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It’s been a while since my last blog post mostly because I’ve been busy with some more long form writing. Most notably, I’ve been writing a paper on the QGIS Projcessing framework in the open access ISPRS International Journal of Geo-Information together with Victor Olaya and I’m still in the process of writing a new book titled “QGIS Map Design” together with Gretchen Peterson which is scheduled for early 2016.

Today’s post has been on my todo list for a while now. It’s inspired by a talk at a recent cartography conference I attended:

https://twitter.com/underdarkGIS/status/664402693801287681

(For a summary of the whole event, check the storify I compiled.)

The idea of this slide and several more was to show all the attention to detail which goes into designing a good road map. One aspect seemed particularly interesting to me since I had never considered it before: what do we communicate by our choice of line caps? The speaker argued that we need different caps for different situations, such as closed square caps at the end of a road and open flat caps when a road turns into a narrower path.

I’ve been playing with this idea to see how to reproduce the effect in QGIS …

https://twitter.com/underdarkGIS/status/665638297998331905

So first of all, I created a small test dataset with different types of road classes. The dataset is pretty simple but the key to recreating the style is in the attributes for the road’s end node degree values (degree_fro and degree_to), the link’s road class as well as the class of the adjacent roads (class_to and class_from). The degree value simply states how many lines connect to a certain network node. So a dead end as a degree of 1, a t-shaped intersection has a degree of 3, and so on. The adjacent class columns are only filled if the a neighbor is of class minor since I don’t have a use for any other values in this example. Filling the degree and adjacent class columns is something that certainly could be automated but I haven’t looked into that yet.

roadattributes

 

The layer is then styled using rules. There is one rule for each road class value. Rendering order is used to ensure that bridges are drawn on top of all other lines.

roadrules

Now for the juicy part: the caps are defined using a data-defined expression. The goal of the expression is to detect where a road turns into a narrow path and use a flat cap there. In all other cases, square cap should be used.

roadrule

Like some of you noted on Twitter after I posted the first preview, there is one issue and that is that we can only set one cap style per line and it will affect both ends of the line in the same way. In practice though, I’m not sure this will actually cause any issues in the majority of cases.

I wonder if it would be possible to automate this style in a way such that it doesn’t require any precomputed attributes but instead uses some custom functions in the data-defined expressions which determine the correct style on the fly. Let me know if you try it!

The QGIS project is asking for user feedback to gain a better understanding of the wishes and requirements of its user base. Please take part in the survey and share the links with other QGIS users. The survey is available in multiple languages:

In Publishing interactive web maps using QGIS, I presented two plugins for exporting web maps from QGIS. Today, I want to add an new member to this family: the qgis2web plugin is the successor of qgis-ol3 and combines exports to both OpenLayers3 as well as Leaflet.

The plugin is under active development and currently not all features are supported for both OpenLayers3 and Leaflet, but it’s a very convenient way to kick-off a quick webmapping project.

Here’s an example of an OpenLayers3 preview with enabled popups:

OpenLayers3 preview

OpenLayers3 preview

And here is the same map in Leaflet with the added bonus of a nice address search bar which can be added automatically as well:

Leaflet preview

Leaflet preview

The workflow is really straight forward: select the desired layers and popup settings, pick some appearance extras, and then don’t forget to hit the Update preview button otherwise you might be wondering why nothing happens ;)

I’ll continue testing these plugins and am looking forward to seeing what features the future will bring.

Granted, I could only follow FOSS4G 2015 remotely on social media but what I saw was quite impressive and will keep me busy exploring for quite a while. Here’s my personal pick of this year’s highlights which I’d like to share with you:

QGIS

Marco Hugentobler at FOSS4G 2015 (Photo by Jody Garnett)

Marco Hugentobler at FOSS4G 2015 (Photo by Jody Garnett)

The Sourcepole team has been particularly busy with four presentations which you can find on their blog.

Marco Hugentobler’s keynote is just great, summing up the history of the QGIS project and discussing success factor for open source projects.

Marco also gave a second presentation on new QGIS features for power users, including live layer effects, new geometry support (curves!), and geometry checker.

There has also been an update to QTiles plugin by NextGIS this week.

If you’re a bit more into webmapping, Victor Olaya presented the Web App Builder he’s been developing at Boundless. Web App Builder should appear in the official plugin repo soon.

