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Mon Jul 24 19:45:17 2017

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QGIS Planet

Docker basics with Geodocker GeoServer

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


Movement data in GIS #6: updates from AGILE2017

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


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.

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.

 


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.

migration_raw_data

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:

migration_attribute_table

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:

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:

small_multiples_print_composer_init

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:

small_multiples_style_rules

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',
  scale_linear("weight",0,2000,0,1)
)

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 rocksandwater.net.)

<!DOCTYPE qgis_style>
<qgis_style version="0">
  <symbols/>
    <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"/>
    </colorramp>
  </colorramps>
</qgis_style>

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

small_multiples_print_composer

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

migration

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.


QGIS Atlas Tutorial – Material Design

This is a guest post by Mickael HOARAU @Oneil974

For people who are working on QGIS Atlas feature, I worked on an Atlas version of the last tutorial I have made. The difficulty level is a little bit more consequente then last tutorial but there are features that you could appreciate. So I’m happy to share with you and I hope this would be helpful.

Click to view slideshow.

You can download tutorial here:

Material Design – QGIS Atlas Tutorial

And sources here:

https://drive.google.com/file/d/0B37RnaYSMWAZUUJ2NUxhZC1TNmM/view?usp=sharing

 

PS : I’m looking for job offers, feel free to contact me on twitter @Oneil974


How to fix a broken Processing model with AttributeError: ‘NoneType’ object has no attribute ‘getCopy’

Broken Processing models are nasty and this error is particularly unpleasant:

...
File "/home/agraser/.qgis2/python/plugins/processing/modeler/
ModelerAlgorithm.py", line 110, in algorithm
self._algInstance = ModelerUtils.getAlgorithm(self.consoleName).getCopy()
AttributeError: 'NoneType' object has no attribute 'getCopy'

It shows up if you are trying to open a model in the model editor that contains an algorithm which Processing cannot find.

For example, when I upgraded to Ubuntu 16.04, installing a fresh QGIS version did not automatically install SAGA. Therefore, any model with a dependency on SAGA was broken with the above error message. Installing SAGA and restarting QGIS solves the issue.


Movement data in GIS: issues & ideas

Since I’ve started working, transport and movement data have been at the core of many of my projects. The spatial nature of movement data makes it interesting for GIScience but typical GIS tools are not a particularly good match.

Dealing with the temporal dynamics of geographic processes is one of the grand challenges for Geographic Information Science. Geographic Information Systems (GIS) and related spatial analysis methods are quite adept at handling spatial dimensions of patterns and processes, but the temporal and coupled space-time attributes of phenomena are difficult to represent and examine with contemporary GIS. (Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University)

It’s still a hot topic right now, as the variety of related publications and events illustrates. For example, just this month, there is an Animove two-week professional training course (18–30 September 2016, Max-Planck Institute for Ornithology, Lake Konstanz) as well as the GIScience 2016 Workshop on Analysis of Movement Data (27 September 2016, Montreal, Canada).

Space-time cubes and animations are classics when it comes to visualizing movement data in GIS. They can be used for some visual analysis but have their limitations, particularly when it comes to working with and trying to understand lots of data. Visualization and analysis of spatio-temporal data in GIS is further complicated by the fact that the temporal information is not standardized in most GIS data formats. (Some notable exceptions of formats that do support time by design are GPX and NetCDF but those aren’t really first-class citizens in current desktop GIS.)

Most commonly, movement data is modeled as points (x,y, and optionally z) with a timestamp, object or tracker id, and potential additional info, such as speed, status, heading, and so on. With this data model, even simple questions like “Find all tracks that start in area A and end in area B” can become a real pain in “vanilla” desktop GIS. Even if the points come with a sequence number, which makes it easy to identify the start point, getting the end point is tricky without some custom code or queries. That’s why I have been storing the points in databases in order to at least have the powers of SQL to deal with the data. Even so, most queries were still painfully complex and performance unsatisfactory.

So I reached out to the Twitterverse asking for pointers towards moving objects database extensions for PostGIS and @bitnerd, @pwramsey, @hruske, and others replied. Amongst other useful tips, they pointed me towards the new temporal support, which ships with PostGIS 2.2. It includes the following neat functions:

  • ST_IsValidTrajectory — Returns true if the geometry is a valid trajectory.
  • ST_ClosestPointOfApproach — Returns the measure at which points interpolated along two lines are closest.
  • ST_DistanceCPA — Returns the distance between closest points of approach in two trajectories.
  • ST_CPAWithin — Returns true if the trajectories’ closest points of approach are within the specified distance.

