Visualization of Air Traffic in World's Busiest Airports


The visualization above represents 24 hours of flights arriving to and departing from Dallas/Fort Worth International Airport. Each tiny line represents a single flight and the circle shape is given by the fact that only planes within a 200km radius from the airport have been considered. In the graphic on the left, the saturation of the color is proportional to the altitude of the planes: white means low altitude, while blue means high altitude. The graphic on the right has been colored using the same concept (white = low altitude, color = high altitude), but this time the color of each line is green for arriving flights and red for departing flights.

I have collected 24 hours of data for flights arriving and departing from the 10 world's busiest airports and produced 2 visualizations for each airport. The result can be seen below.

To build this visualization, I had to go through several steps:

First I have downloaded flight data at regular intervals using Python for 24 hours. I have downloaded just data related to flights that were in a 200km radius from one of the airports in my list.

The coordinates (latitude and longitude) of each point have been translated at data collection time to x and y coordinates using the Mercator projection; I decided to use this projection since most of the other projections (e.g. Equirectangular) I tried weren't able to preserve the shape of the circles.

After this process, I had a bunch of lists (flights) containing lists of x and y coordinates (positions of the plane in a given snapshot) that needed to be connected. To do so, I used Processing, since it's very easy to use, it allows a lot of customization, and has a built-in implementation of Catmull-Rom splines (a mathematical method to draw a curve through points). After obtaining the curves representing the flights, I have changed the color and saturation of the lines using the HSB color space. This allowed me to define a color by setting its hue value (blue for the visualization on the left, green for arriving flights and red for departing flights in the visualization of the right), setting maximum brightness (so that lines are clearly visible on the black background), and setting the saturation value depending on the altitude (low saturation, white, for low altitude and high saturation, full colored, for high altitude).

It's interesting to notice how each airport has a different pattern and how arriving and departing flights take different routes. In some airports, a few green circles are present: they are formed by arriving planes which are waiting their turn to land.


  1. Very cool. I wonder if you could apply this technique to solve the problem of looking for a flying practice area with the least amount of air traffic under 10k feet. You'd probably want to process at least a month worth of data.

  2. The patterns should be somewhat consistent as these airports use Standard Instrument Departures (SIDs) and Standard Terminal Arrival Routes (STARs). Air Traffic Control assigns a SID or STAR to manage traffic flow around these busy airports.