electricsheepafrica/africa-ourairports-gnq

---
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
- gnq
pretty_name: "Airports in Equatorial Guinea"
dataset_info:
splits:
- name: train
num_examples: 6
- name: test
num_examples: 1
---

# Airports in Equatorial Guinea

**Publisher:** OurAirports · **Source:** [HDX](https://data.humdata.org/dataset/ourairports-gnq) · **License:** `cc-by-igo` · **Updated:** 2026-01-10

---

## Abstract

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

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

*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)** | 8 |
| **Columns** | 24 (6 numeric, 17 categorical, 0 datetime) |
| **Train split** | 6 rows |
| **Test split** | 1 rows |
| **Geographic scope** | GNQ |
| **Publisher** | OurAirports |
| **HDX last updated** | 2026-01-10 |

---

## Variables

**Geographic** — `type` (large_airport, small_airport, #loc +airport +type), `latitude_deg` (range -1.4086–3.7553), `longitude_deg` (range 5.6242–11.3025), `country_name` (Equatorial Guinea, #country +name), `iso_country` (GQ, #country +code +iso2) and 5 others.

**Temporal** — `last_updated`.

**Outcome / Measurement** — `score` (range 50.0–1000.0).

**Identifier / Metadata** — `id` (range 2875.0–318498.0), `ident` (#meta +code, FGSL, FGBT), `name` (#loc +airport +name, Malabo International Airport, Bata International Airport), `gps_code`, `icao_code` and 3 others.

**Other** — `elevation_ft` (range 13.0–2165.0), `continent` (AF, #region +continent +code), `scheduled_service` (range 0.0–1.0), `wikipedia_link`.

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | float64 | 12.5% | 2875.0 – 318498.0 (mean 226744.0) |
| `ident` | object | 0.0% | #meta +code, FGSL, FGBT |
| `type` | object | 0.0% | large_airport, small_airport, #loc +airport +type |
| `name` | object | 0.0% | #loc +airport +name, Malabo International Airport, Bata International Airport |
| `latitude_deg` | float64 | 12.5% | -1.4086 – 3.7553 (mean 1.355) |
| `longitude_deg` | float64 | 12.5% | 5.6242 – 11.3025 (mean 9.5073) |
| `elevation_ft` | float64 | 12.5% | 13.0 – 2165.0 (mean 945.2857) |
| `continent` | object | 0.0% | AF, #region +continent +code |
| `country_name` | object | 0.0% | Equatorial Guinea, #country +name |
| `iso_country` | object | 0.0% | GQ, #country +code +iso2 |
| `region_name` | object | 0.0% | Litoral Province, Wele-Nzas Province, #adm1 +name |
| `iso_region` | object | 0.0% | GQ-LI, GQ-WN, #adm1 +code +iso |
| `local_region` | object | 0.0% | LI, WN, #adm1 +code +local |
| `municipality` | object | 0.0% | #loc +municipality +name, Malabo, Bata |
| `scheduled_service` | float64 | 12.5% | 0.0 – 1.0 (mean 0.5714) |
| `gps_code` | object | 12.5% | |
| `icao_code` | object | 50.0% | |
| `iata_code` | object | 12.5% | |
| `wikipedia_link` | object | 12.5% | |
| `keywords` | object | 62.5% | |
| `score` | float64 | 12.5% | 50.0 – 1000.0 (mean 578.5714) |
| `last_updated` | datetime64[ns, UTC] | 12.5% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 2875.0 | 318498.0 | 226744.0 | 313964.0 |
| `latitude_deg` | -1.4086 | 3.7553 | 1.355 | 1.6368 |
| `longitude_deg` | 5.6242 | 11.3025 | 9.5073 | 9.8057 |
| `elevation_ft` | 13.0 | 2165.0 | 945.2857 | 82.0 |
| `scheduled_service` | 0.0 | 1.0 | 0.5714 | 1.0 |
| `score` | 50.0 | 1000.0 | 578.5714 | 850.0 |

