planecrashdata / README.md
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Add PlaneCrashData global aviation dataset (Parquet, CSV, JSONL) with comprehensive metadata and documentation
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---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: aircraft
dtype: string
- name: operator
dtype: string
- name: registration
dtype: string
- name: flight_phase
dtype: string
- name: flight_type
dtype: string
- name: survivors
dtype: int64
- name: crash_site
dtype: string
- name: schedule
dtype: string
- name: msn
dtype: string
- name: yom
dtype: int64
- name: crash_location
dtype: string
- name: country
dtype: string
- name: region
dtype: string
- name: crew_on_board
dtype: int64
- name: crew_fatalities
dtype: int64
- name: passengers_on_board
dtype: int64
- name: passengers_fatalities
dtype: int64
- name: other_fatalities
dtype: int64
- name: total_fatalities
dtype: int64
- name: circumstances
dtype: string
- name: crash_cause
dtype: string
- name: timestamp
dtype: string
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: image_url
dtype: string
- name: hex_code
dtype: string
- name: external_id
dtype: string
splits:
- name: train
num_bytes: 10260424
num_examples: 27598
download_size: 10260424
dataset_size: 10260424
pretty_name: PlaneCrashData - Global Aviation Safety & Accident Database (1918-Present)
license: cc-by-4.0
tags:
- aviation
- transportation
- flight-safety
- plane-crashes
- aircraft-accidents
- aviation-history
- geospatial
- time-series
- open-data
task_categories:
- tabular-classification
- tabular-regression
- text-classification
- time-series-forecasting
size_categories:
- 10K<n<100K
language:
- en
---
# PlaneCrashData — Global Aviation Safety & Accident Database (1918–Present)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset Viewer](https://img.shields.io/badge/HF%20Viewer-Enabled-blue)](https://huggingface.co/datasets/MauroPello/planecrashdata)
[![Live Platform](https://img.shields.io/badge/Live%20Platform-PlaneCrashData.com-orange)](https://www.planecrashdata.com)
[![GitHub](https://img.shields.io/badge/GitHub-MauroPello%2Fflight--crashes-black)](https://github.com/MauroPello/flight-crashes)
**PlaneCrashData** is a comprehensive, open historical dataset cataloging global aviation accidents and incidents covering over a century of civil and military aviation from **1918 to the present day**.
The dataset aggregates **27,598 verified aviation accident records** with rich structural metadata, including aircraft technical specifications, operator/airline records, flight phases, full textual circumstance narratives, causal categorizations, survival and fatality statistics, and geospatial coordinates.
Accompanying interactive web visualization: [www.planecrashdata.com](https://www.planecrashdata.com).
---
## Key Highlights & Dataset Statistics
- **Total Recorded Incidents**: `27,598`
- **Temporal Coverage**: `May 1918 – August 2026` (over 108 years)
- **Unique Aircraft Types/Models**: `1,187`
- **Unique Airlines & Operators**: `9,002`
- **Countries & Territories**: `226`
- **Total Fatalities Recorded**: `151,390`
- **Geospatially Geocoded Records**: Coordinates (`latitude`, `longitude`) for geospatial GIS analysis
---
## Dataset Schema & Fields
| Field Name | Type | Description | Example |
| :--- | :--- | :--- | :--- |
| `id` | `int64` | Unique incident identifier | `1` |
| `aircraft` | `string` | Aircraft model / type | `Boeing 737-800`, `Douglas DC-3` |
| `operator` | `string` | Airline or operating organization | `Pan American World Airways`, `USAF` |
| `registration` | `string` | Aircraft registration / tail number | `N737AA`, `G-ABCD` |
| `flight_phase` | `string` | Phase of flight when incident occurred | `Takeoff (climb)`, `Cruise`, `Approach`, `Landing` |
| `flight_type` | `string` | Category of flight operation | `Scheduled Revenue Flight`, `Cargo`, `Military`, `Test` |