Instead of  points, these functions expect trajectories that are stored as LinestringM (or LinestringZM) where M is the time dimension. This approach makes many analyses considerably easier to handle. For example, clustering trajectory start and end locations and identifying the most common connections:

animation_clusters

(data credits: GeoLife project)

Overall, it’s an interesting and promising approach but there are still some open questions I’ll have to look into, such as: Is there an efficient way to store additional info for each location along the trajectory (e.g. instantaneous speed or other status)? How well do desktop GIS play with LinestringM data and what’s the overhead of dealing with it?


Material design map tutorial for QGIS Composer

This is a guest post by Mickael HOARAU @Oneil974

For those wishing to get a stylized map on QGIS composer, I’ve been working on a tutorial to share with you a project I’m working on. Fan of web design and GIS user since few years, I wanted to merge Material Design Style with Map composer. Here is a tutorial to show you how to make simply a Material Design Map style on QGIS.

Click to view slideshow.

You can download tutorial here:

Tutorial Material Design Map

And sources here:

Sources Material Design Map

An Atlas Powered version is coming soon!


Videos and slides from FOSSGIS & AGIT OSGeo Day

Last week I had the pleasure to attend the combined FOSSGIS, AGIT and GI_Forum conferences in Salzburg. It was a great joint event bringing together GIS user and developers from industry and academia, working with both open source and commercial GIS.

I was particularly impressed by the great FOSSGIS video team. Their tireless work makes it possible to re-watch all FOSSGIS talks (in German).

I also had the pleasure to give a few presentations. Most of all, it was an honor to give the AGIT opening keynote, which I dedicated to Open Source, Open Data & Open Science.

In addition, I also gave one talk related to an ongoing research project on pedestrian routing. It was really interesting to see that other people – in particular from the OSM community – also talked about this problem during FOSSGIS:

(For more details, please see the full paper (OA).)

To wrap up this great week, Astrid Emde, Andreas Hocevar, and myself took the chance to celebrate the 10th anniversary of OSGeo during AGIT2016 OSGeo Day.

And last but not least, I presented an update from the QGIS project with news about the 3.0 plans and a list of (highly subjective) top new features:


Slides & workshop material from #QGISConf2016

If you could not make it to Girona for this year’s QGIS user conference, here’s your chance to catch up with the many exciting presentations and workshops that made up the conference program on May 25-26th:

(Some resources are still missing but they’ll hopefully be added in the coming days.)

Update: Now you can also watch the talks online or even download them.

Thanks to everyone who was involved in making this second QGIS user conference a great experience for all participants!


Better digitizing with QGIS 2.14

Tracing button

If you are using QGIS for digitizing work, you have probably seen the 2.14 Changelog entry for Trace Digitizing. The main reason why this is a really cool new feature is that it speeds up digitizing a lot. When tracing is enabled, the digitizing tools take care to follow existing features (as configured in the snapping options). For a detailed howto and videos check Lutra’s blog.


QGIS 3.0 plans

News about the path to QGIS 3.0 …

QGIS.org blog

qgis-icon-60x60

Ok so quick spoiler here: there is no QGIS 3.0 ready yet, nor will there be a QGIS 3.0 for some time. This article provides a bit more detail on the plans for QGIS 3.0. A few weeks ago I wrote about some of the considerations for the 3.0 release, so you may want to read that first before continuing with this article as I do not cover the same ground here.

lot of consideration has gone into deciding what the approach will be for the development of QGIS 3.0. Unfortunately the first PSC vote regarding which proposal to follow was a split decision (4 for, 3 against, 1 abstention and 1 suggestion for an alternative in the discussion). During our PSC meeting this week we re-tabled the topic and eventually agreed on Jürgen Fischer’s proposal (Jürgen is a QGIS PSC Member and the QGIS Release Manager) by a much more unanimous…

View original post 1,208 more words


Quick webmaps with qgis2web

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.


What went on at FOSS4G 2015?

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:


FOSS4G specials at Packt and Locate Press

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


Using TimeManager for WMS-T layers

This is a guest post by Karolina Alexiou (aka carolinux), Anita’s collaborator on the Time Manager plugin.

As of version 2.1.5, TimeManager provides some support for stepping through WMS-T layers, a format about which Anita has written  in the past.  From the official definition, the OpenGIS® Web Map Service Interface Standard (WMS) provides a simple HTTP interface for requesting geo-registered map images from one or more distributed geospatial databases. A WMS request defines the geographic layer(s) and area of interest to be processed. The response to the request is one or more geo-registered map images (returned as JPEG, PNG, etc) that can be displayed in a browser application. QGIS can display those images as a raster layer. The WMS-T standard allows the user of the service to set a time boundary in addition to a geographical boundary with their HTTP request.

