A few weeks ago, the city of Vienna released a great dataset: the so-called “Flächen-Mehrzweckkarte” (FMZK) is a polygon vector layer with an amazing level of detail which contains roads, buildings, sidewalk, parking lots and much more detail:

preview of the Flächen-Mehrzweckkarte

preview of the Flächen-Mehrzweckkarte

Now, of course we can use this dataset to create gorgeous maps but wouldn’t it be great to use it for analysis? One thing that has been bugging me for a while is routing for pedestrians and how it’s still pretty bad in many situations. For example, if I’d be looking for a route from the northern to the southern side of the square in the previous screenshot, the suggestions would look something like this:

Pedestrian routing in Google Maps

Pedestrian routing in Google Maps

… Great! Google wants me to walk around it …

Pedestrian routing on openstreetmap.org

Pedestrian routing on openstreetmap.org

… Openstreetmap too – but on the other side :P

Wouldn’t it be nice if we could just cross the square? There’s no reason not to. The routing graphs of OSM and Google just don’t contain a connection. Polygon datasets like the FMZK could be a solution to the issue of routing pedestrians over squares. Here’s my first attempt using GRASS r.walk:

Routing with GRASS r.walk

Routing with GRASS r.walk (Green areas are walk-friendly, yellow/orange areas are harder to cross, and red buildings are basically impassable.)

… The route crosses the square – like any sane pedestrian would.

The key steps are:

  1. Assigning pedestrian costs to different polygon classes
  2. Rasterizing the polygons
  3. Computing a cost raster for moving using r.walk
  4. Computing the route using r.drain

I’ve been using GRASS 7 for this example. GRASS 7 is not yet compatible with QGIS but it would certainly be great to have access to this functionality from within QGIS. You can help make this happen by supporting the crowdfunding initiative for the GRASS plugin update.

Over the last couple of weeks, Karolina has been very busy improving and expanding Time Manager. This post is to announce the 1.6 release of Time Manager which brings you many fixes and exciting new features.

Screenshot 2015-03-25 17.58.38

What’s this feature interpolation you’re talking about?

Interpolation is really helpful if you have multiple observations of the same (moving) real-world object at different points in time and you want to visualize the movement between the observations. This can be used to visualize animal paths, vehicle tracks, or any other movement in space.

The following example shows a simple layer which contains 12 point features (3 for each id value).

Screenshot 2015-03-25 17.50.55

Using Time Manager interpolation, it is easy to create animations with interpolated positions between observations:


How is it done?

When you open the Time Manager 1.6 Settings | Add layer dialog, you will find a new option for interpolation settings. This first version supports linear interpolation of point features but more options might be added in the future. Note how the id attribute is specified to let Time Manager know which features belong to the same real-world object.

Screenshot 2015-03-25 17.43.08

For the interpolation, Time Manager creates a new layer which contains the interpolated features. You can see this layer in the layer list.

Screenshot 2015-03-25 17.46.13

I’m really looking forward to seeing all the great animations this feature will enable. Thanks Karolina for making this possible!

You probably remember my Game of Life posts from last year: Experiments with Conway’s Game of Life & More experiments with Game of Life where I developed a vector-based version of GoL.

Richard Wen and Claus Rinner at Ryerson University now published a raster-based version.

Here’s a screenshot of the script in action:

Screenshot 2015-03-08 20.04.07

The code is hosted on Github and I’m sure there will be many other ideas which can build on code snippets to read and write raster cell values.

For more info, please visit the GIS at Ryerson blog.

How do you objectively define and compute which parts of a network are in the center? One approach is to use the concept of centrality.

Centrality refers to indicators which identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super spreaders of disease. (Source: http://en.wikipedia.org/wiki/Centrality)

Researching this topic, it turns out that some centrality measures have already been implemented in GRASS GIS. thumbs up!

v.net.centrality computes degree, betweeness, closeness and eigenvector centrality.

As a test, I’ve loaded the OSM street network of Vienna and run

v.net.centrality -a input=streets@anita_000 output=centrality degree=degree closeness=closeness betweenness=betweenness eigenvector=eigenvector


The computations take a while.

