From 28th April to 1st May the QGIS project organized another successful developer meeting at the Linuxhotel in Essen, Germany. Here is a quick summary of the key topics I’ve been working on during these days.

New logo rollout

It’s time to get the QGIS 3 logo out there! We’ve started changing our social media profile pictures and Website headers to the new design: 

Resource sharing platform 

In QGIS 3, the resource sharing platform will be available by default – just like the plugin manager is today in QGIS 2. We are constantly looking for people to share their mapping resources with the community. During this developer meeting Paolo Cavallini and I added two more SVG collections:

Road sign SVGs by Bertrand Bouteilles & Roulex_45 (CC BY-SA 3.0)

SVGs by Yury Ryabov & Pavel Sergeev (CC-BY 3.0)

Unified Add Layer button

We also discussed the unified add layer dialog and are optimistic that it will make its way into 3.0. The required effort for a first version is currently being estimated by the developers at Boundless.


The new TimeManager version 2.4 fixes a couple of issues related to window resizing and display on HiDPI screens. Additionally, it now saves all label settings in the project file. This is the change log:

- Fixed #222: hide label if TimeManager is turned off
- Fixed #156: copy parent style to interpolation layer
- Fixed #109: save label settings in project
- Fixed window resizing issues in label options gui
- Fixed window resizing issues in video export gui
- Fixed HiDPI issues with arch gui

After my previous posts on flow maps, many people asked me how to create the curved arrows that you see in these maps.

Arrow symbol layers were introduced in QGIS 2.16.

The following quick screencast shows how it is done. Note how additional nodes are added to make the curved arrows:

In 2012 I published a post on mapping the then newly released Tirol river dataset.

In the comments, reader Michal Zimmermann asked:

Do you think it would be possible to create a river stream which gains width along its way? I mean rivers are usually much narrower on their beginnings, then their width increases and the estuary should be the widest part, right?

For a long time, this kind of river style, also known as “tapered lines” could only be created in vector graphics software, such as Inkscape and Illustrator.

With the help of geometry generators, we can now achieve this look directly in QGIS:

Data cc-by Land Tirol

In the river dataset published by the state of Tirol, all rivers are digitized in upstream direction. For this styling to work, it is necessary that the line direction is consistent throughout the whole dataset.

We use a geometry generator symbol layer to split the river geometry into its individual segments:


Then we can use the information about the total number of segments (accessible via the expression variable @geometry_part_count) and the individual segment’s number (@geometry_part_num) to calculate the segment’s line width.

The stroke width expression furthermore uses the river category (GEW_GRKL) to vary the line width depending on the category:

WHEN "GEW_GRKL" = '< 10 km2 Fluss' THEN 0.2
WHEN "GEW_GRKL" = '10 km2 Fluss' THEN 0.4
WHEN "GEW_GRKL" = '100 km2 Fluss' THEN 0.6
WHEN "GEW_GRKL" = '1.000 km2 Fluss' THEN 0.8
ELSE 1.0
* ( 1- ( @geometry_part_num /  @geometry_part_count ))

If the rivers are digitized in downstream direction, you can simply remove the 1- term.

Happy mapping!

Geometry generator symbol layers are a feature that has been added in QGIS 2.14. They allow using the expression engine to modify geometries or even create new geometries while rendering.

Geometry generator symbol layers make it possible to use expression syntax to generate a geometry on the fly during the rendering process. The resulting geometry does not have to match with the original geometry type and we can add several differently modified symbol layers on top of each other.

The latest version of the QGIS user manual provides some example expressions, which served as a basis for the following examples:

Rendering the centroid of a feature

To add a geometry layer representing feature centroids, we need to set the geometry type to Point / Multipoint and enter the following expression:

centroid( $geometry )

It is worth noting that the correct geometry type has to be set manually. If a wrong type is set, the symbol layer can not be rendered.