We are going to add the following url as the web map provider service: http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r-t.cgi

From QGIS, go to Layer>Add Layer>Add WMS/WMST Layer and add a new server and connect to it. For the service we have chosen, we only need to specify a name and the url.

Select the top level layer, in our case named nexrad_base_reflect and click Add. Now you have added the layer to your QGIS project.

To add it to TimeManager as well, add it as a raster with the settings from the screenshot below. Start time and end time have the values 2005-08-29:03:10:00Z and 2005-08-30:03:10:00Z respectively, which is a period which overlaps with hurricane Katrina. Now, the WMS-T standard uses a handful of different time formats, and at this time, the plugin requires you to know this format and input the start and end values in this format. If there’s interest to sponsor this feature, in the future we may get the format directly from the web service description. The web service description is an XML document (see here for an example) which, among other information, contains a section that defines the format, default time and granularity of the time dimension.

add_raster

If we set the time step to 2 hours and click play, we will see that TimeManager renders each interval by querying the web map service for it, as you can see in this short video.

Querying the web service and waiting for the response takes some time. So, the plugin requires some patience for looking at this particular layer format in interactive mode. If we export the frames, however, we can get a nice result. This is an animation showing hurricane Katrina progressing over a 30 minute interval.

whoosh

If you want to sponsor further development of the Time Manager plugin, you can arrange a session with me – Karolina Alexiou – via Codementor.


A Processing model for Tanaka contours

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!


AGIT & GI_Forum 2015 wrap-up

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.

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.


QGIS 2.10 symbology feature preview

With the release of 2.10 right around the corner, it’s time to have a look at the new features this version of QGIS will bring. One area which has received a lot of development attention is layer styling. In particular, I want to point out the following new features:

1. Graduated symbol size

The graduated renderer has been expanded. Formerly, only color-graduated symbols could be created automatically. Now, it is possible to choose between color and size-graduated styles:

Screenshot 2015-06-21 18.39.25

2. Symbol size assistant

On a similar note, I’m sure you’ll enjoy the size assistant for data-defined size:

Screenshot 2015-06-21 23.16.10 Screenshot 2015-06-21 23.16.01

What’s particularly great about this feature is that it also creates a proper legend for the data-defined sizes:

Screenshot 2015-06-21 23.18.46

3. Interactive class exploration and definition

Another great addition to the graduated renderer dialog is the histogram tab which visualizes the distribution of values as well as the defined class borders. Additionally, the user can interactively change the classes by moving the class borders:

Screenshot 2015-06-21 18.43.09

4. Live layer effects

Since Nyall’s crowd funding initiative for live layer effects was a resounding success, it is now possible to create amazing effects for your vector styles such as shadows, glow, and blur effects:

Screenshot 2015-06-21 18.45.22

I’m very much looking forward to seeing all the new map designs this enables on the QGIS map Flickr group.

Thanks to everyone who was involved in developing and funding these new features!


Routing in polygon layers? Yes we can!

A few weeks ago, the city of Vienna released a great dataset: the so-called “Flächen-Mehrzweckkarte” (FMZK) is a polygon vector layer with an amazing level of detail which contains roads, buildings, sidewalk, parking lots and much more detail:

preview of the Flächen-Mehrzweckkarte

preview of the Flächen-Mehrzweckkarte

Now, of course we can use this dataset to create gorgeous maps but wouldn’t it be great to use it for analysis? One thing that has been bugging me for a while is routing for pedestrians and how it’s still pretty bad in many situations. For example, if I’d be looking for a route from the northern to the southern side of the square in the previous screenshot, the suggestions would look something like this:

Pedestrian routing in Google Maps

Pedestrian routing in Google Maps

… Great! Google wants me to walk around it …

Pedestrian routing on openstreetmap.org

Pedestrian routing on openstreetmap.org

… Openstreetmap too – but on the other side :P

Wouldn’t it be nice if we could just cross the square? There’s no reason not to. The routing graphs of OSM and Google just don’t contain a connection. Polygon datasets like the FMZK could be a solution to the issue of routing pedestrians over squares. Here’s my first attempt using GRASS r.walk:

Routing with GRASS r.walk

Routing with GRASS r.walk (Green areas are walk-friendly, yellow/orange areas are harder to cross, and red buildings are basically impassable.)

… The route crosses the square – like any sane pedestrian would.

The key steps are:

  1. Assigning pedestrian costs to different polygon classes
  2. Rasterizing the polygons
  3. Computing a cost raster for moving using r.walk
  4. Computing the route using r.drain

I’ve been using GRASS 7 for this example. GRASS 7 is not yet compatible with QGIS but it would certainly be great to have access to this functionality from within QGIS. You can help make this happen by supporting the crowdfunding initiative for the GRASS plugin update.


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