Preview of Web App Builder from Victors presentation

Preview of Web App Builder from Victors presentation

Geocoding

If you work with messy, real-world data, you’ve most certainly been fighting with geocoding services, trying to make the best of a bunch of address lists. The Python Geocoder library promises to make dealing with geocoding services such as Google, Bing, OSM & many easier than ever before.

Let me know if you tried it.

Mobmap Visualizations

Mobmap – or more specifically Mobmap2 – is an extension for Chrome which offers visualization and analysis capabilities for trajectory data. I haven’t tried it yet but their presentation certainly looks very interesting:

We are celebrating FOSS4G 2015 in Seoul with great open source GIS book discounts at both Packt and Locate Press. So if you don’t have a copy of “Learning QGIS”, “The PyQGIS Programmer’s Guide”, or “Geospatial Power Tools” yet, check out the following sites:

2

This is a follow-up on my previous post introducing an Open source IDF parser for QGIS. Today’s post takes the code further and adds routing functionality for foot, bike, and car routes including oneway streets and turn restrictions.

You can find the script in my QGIS-resources repository on Github. It creates an IDFRouter object based on an IDF file which you can use to compute routes.

The following screenshot shows an example car route in Vienna which gets quite complex due to driving restrictions. The dark blue line is computed by my script on GIP data while the light blue line is the route from OpenRouteService.org (via the OSM route plugin) on OSM data. Minor route geometry differences are due to slight differences in the network link geometries.

Screenshot 2015-08-01 16.29.57

IDF is the data format used by Austrian authorities to publish the official open government street graph. It’s basically a text file describing network nodes, links, and permissions for different modes of transport.

Since, to my knowledge, there hasn’t been any open source IDF parser available so far, I’ve started to write my own using PyQGIS. You can find the script which is meant to be run in the QGIS Python console in my Github QGIS-resources repo.

I haven’t implemented all details yet but it successfully parses nodes and links from the two example IDF files that have been published so far as can be seen in the following screenshot which shows the Klagenfurt example data:

Screenshot 2015-07-23 16.23.25

If you are interested in advancing this project, just get in touch here or on Github.

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!

It’s my pleasure to report back from this year’s AGIT and GI_Forum conference (German and English speaking respectively). It was great to meet the gathered GIS crowd! If you missed it, don’t despair: I’ve compiled a personal summary on Storify, and papers (German, English) and posters are available online. Here’s a pick of my favorite posters:

I also had the pleasure to be involved in multiple presentations this year:

QGIS at the OSGeo Day

As part of the OSGeo Day, I had the chance to present the latest and greatest QGIS features for map design in front of a full house:

Routing with OSM

On a slightly different note, my colleague Markus Straub and I presented an introduction to routing with OpenStreetMap covering which kind of routing-related information is available in OSM as well as a selection of different tools to perform routing on OSM.

Solving the “unnamed link” problem

In this talk, I presented approaches to solving issues with route descriptions that contain unnamed pedestrian or cycle paths.

Here you can find the full open access paper: Graser, A., & Straub, M. (2015). Improving Navigation: Automated Name Extraction for Separately Mapped Pedestrian and Cycle Links. GI_Forum ‒ Journal for Geographic Information Science, 1-2015, 546-556, doi:10.1553/giscience2015s546.

Inferring road popularity from GPS trajectories

In this talk, my colleague Markus Straub presented our new approach to computing how popular a certain road is. The resulting popularity value can be used for planning as well as routing.

https://twitter.com/underdarkGIS/status/618690617556795392

Here you can find the full open access paper: Straub, M., & Graser, A. (2015). Learning from Experts: Inferring Road Popularity from GPS Trajectories. GI_Forum ‒ Journal for Geographic Information Science, 1-2015, 41-50, doi:10.1553/giscience2015s41.

If you are following QGIS on Twitter you’ve probably noticed the increasing number of tweets by journalists using QGIS.

For example this map in the Financial Times by Hannah Dormido

or this one with overview maps and three different levels of details

or this map with semi-transparent label backgrounds and nice flag images

or even Time Manager animations by raoulranoa in the Los Angeles Times

https://twitter.com/ranoa/status/616668385036902401

I think this is a great development and a sign of how wide-spread QGIS usage is today.

If you know of any other examples or if you are a journalist using QGIS yourself, I’d love to see more!