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QGIS Planet

Logo Evolution – Call to User Groups

With QGIS 3.0 moving closer, it is also time to get the new QGIS logo out there.

QGIS logo evolution

We are therefore asking all our user groups to join the project redesign efforts. Please have a look at your user group logo and bring it in line with the QGIS 3.0 design.

We have compiled a Visual Style Guide which provides all logo materials, including fonts that you can use for your own user group logo. Here is an example of how the Italian user group has updated their logo:

Italian user group redesigned

We are looking forward to seeing all your creative efforts!

Straight and curved arrows with QGIS

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:

About label halos

A lot of cartographers have a love/hate relationship with label halos. On one hand they can be an essential technique for improving label readability, especially against complex background layers. On the other hand they tend to dominate maps and draw unwanted attention to the map labels.

In this post I’m going to share my preferred techniques for using label halos. I personally find this technique is a good approach which minimises the negative effects of halos, while still providing a good boost to label readability. (I’m also going to share some related QGIS 3.0 news at the end of this post!)

Let’s start with some simple white labels over an aerial image:

These labels aren’t very effective. The complex background makes them hard to read, especially the “Winton Shire” label at the bottom of the image. A quick and nasty way to improve readability is to add a black halo around the labels:

Sure, it’s easy to read the labels now, but they stand out way too much and it’s difficult to see anything here except the labels!

We can improve this somewhat through a better choice of halo colour:

This is much better. We’ve got readable labels which aren’t too domineering. Unfortunately the halo effect is still very prominent, especially where the background image varies a lot. In this case it works well for the labels toward the middle of the map, but not so well for the labels at the top and bottom.

A good way to improve this is to take advantage of blending (or “composition”) modes (which QGIS has native support for). The white labels will be most readable when there’s a good contrast with the background map, i.e. when the background map is dark. That’s why we choose a halo colour which is darker than the text colour (or vice versa if you’ve got dark coloured labels). Unfortunately, by choosing the mid-toned brown colour to make the halos blend in more, we are actually lightening up parts of this background layer and both reducing the contrast with the label and also making the halo more visible. By using the “darken” blend mode, the brown halo will only be drawn for pixels were the brown is darker then the existing background. It will darken light areas of the image, but avoid lightening pixels which are already dark and providing good contrast. Here’s what this looks like:

The most noticeable differences are the labels shown above darker areas – the “Winton Shire” label at the bottom and the “Etheridge Shire” at the top. For both these labels the halo is almost imperceptible whilst still subtly doing it’s part to make the label readable. (If you had dark label text with a lighter halo color, you can use the “lighten” blend mode for the same result).

The only issue with this map is that the halo is still very obvious around “Shire” in “Richmond Shire” and “McKinlay” on the left of the map. This can be reduced by applying a light blur to the halo:

There’s almost no loss of readability by applying this blur, but it’s made those last prominent halos disappear into the map. At first glance you probably wouldn’t even notice that there’s any halos being used here. But if we compare back against the original map (which used no halos) we can see the huge difference in readability:

Compare especially the Winton Shire label at the bottom, and the Richmond Shire label in the middle. These are much clearer on our tweaked map versus the above image.

Now for the good news… when QGIS 3.0 is released you’ll no longer have to rely on an external illustration/editing application to get this effect with your maps. In fact, QGIS 3.0 is bringing native support for applying many types of live layer effects to label buffers and background shapes, including blur. This means it will be possible to reproduce this technique directly inside your GIS, no external editing or tweaking required!

QGIS Composer Rewrite and Layout Engine crowdfund launched!

At North Road we believe that crowdfunding is a sustainable way to maintain and enhance open source software, like the QGIS open source GIS package. We’ve run a number of successful crowdfunding campaigns in the past, including support in QGIS for live layer effects, a point cluster renderer, and a unique value renderer for raster layers.

Now, we’re proud to announce our latest crowd funding endeavour, and our biggest to date, the QGIS Layout and Reporting Engine Campaign.

