Most people in software development are familiar with time-aspect of data. Data can have a certain time, like events or a certain period. By now there are good tools and libraries to handle complex issues, such as different time-zones and DST (Daylight Saving Time). And developers now know how to use those libraries and tools.
Data can have geographic aspect too , besides WHEN we also have WHERE. In the last half year I had the pleasure to work with some experts on geographic data aka GIS. What I picked up (no particular order)
Not just GPS
Geographical coordinates are not just GPS coordinates. There are a lot of other coordinate systems and geographical systems. The X and Y coordinates usually refer to WGS84, most commonly recognizable in Google Maps or OpenStreetMap, but there are a lot of other coordinate reference systems too.
GIS can be complex, but you don’t and shouldn’t have to deal with all the details. For example, of course the earth is not flat, it’s not even a perfect sphere.
There’s some pretty complicated math needed to be able to view a set of geographic data on a screen, or calculate the distance between multiple points or decide if two areas overlap.
Fortunately there’s no need to figure out that math (although a bit of understanding helps), as there’s excellent software to work with geographic data.
Just as dealing with timezone data, or even more so with cryptographic systems – don’t roll out your own solution but use excellent software that’s out there to handle GIS.
So when working with GIS, take some time to investigate existing libraries and tools. I list some in this article, but don’t let this limit you.
GDAL
Popular tools and libraries Postgres (via PostGIS), DuckDB (via Spatial Extension), Clickhouse and Pandas (GeoPandas) can handle geographic data.
Most of software-products use GDAL directly or indirectly. That library is written in C++ but has bindings for most common languages such as Java, Python and JavaScript. Even if you’ve never heard GDAL, you probably use that library if you deal with geographical data.
As a result, if you learned to use PostGIS, using geospatial queries in DuckDB look very familiar.
QGIS
The go-to tool for geospatial analyses is QGIS. QGIS is like DBeaver or DataGrip is for SQL what SoapUI once was for webservices.
QGIS is a wonderful open source tool to open, view and handle any kind of georelated data, whether you want to open an ancient shape file, a geopackage, or connect to a webservice. QGIS allows you to edit most of these files too, and even draw on map.
If you work with geographic data or want to work, you should definitely get familiar with QGIS. The tool might be a bit overwhelming at start, but there’s a welcoming communities around QGIS to help you. If you’re in The Netherlands I’d recommend QGIS Gebruikers Nederland.
Rijksdriehoeksystem
Rijksdriehoeksystem is used commonly in The Netherlands. RD or Rijksdriehoeksystem gives you the relative position to the Onze Lievevrouwen Toren in Amersfoort. When dealing with Dutch Geographic data, usually RD is used.
Why use a different coordinate system you might ask? The WGS84 is designed to give you the exact/absolute coordinate somewhere on Earth. It doesn’t take into account continental drift or changes in landscape. The RD system of the Netherlands is relative, so even while the Eurasian content drifts, the coordinates remain the same.
On the other hand, RD sort of assumes the earth is flat, and for The Netherlands that assumption is quite correct. But you can’t use RD outside The Netherlands.
Public data
There are many cool geographic data sets and services available for download or use. We probably all know Openstreetmaps. But there are a *lot* of readily available geo-data out there. Sometimes as a service, and sometimes downloadable as file. The Dutch Government publishes a lot of open datasets which you can find PDOK. Or here’s a portal for the UK Government. Of course the EU publishes a lot data too. There’s a lot of data out there, freely to use. Some you can download, some you can use as a service, and easier than you might think.
All in all, the working with spatial data is more excited and accessible than I thought. This post is in no means meant to be a complete overview. I could (and should) write multiple articles on subjects of each paragraph.
Do you need help with your geographic data, I’m available for hire. Contact me!