If you like playing with data, chances are you’ve come across D3.js, Data-Driven Documents. Here are two of the first visualizations I made with it. I’m going to write up a more detailed blog post about it later. In the meantime – without commentary -just enjoy.
!! UPDATE: check out my post about Mapillary’s Rhythm !!
And the second one (without attempting to scale it to the screen):
How long does it take to map an area with Mapillary? Well, apparently it’s up to you. According to he video below, it shouldn’t take long. It shows how street level photo coverage evolved in Phoenix, AZ between June 2015 and April 2016. I’m always amazed to see what can be achieved in just less than a year.
Videos were generated using the Time manager plugin of QGIS and Libav.
I’ve recently found Mapillary which is a great project that aims to cover the world with street level photos, just like Google’s StreetView. The big difference is that they use the crowdsourcing approach and collect images from volunteers, mostly equipped with smartphones or action cameras. All photos are available under CC BY-SA 4.0. They process all uploaded photos using computer vision on their servers. They have a nice API so everything is given. They’re open, they’re geospatial and they’re nice. You can talk with them via Twitter or email. They’ll respond. Currently, you can find them in Malmö, Sweden and in West Hollywood, Los Angeles. The project pretty soon has gone worldwide. The service was initially released in the last week of February 2014 at the Launch Festival and since then they cover 101658370 meters with 3541820 uploaded photos until September 21. Check out their site and see what they are doing. From my sight, it’s pretty impressive. I’ve shot this panorama view in Key West, FL.