Microsoft Flight Simulator’s Data Insanity Spawns Enormous Buildings And Anomalies From OpenStreetMap

The OpenStreetMap project is an excellent example of how powerful crowdsourced data can be, but that’s not to say the system is perfect. Invalid data, added intentionally or otherwise, can sometimes slip through the cracks and lead to some interesting problems. A fact that developers Asobo Studio are becoming keenly aware of as players explore their recently released Microsoft Flight Simulator 2020.

Like a Wiki, users can update OpenStreetMap and about a year ago, user nathanwright120 marked a 2 story building near Melbourne, Australia as having an incredible 212 floors (we think it’s this commit). The rest of his edits seem legitimate enough, so it’s a safe bet that it was simply a typo made in haste. The sort of thing that could happen to anyone. Not long after, thanks to the beauty of open source, another user picked up on the error and got it fixed up.

But not before some script written by Asobo Studio went through sucked up the OpenStreetMap data for Australia and implemented it into their virtual recreation of the planet. The result is that the hotly anticipated flight simulator now features a majestic structure in the Melbourne skyline that rises far above…everything.

The whole thing is great fun, and honestly, players probably wouldn’t even mind if it got left in as a Easter egg. It’s certainly providing them with some free publicity; in the video below you can see a player by the name of Conor O’Kane land his aircraft on the dizzying edifice, a feat which has earned him nearly 100,000 views in just a few days.

But it does have us thinking about filtering crowdsourced data. If you ask random people to, say, identify flying saucers in NASA footage, how do you filter that? You probably don’t want to take one person’s input as authoritative. What about 10 people? Or a hundred?

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The Heat Of The Moments – Location Visualization In Python

Have you ever taken a look at all the information that Google has collected about you over all these years? That is, of course, assuming you have a Google account, but that’s quite a given if you own an Android device and have privacy concerns overruled by convenience. And considering that GPS is a pretty standard smartphone feature nowadays, you shouldn’t be surprised that your entire location history is very likely part of the collected data as well. So unless you opted out from an everchanging settings labyrinth in the past, it’s too late now, that data exists — period. Well, we might as well use it for our own benefit then and visualize what we’ve got there.

Location data naturally screams for maps as visualization method, and [luka1199] thought what would be better than an interactive Geo Heatmap written in Python, showing all the hotspots of your life. Built around the Folium library, the script reads the JSON dump of your location history that you can request from Google’s Takeout service, and overlays the resulting heatmap on the OpenStreetMap world map, ready for you to explore in your browser. Being Python, that’s pretty much all there is, which makes [Luka]’s script also a good starting point to play around with Folium and map visualization yourself.

While simply just looking at the map and remembering the places your life has taken you to can be fun on its own, you might also realize some time optimization potential in alternative route plannings, or use it to turn your last road trip route into an art piece. Just, whatever you do, be careful that you don’t accidentally leak the location of some secret military facilities.

[via r/dataisbeautiful]

A Simple Runner’s GPS Logger

[Daniel] received a grant from the University of Minnesota’s ECE Envision Fund and was thus responsible for creating something. He built a runner’s GPS logger, complete with a screen that will show a runner the current distance travelled, the time taken to travel that distance, and nothing else. No start/stop, no pause, nothing. Think of it as a stripped-down GPS logger, a perfect example of a minimum viable product, and a great introduction to getting maps onto a screen with an ARM micro.

The build consists of an LPC1178 ARM Cortex M3 microcontroller, a display, GPS unit, and a battery with not much else stuffed into the CNC milled case. The maps come from OpenStreetMap and are stored on a microSD card. Most of the files are available on GitHub, and the files for the case design will be uploaded shortly.

The CNC machine [Daniel] used to create the enclosure is a work of art unto itself. We featured it last year, and it’s good enough to do PCBs with 10 mil traces. Excellent work, although with that ability, we’re wondering why the PCB for the Runner’s GPS is OSH Park purple.

Using A Raspberry Pi To Give Your Car More Features

[Andrei] is cruising in style thanks to his Raspi-powered CarPC project, which is a steal at $200 considering all the functionality it provides. This is an update to the work we saw from him back in March. Rather than completely replace his car’s head unit, [Andrei] simply relocated it to the trunk, permanently set it to the “aux input” source, and connected the Raspberry Pi’s audio output. The Pi runs a Raspbian Wheezy distro with XBMC and is mounted in the storage area beneath the middle armrest. [Andrei] filled the hole left by the old stereo with a 7-inch touchscreen display, which connects to the Pi through both HDMI and USB. If you throw the car into reverse, the Pi automatically selects the touchscreen’s AV input to display the car’s backup camera, then flips back when put in drive.

The unit also provides navigation via the open-source Navit software using OpenStreetMap data. An ST22 SkyTraq GPS receiver grabs coordinates and feeds them into the Raspi, which updates the on-screen map once per second. You’ll want to watch the video after the break (Audio Warning: Tupac) to see for yourself just how well the CarPC came together,

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Wireshark 1.2.0 Available


Everyone’s favorite packet sniffer has a new stable release. Wireshark 1.2.0 has a slew of new features. They’ve included a 64-bit Windows installer and improved their OSX support. A number of new protocols are recognized and filter selection autocompletes. One of the more interesting additions is the combined GeoIP and OpenStreetMap lookups. We’re excited about this new release as Wireshark has proven an indispensable tool in the past for figure out exactly what was going on on our network.

[via Lifehacker]