The airport compass

3 years ago, I got inspired by a very interesting figure published on a paper by Geoff Boeing called “Urban spatial order: street network orientation, configuration, and entropy”

This figure represents the distribution of the orientation of every street of major cities.

I like how it gives a quick representation of each city’s layout and how evident are the various levels of entropy. You can easily recognise the cities which have a complex system of streets and those who have a simpler grid layout.

Shortly after seeing this chart, I immediately thought of replicating this view for airports’ traffic worldwide.

Sources and code

For this project I used two major open-source datasets, so that anyone who wants, can replicate it freely. I made the source-code available on my GitHub here.

Instead of using streets of cities, I needed to use routes of airports.

• The airlines routes data comes from @jpatokal’s OpenFlights repository
• The airports data is from @davidmegginson’s OurAirports repository

The latter is community-driven dataset and is updated on a frequent basis. The airline routes dataset, however, is 10 years old! If you would want to see a version using the latest aviation data from WINGX, please let me know!

I first generated the top 15 airports by number of destinations. Overall, I am quite happy with how the image looks. From North to South, East to West, it is possible to quickly find out what type of traffic each airport is having (in terms of departures). I have also generated a larger poster, containing the top 30 airports, which can be found at the end of this post.

Each chart (or compass) displays the distribution of outbound flights and aligns those for each inital bearing. The longer the yellow bar, the more frequent is that bearing. For example, if an airport has most of the departures pointing West (or 270°), you would see the longest bar pointing left.

A closer look

Some airports like New York JFK and London Heathrow (LHR) represent an important hub for transatlantic flights. In these charts, we can see how LHR has a long bar towards West (towards the North America) and JFK has one towards East (Europe).

Other airports’ compasses, instead, are majorly driven by the geographical constrains of their location. Tokyo’s Narita Intl. Airport is a very good example to illustrate this case. It has virtually no routes pointing eastwards! This is because Japan has the Pacific Ocean to its east side and most of the routes connecting Japan to North America are flying with an initial bearing pointing North! (Since this would be the shortest great circle path)

By looking at other airports, such as Munich, we can notice how their central hub function is also represented in the plot.

What’s next?

I first developed this 3 years ago and I re-adapted my old code for this post. There are some changes and improvements that can be made. For example:

• The data is grouped by each unique bearing, which means that if a route is at a 269 degree angle, it will create a separate bar to those routes with a 270 degree angle. This can be fixed my simply rounding the angles, or, even better, distributing the angles into different buckets (e.g 130-140 degrees, 140-150, etc.)
• The image is a static outlook at airlines’ routes of 2014. Many things can be done to make it more dynamic. For example, a seasonal outlook could be made so that the differences in the “orientation” in certain airports can be more noticeable over time.

But for now, I would love to gather some feedback and comments so that I can then make a second version of this! In my new blog post I go in details on how to replicate these charts: lucianosrp.github.io/posts/airport-compass