---

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

---

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

---

## Citation

```bibtex
@dataset{hdx_africa_ourairports_gnq,
title = {Airports in Equatorial Guinea},
author = {OurAirports},
year = {2026},
url = {https://data.humdata.org/dataset/ourairports-gnq},
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.*

annotations_creators:
- 无注释
language_creators:
- 采集所得
language:
- 英语
license: CC-BY-4.0
multilinguality:
- 单语言
size_categories:
- 样本数少于1000
source_datasets:
- 原创数据集
task_categories:
- 其他
task_ids: []
tags:
- 非洲
- 人道主义
- 人道主义数据交换（Humanitarian Data Exchange, HDX）
- Electric Sheep Africa
- 航空
- 设施与基础设施
- 地理数据
- 人道主义交换语言（Humanitarian Exchange Language, HXL）
- 交通
- 赤道几内亚（GNQ）
pretty_name: "赤道几内亚机场列表"
dataset_info:
splits:
- name: train
num_examples: 6
- name: test
num_examples: 1

---

## 赤道几内亚机场列表

**发布方**：OurAirports · **数据源**：[人道主义数据交换（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/ourairports-gnq) · **许可证**：`cc-by-igo` · **更新时间**：2026-01-10

---

## 数据集摘要
赤道几内亚境内机场列表，包含经纬度坐标。本数据为源自http://ourairports.com/countries/GQ/的未经验证的社区贡献数据。
本数据集每条记录对应一级行政区域的观测数据，最后于2026年1月10日在HDX平台更新。地理覆盖范围：**GNQ（赤道几内亚）**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式（Parquet）。*

---

## 数据集特征
| | |
|---|---|
| **领域** | 人道主义与发展数据 |
| **观测单元** | 一级行政区域观测数据 |
| **总记录数** | 8条 |
| **列数** | 24列（6列数值型，17列分类型，0列日期型） |
| **训练集划分** | 6条记录 |
| **测试集划分** | 1条记录 |
| **地理覆盖范围** | GNQ（赤道几内亚） |
| **发布方** | OurAirports |
| **HDX平台最后更新时间** | 2026-01-10 |

---

## 变量说明
**地理类变量**：`type`（机场类型，可选值：大型机场、小型机场，格式标记：`#loc +airport +type`）、`latitude_deg`（纬度度数，取值范围：-1.4086~3.7553）、`longitude_deg`（经度度数，取值范围：5.6242~11.3025）、`country_name`（国家名称，固定为赤道几内亚，格式标记：`#country +name`）、`iso_country`（国家ISO2代码，固定为GQ，格式标记：`#country +code +iso2`）及其他5个变量。
**时间类变量**：`last_updated`（最后更新时间）。
**结果/测量类变量**：`score`（评分，取值范围：50.0~1000.0）。
**标识符/元数据类变量**：`id`（记录ID，取值范围：2875.0~318498.0）、`ident`（数据集代码，格式标记：`#meta +code`，示例值：FGSL、FGBT）、`name`（机场名称，格式标记：`#loc +airport +name`，示例值：马拉博国际机场、巴塔国际机场）、`gps_code`（全球定位系统代码，GPS）、`icao_code`（国际民用航空组织代码，ICAO）及其他3个变量。
**其他变量**：`elevation_ft`（海拔高度，单位：英尺，取值范围：13.0~2165.0）、`continent`（大洲代码，固定为AF，格式标记：`#region +continent +code`）、`scheduled_service`（定期航班服务标识，取值范围：0.0~1.0）、`wikipedia_link`（维基百科链接）。

---

## 快速上手
python
from datasets import load_dataset

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

print(train.shape)
train.head()

---

## 数据Schema
| 列名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `id` | float64 | 12.5% | 2875.0 – 318498.0（均值：226744.0） |
| `ident` | object | 0.0% | `#meta +code`，示例值：FGSL、FGBT |
| `type` | object | 0.0% | 大型机场、小型机场，格式标记：`#loc +airport +type` |
| `name` | object | 0.0% | `#loc +airport +name`，示例值：马拉博国际机场、巴塔国际机场 |
| `latitude_deg` | float64 | 12.5% | -1.4086 – 3.7553（均值：1.355） |
| `longitude_deg` | float64 | 12.5% | 5.6242 – 11.3025（均值：9.5073） |
| `elevation_ft` | float64 | 12.5% | 13.0 – 2165.0（均值：945.2857） |
| `continent` | object | 0.0% | AF，格式标记：`#region +continent +code` |
| `country_name` | object | 0.0% | 赤道几内亚，格式标记：`#country +name` |
| `iso_country` | object | 0.0% | GQ，格式标记：`#country +code +iso2` |
| `region_name` | object | 0.0% | 滨海省、韦莱-恩萨斯省，格式标记：`#adm1 +name` |
| `iso_region` | object | 0.0% | GQ-LI、GQ-WN，格式标记：`#adm1 +code +iso` |
| `local_region` | object | 0.0% | LI、WN，格式标记：`#adm1 +code +local` |
| `municipality` | object | 0.0% | `#loc +municipality +name`，示例值：马拉博、巴塔 |
| `scheduled_service` | float64 | 12.5% | 0.0 – 1.0（均值：0.5714） |
| `gps_code` | object | 12.5% | 无有效取值 |
| `icao_code` | object | 50.0% | 无有效取值 |
| `iata_code` | object | 12.5% | 无有效取值 |
| `wikipedia_link` | object | 12.5% | 无有效取值 |
| `keywords` | object | 62.5% | 无有效取值 |
| `score` | float64 | 12.5% | 50.0 – 1000.0（均值：578.5714） |
| `last_updated` | datetime64[ns, UTC] | 12.5% | 无有效取值 |
| `esa_source` | object | 0.0% | 无有效取值 |
| `esa_processed` | object | 0.0% | 无有效取值 |

---

## 数值型变量统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `id` | 2875.0 | 318498.0 | 226744.0 | 313964.0 |
| `latitude_deg` | -1.4086 | 3.7553 | 1.355 | 1.6368 |
| `longitude_deg` | 5.6242 | 11.3025 | 9.5073 | 9.8057 |
| `elevation_ft` | 13.0 | 2165.0 | 945.2857 | 82.0 |
| `scheduled_service` | 0.0 | 1.0 | 0.5714 | 1.0 |
| `score` | 50.0 | 1000.0 | 578.5714 | 850.0 |

---

## 数据整理流程
原始数据通过CKAN应用程序编程接口（CKAN API）从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并标准化为蛇形命名法（snake_case）。通用缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。移除了2个缺失率超过80%的列：`local_code`与`home_link`。基于解析成功率阈值（>85%），将7个列从字符串类型转换为数值型或日期型。本数据集以固定随机种子（42）按80/20比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式。

---

## 数据局限性
- 数据源自OurAirports，未经过Electric Sheep Africa的独立验证。
- 自动化清洗无法修正原始数据集中的错报值、定义不一致或采样偏差问题。
- 以下列的缺失率超过20%，在建模时需谨慎使用：`icao_code`、`keywords`。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/ourairports-gnq)查看发布方的方法论说明与注意事项。

---

## 引用格式
bibtex
@dataset{hdx_africa_ourairports_gnq,
title = {Airports in Equatorial Guinea},
author = {OurAirports},
year = {2026},
url = {https://data.humdata.org/dataset/ourairports-gnq},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*