In my opinion, the most interesting centrality measures for this street network are closeness and betweenness:

Closeness “measures to which extent a node i is near to all the other nodes along the shortest paths”. Closeness values are lowest in the center of the network and higher in the outskirts.

Betweenness “is based on the idea that a node is central if it lies between many other nodes, in the sense that it is traversed by many of the shortest paths connecting couples of nodes.” Betweenness values are highest on bridges and other important arterials while they are lowest for dead-end streets.

(Definitions as described in more detail in Crucitti, Paolo, Vito Latora, and Sergio Porta. “Centrality measures in spatial networks of urban streets.” Physical Review E 73.3 (2006): 036125.)

Centrality: low values in pink, high values in green

Centrality: low values in pink, high values in green

Works great! Unfortunately, v.net.centrality is not yet part of the QGIS Processing GRASS toolbox. It would certainly be a great addition.

Since the 2.8 release is done, the QGIS team has been busy with a small side project: setting up a series of shops for fans of QGIS. Right now, the following shops are available:

North America

There is a US and a Canadian shop. Additionally, there is also the possibility to design your own products (US, Canada).



There’s also a series of European shops, for example for the UK, Germany, and France. There are more, if Spreadshirt has a site for your country, there’s probably a QGIS shop too.


Thanks to Nathan for pointing out the Australian shop!

For each product sold, the QGIS project receives around $3 (minus applicable fees) which will go directly towards improving your favorite GIS.


It’s finally here! QGIS 2.8 LTR “Wien” is officially available for download now.

What’s an LTR

LTR stands for “Long Term Release”. This means that QGIS now has a system in place to provide a one-year stable release with backported bug fixes. The idea behind LTR is to have a stable platform for enterprises and organizations that don’t want to update their software and training materials more often than once a year. To make the LTR a success, users and developers alike should be aware that bug fixes should be applied to both the LTR branch as well as the normal development branch. If you are interested in the details, you can find more info in the corresponding QGIS Enhancement Proposal.

Users who enjoy working with the cutting-edge version will be able to follow the regular four-monthly release cycle like last year.

What’s new?

This new version comes with many great new features which you can explore in the official visual changelog. It’s really hard to pick but my personal favorites are:

On the layer styling front, there are two great additions: raster image fills and a live heatmap renderer which makes it possible to create dynamic heatmaps on the fly.

raster image fill

Raster image fill symbol layer type

Another feature I’m sure many of you will enjoy is the support for custom prefixes for joins.

Custom join prefixes

Custom join prefixes

Last but not least, I want to point your attention to the great improvements to the rule-based legend which is now structured in a nice tree.

Rule-based renderer legend tree

Rule-based renderer legend tree

Don’t forget to check out the other new features!


None of this would have been possible without the great QGIS community and all the many different people involved in running the project. Thanks a lot to all of you and a special shout out for the sponsors! *applause*


We all know that QGIS is great for designing maps but did you know that QGIS is also great for interactive web maps? It is! Just check out qgis2leaf and qgis2threejs.

To give these two plugins a test run and learn some responsive web design, I developed a small concept page presenting cycle routes in 3D.

Screenshot 2015-01-31 22.20.15

Qgis2leaf makes it possible to generate Leaflet maps from QGIS layers. It provides access to different background maps and it’s easy to replace them in the final HTML file in case you need something more exotic. I also added another layer with custom popups with images but that was done manually.

Daten CC-BY-3.0: Land Kärnten - data.ktn.gv.at

The web maps use data CC-BY-3.0: Land Kärnten – data.ktn.gv.at

Qgis2threejs on the other hand creates 3D visualizations based on three.js which uses WebGL. (If you follow my blog you might remember a post a while back which showcased Qgis2threejs rendering OSM buildings.)

This is a great way to explore elevation data. I also think that the labeling capabilities add an interesting touch. Controlling the 3D environment takes some getting used to, but if you can handle Google Earth in your browser, this is no different.

Image of Heiligenblut by Angie (Self-photographed) (GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)), via Wikimedia Commons

Image of Heiligenblut by Angie (Self-photographed) (GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)), via Wikimedia Commons


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