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In this post, we use TimeManager to visualize the position of a moving object over time along a trajectory. This is another example of what is possible thanks to QGIS’ geometry generator feature. The result can look like this:

What makes this approach interesting is that the trajectory is stored in PostGIS as a LinestringM instead of storing individual trajectory points. So there is only one line feature loaded in QGIS:

(In part 2 of this series, we already saw how a geometry generator can be used to visualize speed along a trajectory.)

The layer is added to TimeManager using t_start and t_end attributes to define the trajectory’s temporal extent.

TimeManager exposes an animation_datetime() function which returns the current animation timestamp, that is, the timestamp that is also displayed in the TimeManager dock, as well as on the map (if we don’t explicitly disable this option).

Once TimeManager is set up, we can edit the line style to add a point marker to visualize the position of the moving object at the current animation timestamp. To do that, we interpolate the position along the trajectory segments. The first geometry generator expression splits the trajectory in its segments:

The second geometry generator expression interpolates the position on the segment that contains the current TimeManager animation time:

The WHEN statement compares the trajectory segment’s start and end times to the current TimeManager animation time. Afterwards, the line_interpolate_point function is used to draw the point marker at the correct position along the segment:

CASE 
WHEN (
m(end_point(geometry_n($geometry,@geometry_part_num)))
> second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
AND
m(start_point(geometry_n($geometry,@geometry_part_num)))
<= second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
)
THEN
line_interpolate_point( 
  geometry_n($geometry,@geometry_part_num),
  1.0 * (
    second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
	- m(start_point(geometry_n($geometry,@geometry_part_num)))
  ) / (
    m(end_point(geometry_n($geometry,@geometry_part_num)))
	- m(start_point(geometry_n($geometry,@geometry_part_num)))
  ) 
  * length(geometry_n($geometry,@geometry_part_num))
)
END

Here is the animation result for a part of the trajectory between 08:00 and 09:00:

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In a recent post, we used aggregates for labeling purposes. This time, we will use them to create a dynamic data driven style, that is, a style that automatically adjusts to the minimum and maximum values of any numeric field … and that field will be specified in a variable!

But let’s look at this step by step. (This example uses climate.shp from the QGIS sample dataset.)

Here is a basic expression for data defined symbol color using a color ramp:

Similarly, we can configure a data defined symbol size to create a style like this:

Temperatures in July

To stretch the color ramp from the attribute field’s minimum to maximum value, we can use aggregate functions:

That’s nice but if we want to be able to quickly switch to a different attribute field, we now have two expressions (one for color and one for size) to change. This can get repetitive and can be the source of errors if we miss an expression and don’t update it correctly …

To avoid these issues, we use a layer variable to store the name of the field that we want to use. Layer variables can be configured in layer properties:

Then we adjust our expression to use the layer variable. Here is where it gets a bit tricky. We cannot simply replace the field name “T_F_JUL” with our new layer variable @style_field, since this creates an invalid expression. Instead, we have to use the attribute function:

With this expression in place, we can now change the layer variable to T_M_JAN and the style automatically adjusts accordingly:

Temperatures in January

Note how the style also labels the point with the highest temperature? That’s because the style also defines an expression for the show labels option.

It is worth noting that, in most cases, temperature maps should not be styled using a color ramp that adjusts to a specific dataset’s min and max values. Instead, we would want a style with fixed value to color mapping that makes different datasets comparable. In many other use cases, however, it is very convenient to have a style that can automatically adapt to the data.

Today’s post is mostly notes-to-self about using Docker. These steps were tested on a fresh Ubuntu 17.04 install.

Install Docker as described in https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/ “Install using the repository” section.

Then add the current user to the docker user group (otherwise, all docker commands have to be prefixed with sudo)

$ sudo gpasswd -a $USER docker
$ newgrp docker

Test run the hello world image

$ docker run hello-world

For some more Docker basics, see https://github.com/docker/labs/blob/master/beginner/chapters/alpine.md.

Pull Geodocker images, for example from https://quay.io/organization/geodocker

$ docker pull quay.io/geodocker/base
$ docker pull quay.io/geodocker/geoserver

Get a list of pulled images

$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
quay.io/geodocker/geoserver latest c60753e05956 8 months ago 904MB
quay.io/geodocker/base latest 293209905a47 8 months ago 646MB

Test run quay.io/geodocker/base

$ docker run -it --rm quay.io/geodocker/base:latest java -version
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)

Run quay.io/geodocker/geoserver

$ docker run --name geoserver -e AUTHOR="Anita" \
 -d -P quay.io/geodocker/geoserver

The important options are:

-d … Run container in background and print container ID

-P … Publish all exposed ports to random ports

Check if the image is running

$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
684598b57868 quay.io/geodocker/geoserver "/opt/tomcat/bin/c..." 
2 hours ago Up 2 hours 0.0.0.0:32772->9090/tcp geoserver

You can also check which ports to access using

$ docker port geoserver
9090/tcp -> 0.0.0.0:32772

Geoserver should now run on http://localhost:32772/geoserver/ (user=admin, password=geoserver)

For more tests, let’s connect to Geoserver from QGIS

All default example layers are listed

and can be loaded into QGIS

In the previous post, I demonstrated the aggregation support in QGIS expressions. Another popular request is to aggregate or cluster point features that are close to each other. If you have been following the QGIS project on mailing list or social media, you probably remember the successful cluster renderer crowd-funding campaign by North Road.

The point cluster renderer is implemented and can be tested in the current developer version. The renderer is highly customizable, for example, by styling the cluster symbol and adjusting the distance between points that should be in the same cluster:

Beyond this basic use case, the point cluster renderer can also be combined with categorized visualizations and clusters symbols can be colored in the corresponding category color and scaled by cluster size, as demoed in this video by the developer Nyall Dawson:

In the past, aggregating field values was reserved to databases, virtual layers, or dedicated plugins, but since QGIS 2.16, there is a way to compute aggregates directly in QGIS expressions. This means that we can compute sums, means, counts, minimum and maximum values and more!

Here’s a quick tutorial to get you started:

Load the airports from the QGIS sample dataset. We’ll use the elevation values in the ELEV field for the following examples:

QGIS sample airport dataset – categorized by USE attribute

The most straightforward expressions are those that only have one parameter: the name of the field that should be aggregated, for example:

mean(ELEV)

We can also add a second parameter: a group-by field, for example, to group by the airport usage type, we use:

mean(ELEV,USE)

To top it all off, we can add a third parameter: a filter expression, for example, to show only military airports, we use:

mean(ELEV,USE,USE='Military')

Last but not least, all this aggregating goodness also works across layers! For example, here is the Alaska layer labeled with the airport layer feature count:

aggregate('airports','count',"ID")

If you are using relations, you can even go one step further and calculate aggregates on feature relations.

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 

When editing geometries, the node tool now behaves more like editing tools in webmaps: instead of double-clicking to add a new node, the tool automatically suggests a new node when the cursor hovers over a line segment.

In addition, improvements include an 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 left 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!

 

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

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