accessibility Indicators
dataset_info:
  splits:
    - name: train
      num_examples: 238
    - name: test
      num_examples: 59

Djibouti - Accessibility Indicators

Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology) · Source: HDX · License: cc-by-sa · Updated: 2026-02-25

Abstract

This dataset provides insights into spatial accessibility to healthcare and education services across Djibouti. It has been created using free and open tools such as openrouteservice and open data sources, primarily OpenStreetMap (OSM).

To assess accessibility to education and healthcare, we use travel-time isochrones—polygons representing areas reachable within a given time or distance by car. We overlay these isochrones with WorldPop population data, which provides 100m-resolution estimates. This allows us to calculate the population within time intervals from 10 to 120 minutes away from hospital services and distance intervals from 5 to 50 km away from schools. The unit of analysis is defined by geoboundaries country borders, and where available we also summarise results at finer administrative levels (ADM 1–4).

Data Structure:

name: Region or country name.
iso: ISO3 country code.
id: Unique identifier for the administrative unit.
country: ISO3 country code.
admin_level: Administrative level of the unit.
category: Service category — education, hospitals or primary_healthcare.
range_type: Method used for the catchment zone — distance or time.
range: Distance (in meters) or Time away (in seconds) from schools used to generate the polygon.
population: Total population within the specified range.
school_age_population: Number of school-age individuals within the range.
school_age_population_share: Cumulative percentage of school-age population.
school_age_population_interval: Incremental school-age population added in the current distance band.
school_age_population_interval_share: Proportion of new school-age population in the current interval.
population_share: Cumulative percentage of total population.
population_interval: Incremental population added in the current distance band.
population_interval_share: Share of the total population represented by the current interval.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

References:

Further Information:

Open Access Lens

Limitations:

OSM Completeness: This analysis relies on OpenStreetMap (OSM) data. While OSM is the most complete open map of the world, data quality varies significantly by region. In areas with unmapped roads or facilities, accessibility may be underestimated.
Population Estimates: Population counts are derived from WorldPop top-down estimates (constrained). These are statistical models based on census projections and satellite imagery, not direct census counts, and may contain inaccuracies at the local pixel level.
Travel Time Assumptions: Isochrones are calculated using standard vehicle speeds for different road types. These models do not account for real-time traffic, seasonal weather conditions (e.g., flooding), or road surface degradation.
Boundary Precision: Administrative boundaries are sourced from geoBoundaries. These may differ slightly from official government demarcations or other schemas.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-02-25. Geographic scope: DJI.

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

Dataset Characteristics


Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	298
Columns	15 (5 numeric, 10 categorical, 0 datetime)
Train split	238 rows
Test split	59 rows
Geographic scope	DJI
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-02-25

Variables

Geographic — iso (DJ-AS, DJ-AR, DJ-DI), country (DJI), admin_level (ADM2, ADM1, ADM0), category (education), range_type (DISTANCE) and 5 others.

Identifier / Metadata — name (Djibouti, Obock, Tadjourah), id (41766387B65421474667097, 25444519B7681288134121, 25444519B1274960689098), esa_source (HDX), esa_processed (2026-04-27).

Other — range (range 5000.0–50000.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-education-djibouti")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`name`	object	0.0%	Djibouti, Obock, Tadjourah
`iso`	object	53.0%	DJ-AS, DJ-AR, DJ-DI
`id`	object	0.0%	41766387B65421474667097, 25444519B7681288134121, 25444519B1274960689098
`country`	object	0.0%	DJI
`admin_level`	object	0.0%	ADM2, ADM1, ADM0
`category`	object	0.0%	education
`range_type`	object	0.0%	DISTANCE
`range`	int64	0.0%	5000.0 – 50000.0 (mean 27651.0067)
`population_type`	object	0.0%	school_age, total
`population`	int64	0.0%	0.0 – 616929.0 (mean 91235.9933)
`population_share`	float64	0.0%	0.0 – 99.16 (mean 39.6819)
`population_interval`	int64	0.0%	0.0 – 579751.0 (mean 9247.2584)
`population_interval_share`	float64	0.0%	0.0 – 96.39 (mean 4.1372)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-27

Numeric Summary

Column	Min	Max	Mean	Median
`range`	5000.0	50000.0	27651.0067	30000.0
`population`	0.0	616929.0	91235.9933	12131.0
`population_share`	0.0	99.16	39.6819	45.88
`population_interval`	0.0	579751.0	9247.2584	26.0
`population_interval_share`	0.0	96.39	4.1372	0.04

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. 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 HeiGIT (Heidelberg Institute for Geoinformation Technology) 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: iso.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_africa_education_djibouti,
  title     = {Djibouti - Accessibility Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/djibouti-accessibility-indicators},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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