This campaign covers stage 1 of a large, ongoing project to modernise and expand on QGIS’ print composer and layout facilities. Over time QGIS’ composer functionality has grown extensively and now is capable of creating flexible, high quality cartographic outputs. However, we’ve now hit a limit where the current code architecture is prohibiting further improvements and important fixes. In order to add a reporting framework to QGIS, it is necessary for us to refactor and improve large sections of the composer code.

If this campaign is successful, we’ll be adding flexible report generation features to QGIS and cleaning up all the existing composer code. As part of these clean up, we’ll be taking the opportunity to tackle a number of current limitations which cannot be addressed in the current composition code:

  • Layouts will become unit aware, allowing for item placement and properties using millimetres, inches, pixels, centimetres, points, etc.
  • Layouts will have the ability to include mixed page sizes and orientations.
  • Plugins will be able to create custom composer item types (eg allow utilisation of 3rd party graphing and visualisation libraries!).
  • Individual layout items can be rasterised without affecting the rest of the layout. For instance, a map which requires rasterisation due to its use of blend modes will not require all other layout items (such as headings, legends, etc) to be rasterised. This will greatly benefit PDF outputs for complex map layouts.
  • The code refresh will allow more extensive use of data defined layout item properties.
  • A render caching system will be implemented for items, speeding up use of the layout designer and also paving the way for use of live paint effects on layout items (eg dynamic drop shadows).

Full details on what we have planned are available here: QGIS Layout and Reporting Engine Campaign.

To make it possible we need 30,000€ pledged before 31 May 2017. You can help make this a reality by supporting the campaign or by sharing the campaign page and increasing exposure to the campaign. Updates to follow!

Better river styles with tapered lines

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!

Quick guide to geometry generator symbol layers

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:

Update on the QGIS Grant Applications

In February this year, we put out a call for applications for the second (yay!) round of the QGIS grant programme. The intent of the programme is to leverage donor and sponsor funding in order to support community members with great ideas to improve the underlying infrastructure of the QGIS project and code base.

We have had a really great response to the call for applications (detailed list of applications is here for your reading pleasure – 231KB download).

Given that we have 13 proposals (down 7 from our last call) and only 20,000 Euros to disburse, the QGIS voting members will need to make some tough, pragmatic choices.The voting for the grant proposals ends at the mid April 2017, and we plan to announce the successful candidates soon after that. The PSC will arbitrate in the case of a dead heat or the proposal amounts of the top voted proposals not adding up to our funding target.

Although the number of proposals submitted is down from last year, the quality and utility of the proposals this year is really top notch and it is sad to know that we will not be able to fun them all through the grant programme. If you have the wherewithal to further support some of the grant proposals that did not make the cut, or the QGIS 3.0 effort in general, please get into contact with our treasurer, Andreas Neumann (finance [at] or head over to our sponsorship or donations page to support their work!

Lastly, I appeal to those QGIS Voting Members who have not yet cast their votes to check your email and head over to the voting form to cast your vote!

Crayfish 2.5: New Features

A new release of Crayfish is now available from QGIS plugin repo.

Here are the new features in more detail…

Trace animation

We have added support for live trace animation on QGIS canvas:

Please note that export to trace animation (as an avi file) is not supported yet. To change vector style to traces, please refer to the Crayfish manual.

Support for UGRID

UGRID format for Deltares modelling packages are now supported in Crayfish. In addition to 2D mesh, UGRID also supports 1D mesh. Support for 1D mesh is experimental in this release.

FLO-2D HDF format

In addition to ASCII FLO-2D files, Crayfish can handle binary HDF output from FLO-2D. Several bugs related to FLO-2D file format were also resolved.

XDMF format

We are pleased to merge changes from Furgo GeoConsulting to support XDMF format. The new format supports on-the-fly loading of the data.


The new and improved support for file formats for HDF5 and UGRID have been kindly sponsored by FLO-2D and Deltares. We developed trace animation for fun.

