Flight Risk

BBC Future

At a glance

When are planes most likely to crash? And why? Our interactive visualization explores two decades of commercial plane crashes to reveal the patterns hidden in the data.

Interactive data visualization showing commercial passenger plane incidents since 1993

Taking a crash course

Flying is one of the safest ways to travel. Airplanes cause significantly fewer deaths per million people than sea, rail or road. But things do go wrong.

So what is the most common cause of air crashes? What is the emerging trend among the causes? And what phase of a flight is the most dangerous?

To explore the subject for BBC Future, we looked at every commercial plane crash from 1993 onwards and created an interactive visualization that revealed the patterns and connections hidden in the data.

Data visualization showing the deaths per mile from different transport methods

Gathering the evidence

We delved into the data from the last 20 years of commercial plane crashes. We researched everything from types of aircraft and location to cause of crash and even the certainty of cause.

Icons showing the stages of flight and causes of incidents.

Exploring the data

This is an exploratory visualization that rewards time spent – you can find your own stories by diving into the data sets.

Using the controls, people can filter, sort and rearrange the data by five different causes of incident (for example, ‘Human error’ or ‘Mechanic’) and six different phases of flight (from ‘Grounded’ to ‘Landing’).

Hover state of an incident, revealing additional information

Packing well

We needed to pack in a lot of on-screen information and overlapping bubbles make for an efficient use of space.

Clicking on a bubble reveals more background information for each incident such as the type of plane and the country the crash occurred.

Three screens showing the number of incidents at different stages of flight.

Taking it seriously

This is a serious subject, but we wanted to offset a sombre background with bold colours that made the experience feel inviting and contemporary. Similarly, we spent a lot of time adding smooth, organic physics to the movements of the bubbles to provide a pleasing user experience.

The stories emerge

When the visualization was complete, stories began to emerge from the data points. Some were expected, such as the two huge bubbles that appeared over the year 2001.

But intriguing patterns were also revealed. When we visualized causes of crash per year, the breakdown showed the colour yellow shrinking massively over the past two decades.

It highlighted the fact that human error is largely being removed by automation. Something that could have interesting implications for the fast-growing driverless car industry.

timeline showing the number of incidents, broken down by the cause.
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