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There are tons of things going on under the hood of QGIS for the move from version 2 to version 3. Besides other things, we’ll have access to new versions of Qt and Python. If you are using a HiDPI screen, you should see some notable improvements in the user interface of QGIS 3.

But of course QGIS 3 is not “just” a move to updated dependencies. Like in any other release, there are many new features that we are looking forward to. This list is only a start, including tools that already landed in the developer version 2.99:

Improved geometry editing 

Including undo and redo panel for quick access to previous versions.

Improved Processing dialogs

Like many other parts of the QGIS user interface, Processing dialogs now prominently display the function help.

In addition, GDAL/OGR tools also show the underlying GDAL/OGR command which can be copy-pasted to use it somewhere else.

New symbols and predefined symbol groups

The default symbols have been reworked and categorized into different symbol groups. Of course, everything can be customized in the Symbol Library.

Search in layer and project properties

Both the layer properties and the project properties dialog now feature a search field in the top right corner. This nifty little addition makes it much easier to find specific settings fast.

Save images at custom sizes

Last but not least, a long awaited feature: It’s finally possible to specify the exact size and properties of images created using Project | Save as image.

Of course, we still expect many other features to arrive in 3.0. For example, one of the successful QGIS grant applications was for adding 3D support to QGIS. Additionally, there is an ongoing campaign to fund better layout and reporting functionality in QGIS print composer. Please support it if you can!

 

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AGILE 2017 is the annual international conference on Geographic Information Science of the Association of Geographic Information Laboratories in Europe (AGILE) which was established in 1998 to promote academic teaching and research on GIS.

This years conference in Wageningen was my time at AGILE.  I had the honor to present our recent work on pedestrian navigation with landmarks [Graser, 2017]. If you are interested in trying it, there is an online demo. The conference also provided numerous pointers toward ideas for future improvements, including [Götze and Boye, 2016] and [Du et al., 2017]

On the issue of movement data in GIS, there weren’t too many talks on this topic at AGILE but on the conceptual side, I really enjoyed David Jonietz’ talk on how to describe trajectory processing steps:

Source: [Jonietz and Bucher, 2017]

In the pre-conference workshop I attended, there was also an interesting presentation on analyzing trajectory data with PostGIS by Phd candidate Meihan Jin.

I’m also looking forward to reading [Wiratma et al., 2017] “On Measures for Groups of Trajectories” because I think that the presentation only scratched the surface.

References

[Du et al, 2017] Du, S., Wang, X., Feng, C. C., & Zhang, X. (2017). Classifying natural-language spatial relation terms with random forest algorithm. International Journal of Geographical Information Science, 31(3), 542-568.
[Götze and Boye, 2016] Götze, J., & Boye, J. (2016). Learning landmark salience models from users’ route instructions. Journal of Location Based Services, 10(1), 47-63.
[Graser, 2017] Graser, A. (2017). Towards landmark-based instructions for pedestrian navigation systems using OpenStreetMap, AGILE2017, Wageningen, Netherlands.
[Jonietz and Bucher, 2017] Jonietz, D., Bucher, D. (2017). Towards an Analytical Framework for Enriching Movement Trajectories with Spatio-Temporal Context Data, AGILE2017, Wageningen, Netherlands.
[Wiratma et al., 2017] Wiratma L., van Kreveld M., Löffler M. (2017) On Measures for Groups of Trajectories. In: Bregt A., Sarjakoski T., van Lammeren R., Rip F. (eds) Societal Geo-innovation. GIScience 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham

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.

TimeManager

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:

CASE 
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
END 
* ( 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’:

make_line( 
  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.

References

[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.

 

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