stevefaeembra
visualising speed of light on a terrestrial scale [video]
QGIS 2.18.3, Time Manager Plugin, ffmpeg.
Python code to create geodesic buffers around New York using the pyproj library. Used the "accumulate features" option in Time Manager. Used stand-alone ffmpeg as i'm used to it, although i see TM now supports it (and animated gifs with imagemagick)
also used the ogr2ogr -wrapdateline option to avoid the "fall off the end of the world" antimeridian artifacts.
Each black ring represents the ~300 km light travels in 1 millisecond, red rings at 5 ms intervals. Speed is 5 frames/sec, so slowed down 200x from real-time. Takes just under 67 ms to get to antipodean point.
In reality, of thinking about things like ping times to servers, refraction inside the fibre optics slows things somewhat (up to 30-odd percent). And the cables don't necessarily follow great circles (shortest paths).
Aim of this was more to bring the scale down to something I could visualize (as a coder, ms seem a natural unit)
visualising speed of light on a terrestrial scale [video]
QGIS 2.18.3, Time Manager Plugin, ffmpeg.
Python code to create geodesic buffers around New York using the pyproj library. Used the "accumulate features" option in Time Manager. Used stand-alone ffmpeg as i'm used to it, although i see TM now supports it (and animated gifs with imagemagick)
also used the ogr2ogr -wrapdateline option to avoid the "fall off the end of the world" antimeridian artifacts.
Each black ring represents the ~300 km light travels in 1 millisecond, red rings at 5 ms intervals. Speed is 5 frames/sec, so slowed down 200x from real-time. Takes just under 67 ms to get to antipodean point.
In reality, of thinking about things like ping times to servers, refraction inside the fibre optics slows things somewhat (up to 30-odd percent). And the cables don't necessarily follow great circles (shortest paths).
Aim of this was more to bring the scale down to something I could visualize (as a coder, ms seem a natural unit)