AirportMarkersSQR

Mapping Global Airports With Google Maps API

Digital mapping is an integral part of modern web design; Google Maps embeds are a part of millions of sites and consistently expanding. The Google Maps API allows for a customizable framework for visualizing human behavior patterns, this post shows where airports occur across the world with the greatest density.

Fortunately, large datasets can be implemented into the Google Maps JavaScript API! Using a mySQL database, a Google Maps embed can display huge amounts of information, please see below for an example with Global Airports as a metric.

Here’s a mapping project using a mySQL database to connect to a large dataset consisting of 8000+ global airports. There’s a few way this dataset could be mapped; however, using a database is the most efficient!

Check out the results:

I was surprised to see just how many airports there were; they tend to be concentrated near metropolitan area. As you can see Northern/Eastern Russia & the African Sahara are the least dense land areas on the map. There’s a lot of airports on small islands as well, its interesting to see how many opportunities there are for pilots to navigate to!

There’s a lot of passenger & freight air traffic daily, better understanding these seasonal behavior patterns would be useful for optimizing air traffic flow. For example, shipping companies use this information to streamline product orders & supply chains. Check out UPS’s ‘Longitudes’ blog here, I recommend this fascinating post about growth trends in aerospace logistics.

Quickly Load Large Datasets

I chose the Google Maps API for this mapping project over other digital mapping software like CartoDB, simply because of the large size of the dataset. I couldn’t cleanly edit a large dataset using Notepad++ or delete columns in an Excel spreadsheet without messing up the formatting of the dataset. The Google Maps API PHP/mySQL features were the most efficient way to load the CSV of global airports from OpenFlights.org.

Here’s a brief comparison of the world’s global cities.

Check out the images of Paris, France airports at different magnifications:

You can also check out images of Hong Kong airports at different magnifications:

And Buenos Aires, Argentina as well:

These different magnifications help show how dense population is around outlying metropolitan area or could also be a metric for showing fewer airports near less developed cities. In any case, more people would tend to create more demand for airports!

I stripped down some of the dataset to include just a few columns; the full CSV file dataset from OpenFlights can be found here,  with more comprehensive information about global airports.

If you have any questions or comments, please send an email: will[at]mindbodymetrics.com

-Will

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