Feedback and bug report

If you have any problem with Crayfish, please do not email us directly and consider filing a bug here:

Gray is the new Black

Sometimes I prefer to publish my map in gray instead of black. But all newly added QGIS composer items are set to black by default. For changing the colors more easily and rapidly I created the “Turn Gray” plugin. By default it changes all foreground colors (labels and outlines) to gray. But you are free … Continue reading Gray is the new Black

FOSSGIS 2017 in Passau

In zwei Wochen beginnt die alljährliche deutschsprachige FOSSGIS Konferenz zum Theme Open Source GIS und OpenStreetMap in Passau. Vom 22.-25. März 2017 wird die FOSSGIS-Konferenz mit Untersützung der Universität Passau in der Dreiflüssestadt Passau stattfinden. Die FOSSGIS-Konferenz ist die im D-A-CH-Raum führende Konferenz für Freie und Open Source Software für Geoinformationssysteme sowie für die Themen OpenStreetMap und Open Data. An vier Tagen werden in Vorträgen für Einsteiger und Experten, Hands-On Workshops und Anwendertreffen Einblicke in neuste Entwicklungen und Anwendungsmöglichkeiten von Softwareprojekten gegeben.

Photo: Tobias Hobmeier (CC-BY-SA)

Sourcepole ist wieder mit einem Stand vertreten und lädt zu interessanten Workshops und Vorträgen ein:

  • Mittwoch 17:00 - Workshop Entwicklung von QGIS Plugins
  • Donnerstag 14:30 (HS 9) - QGIS Server Projektstatus
  • Donnerstag 14:30 (HS 11) - Von WMS zu WMTS zu Vektor-Tiles
  • Donnerstag 15:00 (HS 9) - QGIS Web Client 2

Das gesamte Programm ist auch als praktische Android App erhältlich und die Online-Anmeldung ist noch bis am 19.3. offen.

Wir freuen uns auf eine interessante Konferenz!

Movement data in GIS #5: current research topics

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.


A report on QGIS Income 2016

Greetings wonderful QGIS Users, Developers and Supporters! 2016 was quite an  exciting year for QGIS – we released QGIS 2.14LTR which has been a great stable release. We also set the wheels in motion for QGIS 3.0 which we plan to release later this year. QGIS development takes a lot of time and effort – and much of the work is done by volunteers – especially the core project maintenance work which typically does not attract paid-for consulting work like new feature work does.

We are extremely lucky and grateful to have a growing band of sponsors that are helping us to slowly but surely fund more core QGIS project work too. In this post we want to say ‘thank you’ to the many sponsors that have stepped up to the plate when we asked for help and sponsored QGIS. We also want to provide some insight as to what level of funding we receive and hopefully stimulate some friendly enthusiasm to stepping up funding efforts from our users around the world.

Our project treasurer, Andreas Neumann, has been so kind as to compile a very detailed report about the sponsorships and donations received over 2016 which we would like to share with you. Before I share his actual report, let me just clarify that we have two distinct ways of contributing money to the project:

  • Donations: You simply nominate an amount you wish to contribute and pay it to the project. Donations are intended as a way for individual users to show their support for QGIS (although we welcome large donations too).
  • Sponsorships: Sponsorships are aimed at organizations. We have fixed tiers of contribution (see below) and each tier includes some benefits in terms of marketing exposure for your company or organisation.

QGIS sponsorship can be made at one of four levels:

2017-01-30 01_22_27-Projecta_ Sponsorship Levels -.png

Higher tier (silver and up) sponsors are included in the footer of our main web site, e.g.:

Screen Shot 2017-02-25 at 8.50.22 AM.png

If you want to see your company listed there, why not head over to the QGIS Sponsorship page to find out how to become a sponsor?

Some interesting highlights from Andreas’ report:

Screen Shot 2017-02-25 at 9.00.07 AM.png

We had  1128 donations in 2016 amounting to a total of  €25108. We had  47 Sponsors in 2016 which raised a total income of  €67921. December is the month that attracts the highest income from donations and sponsors (though that may coincide with additional marketing we did calling for sponsors).  Looking at contributions by country, Denmark was the highest national contributor in 2016, providing a total of  €17795 to the QGIS.ORG project. If you normalize by population,  Iceland provided the largest per capita contribution to QGIS, providing  €9056 per million people.  Iceland also had the highest contribution rate when measured against GDP with  ~€154 per billion GDP.

