electricsheepafrica/africa-education-sudan

Sudan - Accessibility Indicators

Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology) · Source: HDX · License: cc-by-sa · Updated: 2026-02-27

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Abstract

This dataset provides insights into spatial accessibility to healthcare and education services across Sudan. 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:

• Geldsetzer, P., Reinmuth, M., Ouma, P. O., Lautenbach, S. et al. (2020)
• Petricola, S., Reinmuth, M., Lautenbach, S. et al. (2022)
• Klipper, I. G., Zipf, A., and Lautenbach, S. (2021)
• Ruiz Sánchez, R., Reinmuth, M., Albornoz, C., Lautenbach, S., and Zipf, A. (2025)

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 2026-02-27. Geographic scope: SDN.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	3,716
Columns	14 (5 numeric, 9 categorical, 0 datetime)
Train split	2,972 rows
Test split	743 rows
Geographic scope	SDN
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-02-27

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Variables

Geographic — country (SDN), admin_level (ADM2, ADM1, ADM0), category (education), range_type (DISTANCE), population_type (school_age, total) and 4 others.

Identifier / Metadata — name (Khartoum, Ar Rahad, Sennar), id (16777658B19857105557319, 16777658B27261158380636, 16777658B23897511285986), esa_source (HDX), esa_processed (2026-04-27).

Other — range (range 5000.0–50000.0).

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
name	object	0.0%	Khartoum, Ar Rahad, Sennar
id	object	0.0%	16777658B19857105557319, 16777658B27261158380636, 16777658B23897511285986
country	object	0.0%	SDN
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 28100.1076)
population_type	object	0.0%	school_age, total
population	int64	0.0%	0.0 – 21624249.0 (mean 209500.3122)
population_share	float64	0.0%	0.0 – 100.0 (mean 31.0481)
population_interval	int64	0.0%	0.0 – 11360923.0 (mean 25642.4244)
population_interval_share	float64	0.0%	0.0 – 97.04 (mean 4.0914)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
range	5000.0	50000.0	28100.1076	30000.0
population	0.0	21624249.0	209500.3122	28046.5
population_share	0.0	100.0	31.0481	22.79
population_interval	0.0	11360923.0	25642.4244	2013.0
population_interval_share	0.0	97.04	4.0914	1.47

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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. 1 column(s) with >80% missing values were removed: iso. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

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

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