CoolFace
Datasetpublic

electricsheepasia/asia-aviation-ourairports-turkmenistan

Airports in Turkmenistan Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-09 Abstract List of airports in Turkmenistan, with latitude and longitude. Unverified community data from http://ourairports.com/countries/TM/ Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-09. Geographic scope: TKM. Curated into ML-ready Parquet format by Electric Sheep Africa.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-aviation-ourairports-turkmenistan.

sourceHugging Faceotherupdated 5mo agoView on Hugging Face
0likes12downloads
Dataset Card

Airports in Turkmenistan

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-09


Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-09. Geographic scope: TKM.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).


Dataset Characteristics

DomainHumanitarian and development data
Unit of observationFirst-level administrative unit observations
Rows (total)60
Columns20 (6 numeric, 13 categorical, 0 datetime)
Train split48 rows
Test split12 rows
Geographic scopeTKM
PublisherOurAirports
HDX last updated2026-04-09

Variables

Geographic — type (heliport, smallairport, mediumairport), latitude_deg (range 35.6629–41.7599), longitude_deg (range 52.6119–65.9655), country_name (Turkmenistan, #country +name), iso_country (TM, #country +code +iso2) and 5 others.

Temporal — last_updated.

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

Identifier / Metadata — id (range 6390.0–606160.0), ident (#meta +code, UTAA, UTAM), name (State Tribune Heliport, Ashgabat International Airport, #loc +airport +name), esa_source, esa_processed.

Other — elevation_ft (range -77.0–4820.0), continent (AS, #region +continent +code), scheduled_service (range 0.0–1.0).


Quick Start

python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-aviation-ourairports-turkmenistan")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

ColumnTypeNull %Range / Sample Values
idfloat641.7%6390.0 – 606160.0 (mean 267745.8305)
identobject0.0%#meta +code, UTAA, UTAM
typeobject0.0%heliport, smallairport, mediumairport
nameobject0.0%State Tribune Heliport, Ashgabat International Airport, #loc +airport +name
latitude_degfloat641.7%35.6629 – 41.7599 (mean 38.5301)
longitude_degfloat641.7%52.6119 – 65.9655 (mean 58.4373)
elevation_ftfloat6451.7%-77.0 – 4820.0 (mean 723.6552)
continentobject0.0%AS, #region +continent +code
country_nameobject0.0%Turkmenistan, #country +name
iso_countryobject0.0%TM, #country +code +iso2
region_nameobject0.0%Balkan Region, Ashgabat (city), Lebap Region
iso_regionobject0.0%TM-B, TM-S, TM-L
local_regionobject0.0%B, S, L
municipalityobject3.3%Ashgabat, Mary, Türkmenabat
scheduled_servicefloat641.7%0.0 – 1.0 (mean 0.1186)
keywordsobject63.3%
scorefloat641.7%0.0 – 1050.0 (mean 116.9492)
last_updateddatetime64[ns, UTC]1.7%
esa_sourceobject0.0%
esa_processedobject0.0%

Numeric Summary

ColumnMinMaxMeanMedian
id6390.0606160.0267745.8305340414.0
latitude_deg35.662941.759938.530137.9499
longitude_deg52.611965.965558.437358.361
elevation_ft-77.04820.0723.6552649.0
scheduled_service0.01.00.11860.0
score0.01050.0116.94920.0

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 6 column(s) with >80% missing values were removed: `gpscode, icaocode`, `iatacode, localcode`, `homelink, wikipedia_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.


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

Citation

bibtex
@dataset{hdx_asia_aviation_ourairports_turkmenistan,
  title     = {Airports in Turkmenistan},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-tkm},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.