| `survivors` | `int64` | Number of verified survivors (nullable) | `15` |
| `crash_site` | `string` | Physical surroundings / environment | `Airport (less than 10 km from airport)`, `Mountains`, `Sea` |
| `schedule` | `string` | Origin and destination route | `New York - London`, `Paris - Rome` |
| `msn` | `string` | Manufacturer Serial Number (MSN / c/n) | `24680` |
| `yom` | `int64` | Year of aircraft manufacture | `1998` |
| `crash_location` | `string` | Detailed geographical location description | `Tenerife-Los Rodeos Airport Canary Islands` |
| `country` | `string` | Country of incident | `United States of America`, `Spain` |
| `region` | `string` | Geographical region / continent | `North America`, `Europe`, `Asia` |
| `crew_on_board` | `int64` | Number of crew members on board | `8` |
| `crew_fatalities` | `int64` | Crew fatalities count | `0` |
| `passengers_on_board` | `int64` | Number of passengers on board | `150` |
| `passengers_fatalities` | `int64` | Passenger fatalities count | `0` |
| `other_fatalities` | `int64` | Ground / non-occupant fatalities count | `0` |
| `total_fatalities` | `int64` | Total fatalities (crew + passengers + ground) | `0` |
| `circumstances` | `string` | Detailed narrative description of the event | *Narrative text...* |
| `crash_cause` | `string` | Categorized primary cause | `Technical failure`, `Pilot error`, `Weather` |
| `timestamp` | `string` | UTC timestamp in ISO-8601 format | `1977-03-27 17:06:50+00:00` |
| `latitude` | `float64` | Latitude coordinate in decimal degrees | `28.4827` |
| `longitude` | `float64` | Longitude coordinate in decimal degrees | `-16.3415` |
| `image_url` | `string` | Representative or historical aircraft image URL | `https://...` |
| `hex_code` | `string` | ICAO 24-bit Mode S transponder hex code | `4005B5` |
| `external_id` | `string` | External database reference ID (e.g. ASN) | `19770327-0` |
---
## Quickstart & Usage
### Using Hugging Face `datasets` (Python)
```python
from datasets import load_dataset
# Load dataset
dataset = load_dataset("MauroPello/planecrashdata")
# Inspect dataset
print(dataset["train"][0])
```
### Using `pandas` or `polars` directly with Parquet
```python
import pandas as pd
# Load Parquet directly from Hugging Face Hub
url = "hf://datasets/MauroPello/planecrashdata/data/train-00000-of-00001.parquet"
df = pd.read_parquet(url)
print(f"Loaded {len(df):,} incidents spanning {df['yom'].min()} to {df['yom'].max()}")
```
```python
import polars as pl
# Fast Polars scan
df = pl.read_parquet("hf://datasets/MauroPello/planecrashdata/data/train-00000-of-00001.parquet")
print(df.group_by("region").agg(pl.col("total_fatalities").sum()))
```
### Using `DuckDB` (SQL)
```sql
INSTALL httpfs;
LOAD httpfs;
SELECT
country,
COUNT(*) AS total_crashes,
SUM(total_fatalities) AS total_fatalities
FROM 'https://huggingface.co/datasets/MauroPello/planecrashdata/resolve/main/data/train-00000-of-00001.parquet'
GROUP BY country
ORDER BY total_crashes DESC
LIMIT 10;
```
---
## Applications & Use Cases
1. **Aviation Safety Analysis**: Long-term safety trend evaluations, flight phase vulnerability analysis, and accident rate comparisons across decades.
2. **Predictive Modeling & NLP**: Cause classification and risk factor extraction from historical accident narrative circumstances.
3. **Geospatial & Temporal GIS**: Mapping aviation incident hotspots, terrain correlation, and regional safety improvements.
4. **Historical & Academic Research**: Cross-referencing fleet evolution, aircraft manufacturer safety records, and regulatory milestones.
---
## License
This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
---
## Citation
```bibtex
@misc{planecrashdata2026,
author = {Mauro Pellonara},
title = {PlaneCrashData: Global Aviation Safety and Accident Database (1918-Present)},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/MauroPello/planecrashdata}}
}
```