Drawing buffers around features

Buffers are an example of a polygon geometry generator layer. The second parameter of the buffer function defines if the buffer is generated outside (for positive values) or inside (for negative values) of the feature. The value has to be provided in the layer’s CRS units, in this case, that means an inner buffer of 0.005 degrees:

buffer( $geometry, -0.005 )

Creating a line between features in different layers

The following expression creates lines from all district centroids (as shown in the first example) and a feature from the Citybike layer where the STATION attribute value is ‘Millennium Tower’:

  centroid( $geometry ),
  geometry( get_feature( 'Citybike', 'STATION', 'Millennium Tower' ) ) 

More advanced examples

Using these basic examples as a starting point, geometry generators open a wide field of advanced symbology options. For example, this sector light style presented on GIS.Stackexchange or my recently introduced conveyor belt flow style:

In the 1st part of this series, I mentioned the Workshop on Analysis of Movement Data at the GIScience 2016 conference. Since the workshop took place in September 2016, 11 abstracts have been published (the website seems to be down currently, see the cached version) covering topics from general concepts for movement data analysis, to transport, health, and ecology specific articles. Here’s a quick overview of what researchers are currently working on:

  • General topics
    • Interpolating trajectories with gaps in the GPS signal while taking into account the context of the gap [Hwang et al., 2016]
    • Adding time and weather context to understand their impact on origin-destination flows [Sila-Nowicka and Fotheringham, 2016]
    • Finding optimal locations for multiple moving objects to meet and still arrive at their destination in time [Gao and Zeng, 2016]
    • Modeling checkpoint-based movement data as sequence of transitions [Tao, 2016]
  • Transport domain
    • Estimating junction locations and traffic regulations using extended floating car data [Kuntzsch et al., 2016]
  • Health domain
    • Clarifying physical activity domain semantics using ontology design patterns [Sinha and Howe, 2016]
    • Recognizing activities based on Pebble Watch sensors and context for eight gestures, including brushing one’s teeth and combing one’s hair [Cherian et al., 2016]
    • Comparing GPS-based indicators of spatial activity with reported data [Fillekes et al., 2016]
  • Ecology domain
    • Linking bird movement with environmental context [Bohrer et al., 2016]
    • Quantifying interaction probabilities for moving and stationary objects using probabilistic space-time prisms [Loraamm et al., 2016]
    • Generating probability density surfaces using time-geographic density estimation [Downs and Hyzer, 2016]

If you are interested in movement data in the context of ecological research, don’t miss the workshop on spatio-temporal analysis, modelling and data visualisation for movement ecology at the Lorentz Center in Leiden in the Netherlands. There’s currently a call for applications for young researchers who want to attend this workshop.

Since I’m mostly working with human and vehicle movement data in outdoor settings, it is interesting to see the bigger picture of movement data analysis in GIScience. It is worth noting that the published texts are only abstracts, therefore there is not much detail about algorithms and whether the code will be available as open source.

For more reading: full papers of the previous workshop in 2014 have been published in the Int. Journal of Geographical Information Science, vol 30(5). More special issues on “Computational Movement Analysis” and “Representation and Analytical Models for Location-based Social Media Data and Tracking Data” have been announced.


[Bohrer et al., 2016] Bohrer, G., Davidson, S. C., Mcclain, K. M., Friedemann, G., Weinzierl, R., and Wikelski, M. (2016). Contextual Movement Data of Bird Flight – Direct Observations and Annotation from Remote Sensing.
[Cherian et al., 2016] Cherian, J., Goldberg, D., and Hammond, T. (2016). Sensing Day-to-Day Activities through Wearable Sensors and AI.
[Downs and Hyzer, 2016] Downs, J. A. and Hyzer, G. (2016). Spatial Uncertainty in Animal Tracking Data: Are We Throwing Away Useful Information?
[Fillekes et al., 2016] Fillekes, M., Bereuter, P. S., and Weibel, R. (2016). Comparing GPS-based Indicators of Spatial Activity to the Life-Space Questionnaire (LSQ) in Research on Health and Aging.
[Gao and Zeng, 2016] Gao, S. and Zeng, Y. (2016). Where to Meet: A Context-Based Geoprocessing Framework to Find Optimal Spatiotemporal Interaction Corridor for Multiple Moving Objects.
[Hwang et al., 2016] Hwang, S., Yalla, S., and Crews, R. (2016). Conditional resampling for segmenting GPS trajectory towards exposure assessment.
[Kuntzsch et al., 2016] Kuntzsch, C., Zourlidou, S., and Feuerhake, U. (2016). Learning the Traffic Regulation Context of Intersections from Speed Profile Data.
[Loraamm et al., 2016] Loraamm, R. W., Downs, J. A., and Lamb, D. (2016). A Time-Geographic Approach to Wildlife-Road Interactions.
[Sila-Nowicka and Fotheringham, 2016] Sila-Nowicka, K. and Fotheringham, A. (2016). A route map to calibrate spatial interaction models from GPS movement data.
[Sinha and Howe, 2016] Sinha, G. and Howe, C. (2016). An Ontology Design Pattern for Semantic Modelling of Children’s Physical Activities in School Playgrounds.
[Tao, 2016] Tao, Y. (2016). Data Modeling for Checkpoint-based Movement Data.