Screen Shot 2017-02-25 at 9.04.34 AM.png

You can read Andreas’ full report in this PDF – a huge thank you to Andreas for preparing this fascinating report, and of course to everyone out there who shared their hard earned money with us – your funding has become a vital enabler for moving the project forward.



Tim Sutton
(QGIS Project Chair)

New map coloring algorithms in QGIS 3.0

It’s been a long time since I last blogged here. Let’s just blame that on the amount of changes going into QGIS 3.0 and move on…

One new feature which landed in QGIS 3.0 today is a processing algorithm for automatic coloring of a map in such a way that adjoining polygons are all assigned different color indexes. Astute readers may be aware that this was possible in earlier versions of QGIS through the use of either the (QGIS 1.x only!) Topocolor plugin, or the Coloring a map plugin (2.x).

What’s interesting about this new processing algorithm is that it introduces several refinements for cartographically optimising the coloring. The earlier plugins both operated by pure “graph” coloring techniques. What this means is that first a graph consisting of each set of adjoining features is generated. Then, based purely on this abstract graph, the coloring algorithms are applied to optimise the solution so that connected graph nodes are assigned different colors, whilst keeping the total number of colors required minimised.

The new QGIS algorithm works in a different way. Whilst the first step is still calculating the graph of adjoining features (now super-fast due to use of spatial indexes and prepared geometry intersection tests!), the colors for the graph are assigned while considering the spatial arrangement of all features. It’s gone from a purely abstract mathematical solution to a context-sensitive cartographic solution.

The “Topological coloring” processing algorithm

Let’s explore the differences. First up, the algorithm has an option for the “minimum distance between features”. It’s often the case that features aren’t really touching, but are instead just very close to each other. Even though they aren’t touching, we still don’t want these features to be assigned the same color. This option allows you to control the minimum distance which two features can be to each other before they can be assigned the same color.

The biggest change comes in the “balancing” techniques available in the new algorithm. By default, the algorithm now tries to assign colors in such a way that the total number of features assigned each color is equalised. This avoids having a color which is only assigned to a couple of features in a large dataset, resulting in an odd looking map coloration.

Balancing color assignment by count – notice how each class has a (almost!) equal count

Another available balancing technique is to balance the color assignment by total area. This technique assigns colors so that the total area of the features assigned to each color is balanced. This mode can be useful to help avoid large features resulting in one of the colors appearing more dominant on a colored map.

Balancing assignment by area – note how only one large feature is assigned the red color

The final technique, and my personal preference, is to balance colors by distance between colors. This mode will assign colors in order to maximize the distance between features of the same color. Maximising the distance helps to create a more uniform distribution of colors across a map, and avoids certain colors clustering in a particular area of the map. It’s my preference as it creates a really nice balanced map – at a glance the colors look “randomly” assigned with no discernible pattern to the arrangement.

Balancing colors by distance

As these examples show, considering the geographic arrangement of features while coloring allows us to optimise the assigned colors for cartographic output.

The other nice thing about having this feature implemented as a processing algorithm is that unlike standalone plugins, processing algorithms can be incorporated as just one step of a larger model (and also reused by other plugins!).

QGIS 3.0 has tons of great new features, speed boosts and stability bumps. This is just a tiny taste of the handy new features which will be available when 3.0 is released!

QGIS Grants #2: Call for Grant Proposals 2017

Dear QGIS Community

Last year we held our first ever call for Grant Proposals and it was a great success. If you are an early adopter using QGIS 3.0 preview builds, you can already try out some of the new capabilities that have arrived in QGIS thanks to these grants.

We are very pleased to announce the second round of Grants is now available to QGIS Contributors. The deadline for this round is Sunday, 19 March 2017. All the details for the Grant are described in the application form, and for more context we encourage you to also read these articles:

We look forward to seeing all your great ideas about how to improve QGIS!

