electricsheepafrica/africa-ourairports-rwa

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- aviation
- facilities-infrastructure
- geodata
- hxl
- transportation
- rwa
pretty_name: "Airports in Rwanda"
dataset_info:
splits:
- name: train
num_examples: 8
- name: test
num_examples: 2
---

# Airports in Rwanda

**Publisher:** OurAirports · **Source:** [HDX](https://data.humdata.org/dataset/ourairports-rwa) · **License:** `cc-by-igo` · **Updated:** 2026-01-30

---

## Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-01-30. Geographic scope: **RWA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 10 |
| **Columns** | 25 (7 numeric, 17 categorical, 0 datetime) |
| **Train split** | 8 rows |
| **Test split** | 2 rows |
| **Geographic scope** | RWA |
| **Publisher** | OurAirports |
| **HDX last updated** | 2026-01-30 |

---

## Variables

**Geographic** — `type` (small_airport, medium_airport, #loc +airport +type), `latitude_deg` (range -2.5958–-1.4969), `longitude_deg` (range 28.9079–30.383), `country_name` (Rwanda, #country +name), `iso_country` (RW, #country +code +iso2) and 5 others.

**Temporal** — `last_updated`.

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

**Identifier / Metadata** — `id` (range 3236.0–342091.0), `ident` (#meta +code, HRYR, HRYU), `name` (#loc +airport +name, Kigali International Airport, Ruhengeri Airport), `gps_code` (#loc +airport +code +gps, HRYR, HRYU), `icao_code` and 3 others.

**Other** — `elevation_ft` (range 4630.0–6102.0), `continent` (AF, #region +continent +code), `scheduled_service` (range 0.0–1.0), `home_link`, `wikipedia_link`.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ourairports-rwa")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | float64 | 10.0% | 3236.0 – 342091.0 (mean 88458.0) |
| `ident` | object | 0.0% | #meta +code, HRYR, HRYU |
| `type` | object | 0.0% | small_airport, medium_airport, #loc +airport +type |
| `name` | object | 0.0% | #loc +airport +name, Kigali International Airport, Ruhengeri Airport |
| `latitude_deg` | float64 | 10.0% | -2.5958 – -1.4969 (mean -2.0513) |
| `longitude_deg` | float64 | 10.0% | 28.9079 – 30.383 (mean 29.8476) |
| `elevation_ft` | float64 | 10.0% | 4630.0 – 6102.0 (mean 5127.4444) |
| `continent` | object | 0.0% | AF, #region +continent +code |
| `country_name` | object | 0.0% | Rwanda, #country +name |
| `iso_country` | object | 0.0% | RW, #country +code +iso2 |
| `region_name` | object | 0.0% | Eastern Province, Western Province, #adm1 +name |
| `iso_region` | object | 0.0% | RW-02, RW-04, #adm1 +code +iso |
| `local_region` | float64 | 10.0% | 1.0 – 5.0 (mean 2.7778) |
| `municipality` | object | 0.0% | #loc +municipality +name, Kigali, Ruhengeri |
| `scheduled_service` | float64 | 10.0% | 0.0 – 1.0 (mean 0.2222) |
| `gps_code` | object | 20.0% | #loc +airport +code +gps, HRYR, HRYU |
| `icao_code` | object | 30.0% | |
| `iata_code` | object | 40.0% | |
| `home_link` | object | 80.0% | |
| `wikipedia_link` | object | 20.0% | |
| `keywords` | object | 80.0% | |
| `score` | float64 | 10.0% | 0.0 – 1050.0 (mean 255.5556) |
| `last_updated` | datetime64[ns, UTC] | 10.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 3236.0 | 342091.0 | 88458.0 | 31612.0 |
| `latitude_deg` | -2.5958 | -1.4969 | -2.0513 | -2.1444 |
| `longitude_deg` | 28.9079 | 30.383 | 29.8476 | 30.1395 |
| `elevation_ft` | 4630.0 | 6102.0 | 5127.4444 | 4905.0 |
| `local_region` | 1.0 | 5.0 | 2.7778 | 2.0 |
| `scheduled_service` | 0.0 | 1.0 | 0.2222 | 0.0 |
| `score` | 0.0 | 1050.0 | 255.5556 | 50.0 |

---

## 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`. 1 column(s) with >80% missing values were removed: `local_code`. 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.

---

## 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: `icao_code`, `iata_code`, `home_link`, `keywords`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ourairports-rwa) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ourairports_rwa,
title = {Airports in Rwanda},
author = {OurAirports},
year = {2026},
url = {https://data.humdata.org/dataset/ourairports-rwa},
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.*