Page 1 of 1 (7 posts)

  • talks about »
  • processing


Last update:
Sat Feb 6 07:15:12 2016

A Django site.

QGIS Planet

Increasing the stability of processing algorithms

Processing just got a new testing framework to improve the long-term stability of this important plugin. And you can help to improve it, even if you are not a software developer! This is yet another piece in our never-stopping crusade to

Generate parcels areas from parcel boundaries

Hi! In this blog I describe how you can create proper parcels with polygon geometry in from polylines (parcel boundaries) and points (Parcel point with parcel attributes placed inside parcel boundaries). Since the 1st of januari 2016 a dataset named, BRK (Basis Registratie Kadaster) is available from PDOK. You can download these in GML format … Continue reading Generate parcels areas from parcel boundaries

A Processing model for Tanaka contours

If you follow my blog, you’ve most certainly seen the post How to create illuminated contours, Tanaka-style from earlier this year. As Victor Olaya noted correctly in the comments, the workflow to create this effect lends itself perfectly to being automated with a Processing model.

The model needs only two inputs: the digital elevation model raster and the interval at which we want the contours to be created:

Screenshot 2015-07-05 18.59.34

The model steps are straightforward: the contours are generated and split into short segments before the segment orientation is computed using the following code in the Advanced Python Field Calculator:

p1 = $geom.asPolyline()[0]
p2 = $geom.asPolyline()[-1]
a = p1.azimuth(p2)
if a < 0:
   a += 360
value = a

Screenshot 2015-07-05 18.53.26

You can find the finished model on Github. Happy QGISing!

OSM quality assessment with QGIS: network length

In my previous post, I presented a Processing model to determine positional accuracy of street networks. Today, I’ll cover another very popular tool to assess OSM quality in a region: network length comparison. Here’s the corresponding slide from my FOSS4G presentation which shows an example of this approach applied to OSM and OS data in the UK:


One building block of this tool is the Total graph length model which calculates the length of a network within specified regions. Like the model for positional accuracy, this model includes reprojection steps to ensure all layers are in the same CRS before the actual geoprocessing starts:


The final Compare total graph length model combines two instances of “Total graph length” whose results are then joined to eventually calculate the length difference (lenDIFF).


As usual, you can find the models on Github. If you have any questions, don’t hesitate to ask in the comments and if you find any issues please report them on Github.

OSM quality assessment with QGIS: positional accuracy

Over the last years, research on OpenStreetMap data quality has become increasingly popular. At this year’s FOSS4G, I had the honor to present some work we did at the AIT to assess OSM quality in Vienna, Austria. In the meantime, our paper “Towards an Open Source Analysis Toolbox for Street Network Comparison” has been published for early access. Thanks to the conference organizers who made this possible! I’ve implemented comparison tools found in related OSM literature as well as new tools for oneway street and turn restriction comparison using Sextante scripts and models for QGIS 1.8. All code is available on Github to enable collaboration. If you are interested in OSM data quality research, I’d like to invite you to give the tools a try.

Since most users probably don’t have access to QGIS 1.8 anymore, I’ll be updating the tools to QGIS 2.0 Processing. I’m starting today with the positional accuracy comparison tool. It is based on a method described by Goodchild & Hunter (1997). Here’s the corresponding slide from my FOSS4G presentation:


The basic idea is to evaluate the positional accuracy of a street graph by comparing it with a reference graph. To do that, we check how much of the graph lies within a certain tolerance (buffer) of the reference graph.

The processing model uses the following input: the two street graphs which should be compared, the size of the buffer (tolerance for positional accuracy), a polygon layer with analysis regions, and the field containing the region id. This is how the model looks in Processing modeler:


First, all layers are reprojected into a common CRS. This will have to be adjusted if the tool is used in other geographic regions. Then the reference graph is buffered and – since I found that dissolving buffers directly in the buffer tool can become very slow with big datasets – the faster difference tool is used to dissolve the buffers before we calculate the graph length inside the buffer (inbufLEN) as well as the total graph length in the analysis region (totalLEN). Finally, the two results are joined based on the region id field and the percentage of graph length within the buffered reference graph (inbufPERC) is calculated. A high percentage shows that both graphs agree very well geometrically.

The following image shows the tool applied to a sample of OpenStreetMap (red) and official data published by the city of Vienna (purple) at Wien Handelskai. OSM was used as a reference graph and the buffer size was set to 10 meters.


In general, both graphs agree quite well. The percentage of the official graph within 10 meters of the OSM graph is 93% in the 20th district. In the above image, we can see that links available in OSM are not contained in the official graph (mostly pedestrian/bike links) and there seem to be some connectivity issues as well in the upper right corner of the image.

In my opinion, Processing models are a great solution to document geoprocessing work flows and share them with others. If you want to collaborate on building more models for OSM-related analysis, just leave a comment bellow.

Help for Processing scripts and models

Processing has received a series of updates since the release of QGIS 2.0. (I’m currently running 2.0-20131120) One great addition I want to highlight today is the improved script editor and the help file editor.

Script editor

The improved script editor features a toolbar with commonly used tools such as undo and redo, cut, copy and paste, save and save as …, as well as very useful run algorithm and edit script help buttons. It also shows the script line numbers which makes it easier to work with while debugging code.


The model editor has a similar toolbar now which allows to export the model representation as an image, run the model or edit the model help.

Help editor

When you press the edit script help button, you get access to the new help editor. It’s easy to use: On the top, it displays the current content of the help file. On the bottom-left, it lists the different sections of the help file which can be filled with information. In the input parameters and outputs section, the help editor automatically lists the all parameters specified in the script code. Finally, in the bottom-right, you can enter the description. The resulting help file is saved in the same location as the original script under the name <scriptname>


A routing script for the Processing toolbox

Did you know that there is a network analysis library in QGIS core? It’s well hidden so far, but at least it’s documented in the PyQGIS Cookbook. The code samples from the cookbook can be used in the QGIS Python console and you can play around to get a grip of what the different steps are doing.

As a first exercise, I’ve decided to write a Processing script which will use the network analysis library to create a network-based route layer from a point layer input. You can find the result on Github.

You can get a Spatialite file with testdata from Github as well. It contains a network and a routepoints1 layer:


The interface of the points_to_route tool is very simple. All it needs as an input is information about which layer should be used as a network and which layer contains the route points:


The input points are considered to be ordered. The tool always routes between consecutive points.

The result is a line layer with one line feature for each point pair:


The network analysis library is a really great new feature and I hope we will see a lot of tools built on top of it.

  • Page 1 of 1 ( 7 posts )
  • processing

Back to Top