Tim Sutton

QGIS Project Chair

AutoForm Plugin for QGIS

The AutoForm plugin for QGIS automatically sets the edit widget type for the fields of a selected layer based on their data types and foreign keys. This is in order to save the user time they may need to spend on manually editing these widgets. In order for this to work correctly, the layer information must be stored in a PostgreSQL database. Furthermore, foreign keys must have a constraint rule for this to work. The plugin does NOT allow you to set relations. It merely checks for any and takes advantage of them accordingly.


When you have a database with a lot of tables, there should be a way for quickly entering data whithout having to configure the input form for each table first. In Switzerland this a common use case when you import Interlis data with ili2pg based on an official data model. ili2pg automatically adds reference constraints based on the Interlis model, which is a prerequisite to detect relations in a generic way.

There is already a plugin with a very similar goal: DataDrivenInputMask written by Bernhard Ströbl. Maybe it was bad luck, but we had always troubles when using it. Once we eventually found out the PostgreSQL driver for the QtSql library was missing and another time we failed to use the plugin because we had a socket based connection to PostgreSQL. In both cases we were stuck in the login dialog without a hint why the connection had failed. Another problem is in some cases that the DataDrivenInputMask plugin writes additional metadata into the original database.

So we decided to make our own proof of concept, hopefully avoiding these problems and having the Swiss “ili2pg” use case in mind. A major conceptual difference is, that AutoForm uses the built-in input form functionality of QGIS instead of creating custom dialogs like DataDrivenInputMask does.

Running the plugin

After you have installed the AutoForm plugin (flagged “experimental”) via the QGIS Plugin repository you should see a new option in the Plugins menu called AutoForm. Clicking on it will give you the option Generate Form. Running this with the layer selected will generate your form. If everything is correctly configured, this next step should be easy.

  • Select a PostGIS layer from the Layers Panel
  • Click on Generate Form

And thats it! Now if you toggle editing you should see the changes which were made. In my case, this was the result:



If your layer had a foreign key reference to one or multiple other tables, then they too were added automatically to the Layers Panel and their values should be selectable in their according fields.

Behind the Scenes

In order to perhaps help a bit with understanding how this Plugin works, I am adding this section for anyone who is curious. The Plugin follows this process:

  • Check if a layer is selected
  • If a layer is selected search for any key relationships to other tables
  • If a foreign key relationship to another table was found the load that table into the project and set the corresponding fields to a Value Relation WidgetType
  • Proceed normally and set the remaining Widgets where applicable

The script checks first if the field has already been changed, so as to prevent user made edits or the previously set Value Relations from being overwritten. Then it takes a look at the typeName() of the field in order to determine which widgettype to assign. For example if the field has the Date Type, it would use the calender widget. It does this for each field in the layer. Should the user wish to make additional changes (or correct any mistakes which might have been made), they can do so in the ‘Fields’ tab of the layer properties.


This plugin is in an early stage and serves as a proof of concept. There is much left to be done, especially in supporting embedded forms for related tables. We also don’t analyze additional metadata written by ili2pg, which would allow to recognize whether related tables are only lookup tables without the need to embed them as entry forms.

The source code is available on Github. If you are interested in collaborating or maybe sponsoring improvements to this plugin, please contact us!

Minutes of the Inaugural QGIS General Meeting (2016)

Dear QGIS community members

At the end of December 2016, we held our inaugural Annual General Meeting (AGM). It should be noted that QGIS AGM’s will be ‘virtual’ meetings conducted as a questionnaire (for voting / raising motions) and then a report such as the one below, with optional follow up email discussion if deemed necessary. The reason for this is that our community is spread far and wide and any attempt at an in-person (physical or virtual) meeting will prove very difficult given the number of different time zones the participants live in. Thus this, and future, AGM reports will be a reflection / summary of the various online activities that constitute the ‘AGM’ and the reports should be read with this understanding in mind.  It should also be kept in mind that because we are bootstrapping the QGIS Board, the 2016/ 2017 meetings follow a reduced agenda – the 2018 meeting will commence with the full AGM agenda since we will have a year of financial reporting / board activity etc. behind us. Here follow the minutes:


