electricsheepasia/asia-aviation-ourairports-qatar

Airports in Qatar

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-01-10

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-01-10. Geographic scope: QAT.

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)	24
Columns	21 (6 numeric, 14 categorical, 0 datetime)
Train split	19 rows
Test split	4 rows
Geographic scope	QAT
Publisher	OurAirports
HDX last updated	2026-01-10

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Variables

Geographic — type (heliport, small_airport, closed), latitude_deg (range 24.7478–25.754), longitude_deg (range 50.8206–52.4156), country_name (Qatar, #country +name), iso_country (QA, #country +code +iso2) and 4 others.

Temporal — last_updated.

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

Identifier / Metadata — id (range 5308.0–598538.0), ident (#meta +code, OTHH, OTBD), name (Military Helipad, Hamad International Airport, #loc +airport +name), gps_code, esa_source and 1 others.

Other — elevation_ft (range 3.0–148.0), continent (AS, #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/asia-aviation-ourairports-qatar")
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	4.2%	5308.0 – 598538.0 (mean 306706.4783)
ident	object	0.0%	#meta +code, OTHH, OTBD
type	object	0.0%	heliport, small_airport, closed
name	object	0.0%	Military Helipad, Hamad International Airport, #loc +airport +name
latitude_deg	float64	4.2%	24.7478 – 25.754 (mean 25.2915)
longitude_deg	float64	4.2%	50.8206 – 52.4156 (mean 51.3215)
elevation_ft	float64	54.2%	3.0 – 148.0 (mean 51.1818)
continent	object	0.0%	AS, #region +continent +code
country_name	object	0.0%	Qatar, #country +name
iso_country	object	0.0%	QA, #country +code +iso2
region_name	object	0.0%	Ad-Dawhah Municipality, Al Rayyan Municipality, Al-Shahaniya Municipality
iso_region	object	0.0%	QA-DA, QA-RA, QA-SH
local_region	object	0.0%	DA, RA, SH
municipality	object	12.5%	Doha, Dukhan, Al Rayyan
scheduled_service	float64	4.2%	0.0 – 1.0 (mean 0.087)
gps_code	object	79.2%
wikipedia_link	object	75.0%
score	float64	4.2%	0.0 – 1000.0 (mean 108.6957)
last_updated	datetime64[ns, UTC]	4.2%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	5308.0	598538.0	306706.4783	340581.0
latitude_deg	24.7478	25.754	25.2915	25.2924
longitude_deg	50.8206	52.4156	51.3215	51.363
elevation_ft	3.0	148.0	51.1818	35.0
scheduled_service	0.0	1.0	0.087	0.0
score	0.0	1000.0	108.6957	0.0

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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. 5 column(s) with >80% missing values were removed: icao_code, iata_code, local_code, home_link, keywords. 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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_aviation_ourairports_qatar,
  title     = {Airports in Qatar},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-qat},
  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.