Digital Geography

ArcGIS REST API and QGIS: a practical example

The ArcGIS REST API provides some interesting endpoints which can be used for free with a developer account. But how to do this in QGIS as you might not have a licensed ArcGIS Desktop license at hand: A short example using isochrones or “service areas” as Esri calls them.

Geocoding with Microsoft’s Azure Maps

API’s are getting more and more important as some (maybe the majority?) of GIS users don’t want to handle large datasets, don’t want to care about addresses and geo-coordinates, don’t want to create an own routing algorithm… As most of you might use Google, OSM or HERE for geocoding purposes I would like to introduce Azure Maps for this as well.

Vessel tracking the python way

Let’s assume you like cruise ships, tanker, ferries or you’re so fortunate and own a fleet of vessels cruising over the oceans. But where the heck are the ones you’re interested in. First you can visit MarineTraffic and search for the Vessels you’re interested in. But what if you want to keep track of those vessels or if you want to put them on your “own” map. Now Python comes in handy and I’ll show you how to gather coordinates and put them on a map using the ArcGIS API for Python.

OpenRouteService API: A Leaflet example for Isochrones

The page OpenRouteService.org is a very easy to use website which provides routing from A to B via C. It also allows to choose between different routing types for trucks, pedestrians or bicycles and isochrone analyses based on time and distance. In this article I would like to show you, how to embed the OpenRouteSevrice API into your very own Leaflet based webmap.

How to start with “VANE language” API – MODIS example

If you are reading this post – you might know something about satellite imagery. This is a valuable source to power quite a lot of analytics and monitoring applications. In this post I’d like to give you an idea of how all this Big Data stuff can be obtained and processed online, using the single API called #VANE language. What is VANE? The VANE geospatial platform, that’s coming out of the Beta now, is a new project we started at Openweathermap, relying on our expertise in providing well-designed APIs for weather data which is widely used by devs community. The…

ERDDAP: Diving into a Data Ocean

Data Lakes has become a popular term in the Big Data community. It’s used to refer to a large storage repository and processing engine. However there is now a technology from NOAA (National Oceanic and Atmospheric Administration of the USA) that turns its existing distributed data network of Petabytes of Open Data into what can be described as a Data Ocean! This technology is called ERDDAP and it provides fixed entry points on the Internet from which data can be searched for, queried and transformed. This functionality is made available via a human interface (web site) and Restful Web Services.

ArcGIS Open Data

For a year, ESRI’s open data initiative is online. As I’ve heard about it for the first time I was just thinking: nice move to get people into their ArcGIS online environment. But I have to admit: I was not looking and exploring their platform. Now I stumbled upon a recent post on reddit and there it was again: opendata.arcgis.com has 25,000+ open datasets all accessible by a common API. Full disclosure: I work on the team that builds the product. So let’s have a look at it…

reproject and filetype change in python/pyqgis for QGIS plugin

In my current work on the qgis2leaf plugin I had the idea to place raster data on a leaflet map as an image overlay. With this in mind and looking at a webmap I needed to consider a good filesize, a strict projection of EPSG:4326 and a strict filetype as well. So decision was: projecting everythin to EPSG:4326 and changing file type to *.jpg. I know, how to do this in the Terminal and in QGIS. But what options do you have using python/ pyqgis only? Terminal For doing this work in the terminal/shell/command line the one and only choice…

How many people live in this area?

Not long ago I was tasked with finding out how many people live within an arbitrary polygon. In this particular case, the polygon represented the portion of the United States within a drive-time of 10 hours. For this example, the polygon(s) can be anything you wish. This post will act as a tutorial of sorts on how to answer questions like these using python. Sorry to my Deutsch Freunden on this site, but this will be a U.S based answer as using the Census API is a key part of it. This is a classic case of the modifiable area unit problem.…