Minutes of the Inaugural General Assembly (AGM) 2016 – Concluded 22 December 2016

Dear QGIS.ORG voting members,

In December 2016 we held a special Inaugural AGM event in order to bootstrap the new governance structures for QGIS.ORG. The following items were tabled during our special AGM to cater for the needed transition from the old organisation to the new:

Election of incoming board and PSC member replacements

Under our statutes, existing board / PSC members are automatically put forward for re-election during our PSC elections. A call for nominations was held and closed on 15 December 2016. All voting members were elegible to put forward their nominations. The following valid nominations were received (i.e. for candidates who were not already PSC members):

  1. Régis Haubourg

Subsequent to the call for nominations, an election was held via online vote. Response rate was 20 out of 27 eligible voters. Under our statutes this is considered a quorum. Election results were as follows:

Position Name Votes Received Max Votes
Chair: QGIS Board Tim Sutton 19 20
Vice-Chair: QGIS Board Paolo Cavallini 12 20
Treasurer: QGIS Board Andreas Neumann 20 20
PSC Member Richard Duivenvoorde 17 20
PSC Member Anita Graser 16 20
PSC Member Jϋrgen Fischer 14 20
Honorary PSC Member Gary Sherman

Note that each voting member was allowed 6 votes, but was not allowed to vote for the same person more than once. For the Board Member positions, which were specifically named, the candidate with the greatest number of votes for each role assigned that role. The PSC member positions were calculated based on the total number of votes each candidate received (including those allocated to named board roles). In accordance with our statutes, Gary Sherman remains on the PSC on a permanent basis as Honorary PSC member.

Election of the board chair

Election of the board chair was carried out as part of the general voting process. The successful candidate was Tim Sutton.

Matters brought forward by voting members

There were no matters brought forward by voting members.

Annual AGM for 2017

Now that the special AGM has been held and the Board and PSC constituted, we will hold our first AGM in the first quarter of 2017 (and then each year thereafter will be held in the first quarter). The 2017 AGM will be a reduced format since we will have just elected the PSC and Board,

Agenda for 2017 AGM:

  1. Approval of the annual report (2016) by the chair
  2. Approval of the annual financial report (2016)
  3. Approval of the 2017 budget
  4. Election of two financial auditors for the upcoming year
  5. Deal with matters brought forward by voting members


Given the outcomes of the end-2016 AGM, the QGIS Governance now looks like this (click for larger version):


If you have any queries about the QGIS Governance processes, please do not hesitate to ask!



Tim Sutton (QGIS Project Chair)


Gradient arrows

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!

(Nederlands) TopoTijdReis kaartlagen in QGIS

Sorry, this entry is only available in the Dutch language

Small multiples for OD flow maps using virtual layers

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.

New major release: GRASS GIS 7.2.0 available

We are pleased to announce the stable release of GRASS GIS 7.2.0

What’s new in a nutshell

After almost two years of development the new stable major release GRASS GIS 7.2.0 is available. It provides more than 1950 stability fixes and manual improvements compared to the former stable release version 7.0.5. The new version includes a series of new modules to analyse raster and vector data along with new temporal algebra functionality.More than 50 new addons are also available. A summary of the new features is available at New Features in GRASS GIS 7.2.

About GRASS GIS 7: Its graphical user interface supports the user to make complex GIS operations as simple as possible. The updated Python interface to the C library permits users to create new GRASS GIS-Python modules in a simple way while yet obtaining powerful and fast modules. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. A lot of effort has been invested to standardize parameter and flag names. Finally, GRASS GIS 7 comes with a series of new modules to analyse raster and vector data, along with a full temporal framework. For a detailed overview, see the list of new features. As a stable release series, 7.2.x enjoys long-term support.

Binaries/Installer download:

Source code download:

More details:

See also our detailed announcement:

First time users may explore the first steps tutorial after installation.


The Geographic Resources Analysis Support System (, commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, December 2016

The post New major release: GRASS GIS 7.2.0 available appeared first on GFOSS Blog | GRASS GIS Courses.

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