electricsheepafrica/africa-aviation-botswana

Airports in Botswana

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

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Abstract

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

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

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)	144
Columns	23 (6 numeric, 16 categorical, 0 datetime)
Train split	115 rows
Test split	28 rows
Geographic scope	BWA
Publisher	OurAirports
HDX last updated	2026-04-09

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Variables

Geographic — type (small_airport, closed, large_airport), latitude_deg (range -26.8881–-17.8317), longitude_deg (range 19.9997–29.1312), country_name (Botswana, #country +name), iso_country (BW, #country +code +iso2) and 5 others.

Temporal — last_updated.

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

Identifier / Metadata — id (range 2860.0–607154.0), ident (#meta +code, FBSK, BW-0063), name (#loc +airport +name, Sir Seretse Khama International Airport, Makalamabedi Airport), gps_code, icao_code and 2 others.

Other — elevation_ft (range 1772.0–4250.0), 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-botswana")
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	0.7%	2860.0 – 607154.0 (mean 263136.3566)
ident	object	0.0%	#meta +code, FBSK, BW-0063
type	object	0.0%	small_airport, closed, large_airport
name	object	0.0%	#loc +airport +name, Sir Seretse Khama International Airport, Makalamabedi Airport
latitude_deg	float64	0.7%	-26.8881 – -17.8317 (mean -20.8502)
longitude_deg	float64	0.7%	19.9997 – 29.1312 (mean 23.9378)
elevation_ft	float64	16.7%	1772.0 – 4250.0 (mean 3220.8)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	Botswana, #country +name
iso_country	object	0.0%	BW, #country +code +iso2
region_name	object	0.0%	North West District, Central District, Ghanzi District
iso_region	object	0.0%	BW-NW, BW-CE, BW-GH
local_region	object	0.0%	NW, CE, GH
municipality	object	10.4%	Nxabega, Gope, Lobatse
scheduled_service	float64	0.7%	0.0 – 1.0 (mean 0.035)
gps_code	object	45.1%
icao_code	object	79.2%
wikipedia_link	object	76.4%
keywords	object	52.8%
score	float64	0.7%	0.0 – 1000.0 (mean 88.1119)
last_updated	datetime64[ns, UTC]	0.7%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	2860.0	607154.0	263136.3566	317601.0
latitude_deg	-26.8881	-17.8317	-20.8502	-19.8602
longitude_deg	19.9997	29.1312	23.9378	23.4725
elevation_ft	1772.0	4250.0	3220.8	3167.5
scheduled_service	0.0	1.0	0.035	0.0
score	0.0	1000.0	88.1119	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. 3 column(s) with >80% missing values were removed: 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: gps_code, icao_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_botswana,
  title     = {Airports in Botswana},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-bwa},
  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.