Today’s post was motivated by a question following up on my recent post “Details of good flow maps“: How to create arrows with gradients from transparent to opaque?


The key idea is to use a gradient fill to color the arrows:


It all seems perfectly straightforward: determine the direction of the line and set the gradient rotation according to the line direction.

But wait! That doesn’t work!

The issue is that all default angle functions available in expressions return clockwise angles but the gradient rotation has to be set in counter-clockwise angles. So we need this expression:


Happy QGISing!

In my previous posts, I discussed classic flow maps that use arrows of different width to encode flows between regions. This post presents an alternative take on visualizing flows, without any arrows. This style is inspired by Go with the Flow by Robert Radburn and Visualisation of origins, destinations and flows with OD maps by J. Wood et al.

The starting point of this visualization is a classic OD matrix.


For my previous flow maps, I already converted this data into a more GIS-friendly format: a Geopackage with lines and information about the origin, destination and strength of the flow:


In addition, I grabbed state polygons from Natural Earth Data.

At this point, we have 72 flow features and 9 state polygon features. An ordinary join in the layer properties won’t do the trick. We’d still be stuck with only 9 polygons.

Virtual layers to the rescue!

The QGIS virtual layers feature (Layer menu | Add Layer | Add/Edit Virtual Layer) provides database capabilities without us having to actually set up a database … *win!*

Using a classic SQL query, we can join state polygons and migration flows into a new virtual layer:


The resulting virtual layer contains 72 polygon features. There are 8 copies of each state.

Now that the data is ready, we can start designing the visualization in the Print Composer.

This is probably the most manual step in this whole process: We need 9 map items, one for each mini map in the small multiples visualization. Create one and configure it to your liking, then copy and paste to create 8 more copies.

I’ve decided to arrange the map items in a way that resembles the actual geographic location of the state that is represented by the respective map, from the state of Vorarlberg (a proud QGIS sponsor by the way) in the south-west to Lower Austria in the north-east.

To configure which map item will represent the flows from which origin state, we set the map item ID to the corresponding state ID. As you can see, the map items are numbered from 1 to 9:


Once all map items are set up, we can use the map item IDs to filter the features in each map. This can be implemented using a rule based renderer:


The first rule will ensure that the each map only shows flows originating from a specific state and the second rule will select the state itself.

We configure the symbol of the first rule to visualize the flow strength. The color represents the number number of people moving to the respective district. I’ve decided to use a smooth gradient instead of predefined classes for the polygon fill colors. The following expression maps the feature’s weight value to a shade on the Viridis color ramp:

ramp_color( 'Viridis',

You can use any color ramp you like. If you want to use the Viridis color ramp, save the following code into an .xml file and import it using the Style Manager. (This color ramp has been provided by Richard Styron on

<!DOCTYPE qgis_style>
<qgis_style version="0">
    <colorramp type="gradient" name="Viridis">
      <prop k="color1" v="68,1,84,255"/>
      <prop k="color2" v="253,231,36,255"/>
      <prop k="stops" v="0.04;71,15,98,255:0.08;72,29,111,255:0.12;71,42,121,255:0.16;69,54,129,255:0.20;65,66,134,255:0.23;60,77,138,255:0.27;55,88,140,255:0.31;50,98,141,255:0.35;46,108,142,255:0.39;42,118,142,255:0.43;38,127,142,255:0.47;35,137,141,255:0.51;31,146,140,255:0.55;30,155,137,255:0.59;32,165,133,255:0.62;40,174,127,255:0.66;53,183,120,255:0.70;69,191,111,255:0.74;89,199,100,255:0.78;112,206,86,255:0.82;136,213,71,255:0.86;162,218,55,255:0.90;189,222,38,255:0.94;215,226,25,255:0.98;241,229,28,255"/>

If we go back to the Print Composer and update the map item previews, we see it all come together:


Finally, we set title, legend, explanatory texts, and background color:


I think it is amazing that we are able to design a visualization like this without having to create any intermediate files or having to write custom code. Whenever a value is edited in the original migration dataset, the change is immediately reflected in the small multiples.

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