Finding illegal airstrips in Brazil

Using a combination of satellite imagery, crowdsourced databases, and analyses, The New York Times identified airstrips used for illegal mining in Brazil:

To confirm these locations and connect them with illicit mining, Times reporters built a tool to help analyze thousands of satellite images. They examined historical satellite imagery to determine that 1,269 unregistered airstrips still appeared in active use within the past year. They documented telltale signs of mining nearby, such as clear cut areas of rainforest and pools that miners use to separate dirt from ore. And they determined that hundreds of the airstrips in mining areas are within Indigenous and protected lands, where any form of mining is against the law.

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Shifting flight paths for wealthy Russians

For The New York Times, Pablo Robles, Anton Troianovski, and Agnes Chang mapped the change in destinations for Russian private jets, before and after sanctions. Before, it was more about Paris, Milan, and Geneva. After, Dubai became a top destination.

I like the charts after the map. A slope chart with a white fill provides contrast and a flight departures board gives a little something extra.

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Empty Ukrainian airspace

As you would imagine, Ukrainian airspace looks empty right now. Reuters mapped flights before the Russian invasion, the day of, and after the European Union airspace ban. The above shows private, commercial, and cargo flights. In separate maps, Reuters also reported unidentified flights, along with detours, cancelations, and the general disruption of international airspace.

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Rerouted flights to avoid Russian airspace

Many countries have banned Russian aircraft from entering their airspace. Russian in turn has banned other countries. For Bloomberg, Mira Rojanasakul and Jin Wu mapped current bans and showed how flights have had to reroute.

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When the world shut down, seen through global flights

Lauren Tierney and William Neff for The Washington Post used a rotating globe to show how connections between countries quickly shut down as the coronavirus spread.

I’m looking forward to when we get to watch the map in reverse.

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Canceled flights due to coronavirus

With an animated side-by-side map, The New York Times shows canceled flights in efforts to slow down the spread of the coronavirus. The left map represents 12,814 flights within China on January 23. The right map shows 1,662 on February 13. Keep scrolling to see changes for flights leaving China to other countries.

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Mapping the cheapest flights to everywhere, given your location

Sometimes you really do need to get away. Escape, part search engine and part research project from students at the MIT Senseable City Laboratory in Singapore, shows you the cheapest flights out of any given city. Just put in a location, and you get color-coded connections to everywhere around the world.

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Falling ticket prices for longer flights

Based on data from Expedia, this is an interesting one from The Economist. Using polar coordinates, they used angle to represent percentage change in ticket prices and the radius to represent the distance of a flight.

Too bad they couldn’t get more data from Expedia. I would’ve liked to see the price changes for more flights, especially shorter ones to use as a point of comparison.

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Aviation tracker with depth

I’ve grown bored of maps that show commuter traffic, but for whatever reason, air traffic maps continue to seem interesting. Add this fun experiment by Jacob Wasilkowski to the list. Like any other tracker, the aviation tracker shows where planes are at any given moment, but there’s one small twist. The plane icons are sized by elevation. So if you’re staring down from above, planes that are closer to you appear larger, and those closer to the ground appear smaller.

By the way, the data comes from ADS-B Exchange if you’re interested.


Posted by in flights, maps



Machine learning to find spy planes

Last year, BuzzFeed News went looking for surveillance flight paths from the FBI and Homeland Security. Peter Aldhous describes how they did it. They used machine learning — a random forest algorithm to be more specific — to find the spy planes, which as you might expect tended to circle around more than normal flights.

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