electricsheepafrica/africa-aviation-south-sudan

Airports in South Sudan

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-11

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Abstract

List of airports in South Sudan, with latitude and longitude. Unverified community data from http://ourairports.com/countries/SS/

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-11. Geographic scope: SSD.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	103
Columns	22 (6 numeric, 15 categorical, 0 datetime)
Train split	82 rows
Test split	20 rows
Geographic scope	SSD
Publisher	OurAirports
HDX last updated	2026-04-11

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Variables

Geographic — type (small_airport, heliport, medium_airport), latitude_deg (range 3.5977–11.6448), longitude_deg (range 25.6811–35.8328), country_name (South Sudan, #country +name), iso_country (SS, #country +code +iso2) and 5 others.

Temporal — last_updated.

Outcome / Measurement — score (range 0.0–1000.0).

Identifier / Metadata — id (range 3247.0–607394.0), ident (#meta +code, SS-0040, SS-0001), name (#loc +airport +name, Nyal Airport, Ajuini Airstrip), gps_code, esa_source and 1 others.

Other — elevation_ft (range 1278.0–2500.0), continent (AF, #region +continent +code), scheduled_service (range 0.0–1.0), wikipedia_link.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-aviation-south-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
id	float64	1.0%	3247.0 – 607394.0 (mean 321116.6863)
ident	object	0.0%	#meta +code, SS-0040, SS-0001
type	object	0.0%	small_airport, heliport, medium_airport
name	object	0.0%	#loc +airport +name, Nyal Airport, Ajuini Airstrip
latitude_deg	float64	1.0%	3.5977 – 11.6448 (mean 7.7364)
longitude_deg	float64	1.0%	25.6811 – 35.8328 (mean 30.8364)
elevation_ft	float64	41.7%	1278.0 – 2500.0 (mean 1535.5833)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	South Sudan, #country +name
iso_country	object	0.0%	SS, #country +code +iso2
region_name	object	0.0%	Jonglei State, Upper Nile State, Unity State
iso_region	object	0.0%	SS-JG, SS-NU, SS-UY
local_region	object	0.0%	JG, NU, UY
municipality	object	4.9%	Turalei, Pulmok, Poktap
scheduled_service	float64	1.0%	0.0 – 1.0 (mean 0.0294)
gps_code	object	56.3%
wikipedia_link	object	77.7%
keywords	object	72.8%
score	float64	1.0%	0.0 – 1000.0 (mean 72.549)
last_updated	datetime64[ns, UTC]	1.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	3247.0	607394.0	321116.6863	344053.5
latitude_deg	3.5977	11.6448	7.7364	8.1316
longitude_deg	25.6811	35.8328	30.8364	31.2422
elevation_ft	1278.0	2500.0	1535.5833	1354.5
scheduled_service	0.0	1.0	0.0294	0.0
score	0.0	1000.0	72.549	50.0

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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. 4 column(s) with >80% missing values were removed: icao_code, iata_code, local_code, home_link. 7 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.

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Limitations

• Data originates from OurAirports 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: elevation_ft, gps_code, wikipedia_link, keywords.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_aviation_south_sudan,
  title     = {Airports in South Sudan},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-ssd},
  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.