electricsheepafrica/africa-ourairports-tza

Airports in Tanzania

Publisher: OurAirports · Source: HDX · License: cc-by-igo · Updated: 2026-02-23

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-23. Geographic scope: TZA.

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)	212
Columns	21 (7 numeric, 13 categorical, 0 datetime)
Train split	169 rows
Test split	42 rows
Geographic scope	TZA
Publisher	OurAirports
HDX last updated	2026-02-23

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Variables

Geographic — type (small_airport, medium_airport, closed), latitude_deg (range -11.6123–-1.075), longitude_deg (range 29.6709–40.182), country_name (Tanzania, #country +name), iso_country (TZ, #country +code +iso2) and 4 others.

Temporal — last_updated.

Outcome / Measurement — score (range 0.0–1050.0).

Identifier / Metadata — id (range 3250.0–604206.0), ident (#meta +code, HTMK, TZ-0042), name (#loc +airport +name, Mikumi Airport, Mkangira Airport), gps_code (#loc +airport +code +gps, HTMH, HTMP), esa_source and 1 others.

Other — elevation_ft (range 15.0–7795.0), 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-ourairports-tza")
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	0.5%	3250.0 – 604206.0 (mean 237534.8199)
ident	object	0.0%	#meta +code, HTMK, TZ-0042
type	object	0.0%	small_airport, medium_airport, closed
name	object	0.0%	#loc +airport +name, Mikumi Airport, Mkangira Airport
latitude_deg	float64	0.5%	-11.6123 – -1.075 (mean -5.8509)
longitude_deg	float64	0.5%	29.6709 – 40.182 (mean 35.0959)
elevation_ft	float64	19.8%	15.0 – 7795.0 (mean 3311.9941)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	Tanzania, #country +name
iso_country	object	0.0%	TZ, #country +code +iso2
region_name	object	0.0%	Morogoro Region, Rukwa Region, Singida Region
iso_region	object	0.0%	TZ-16, TZ-20, TZ-23
local_region	float64	0.5%	1.0 – 31.0 (mean 14.8483)
municipality	object	6.6%	Chunya, Rungwa, Ifakara
scheduled_service	float64	0.5%	0.0 – 1.0 (mean 0.0758)
gps_code	object	59.4%	#loc +airport +code +gps, HTMH, HTMP
wikipedia_link	object	78.8%
score	float64	0.5%	0.0 – 1050.0 (mean 87.4408)
last_updated	datetime64[ns, UTC]	0.5%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	3250.0	604206.0	237534.8199	318606.0
latitude_deg	-11.6123	-1.075	-5.8509	-6.1356
longitude_deg	29.6709	40.182	35.0959	35.1591
elevation_ft	15.0	7795.0	3311.9941	3786.5
local_region	1.0	31.0	14.8483	16.0
scheduled_service	0.0	1.0	0.0758	0.0
score	0.0	1050.0	87.4408	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. 5 column(s) with >80% missing values were removed: icao_code, iata_code, local_code, home_link, keywords. 8 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: 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_africa_ourairports_tza,
  title     = {Airports in Tanzania},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-tza},
  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.