Shapefile Top30 airports 2015 with passenger numbers 2013-2015 as an attribute – Downloads
After importing geodata from the GIS to MongoDB and creating a spatial index (part 1), the exciting (spatial) adventure starts. With „normal“ (relational) databases and their spatial extensions (Oracle spatial, PostgreSQL/PostGIS, SQLite/Spatialite,…) a lot of spatial queries and geoprocessing are possible. So let’s try to find out which adresses have to be evacuated 250m around some „event“…
MongoDB (3.2) is a kind of database-hipster at the moment – with improving support for spatial data. So it was time for me to discover some of it’s features concerning spatial data. As a GIS-user my first intention was to get some bigger simple (point) geodata into MongoDB. Part 1 covers this topic, part 2 will cover some spatial operations within MongoDB. I also want to do some performance checks between PostgreSQL/PostGIS and MongoDB related to geodata.
The Raspberry Pi Zero is still very hard to get – it seems to be constantly out of stock.
So I made Raspberry Pi powered* Raspberry Pi Zero Checker: It scans Adafruit, Pimoroni and The Pi Hut for strings indicating stock and warns you with an annoying ‚alarm‘ sound. I also have a PHP version for my web server that also works very well.
*Note: any computer will do – but a Pi can be left on all the time without annoying fan noise or high power bill.
This is a very simple project both soft- and hardwarewise: Get your Pi online, automatically start the script on boot (rc.local or systemd) and plug in some speakers.
If you do not want to get woken up in the middle of the night, you might want to add some code to not trigger the alarm after 22.00h for example.