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Mon Jun 17 23:00:15 2019

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QGIS Planet

Funding for selective masking in QGIS is now complete!

Few months ago, Oslandia launched QGIS lab’s , a place to advertise our new ideas for QGIS, but also a place to help you find co funders to make dreams become reality.

The first initiative is about label selective masking, a feature that will allow us to achieve even more professional rendering for our maps.

Selective masking

 

Thanks to the commitment of the Swiss QGIS user group and local authorities, this work is now funded !

We now can start working hard to deliver you this great feature for QGIS 3.10

Thanks again to our funders

A last word, this is not a classical crowd funding initiative, but a classical contract for each funder.

No more reason not to contribute to free and open source software!

QGIS Print Layouts Graphs and Charts — target reached!

We’ve just passed the extended deadline for our recent QGIS Print Layouts Graphs and Charts campaign, and the great news is that thanks to a large number of generous backers we’ve successfully hit the target for this campaign! This has only been possible thanks to the tireless work of the QGIS community and user groups in promoting this campaign and spreading the word.

The Print Layouts Graphs and Charts campaign is a joint effort with our friends at Faunalia, so we’ll soon be starting work together on all the wonderful new functionality heading to the QGIS DataPlotly plugin as a result. The work will be commencing late June, just after the QGIS 3.8.0 final release. Keep an eye out for further updates on the development from this time! You can read more about what’s coming in detail at the campaign page.

We’d like to take this opportunity to extend our heartfelt thanks to all the backers who have pledged to support this project:

  • Federico Gianoli
  • Papercraft Mountains
  • Liam McCrae
  • Henry Walshaw
  • Raúl Sangonzalo
  • Ferdinando Urbano
  • pitsch-ing.ch
  • Carbon-X
  • Gabriel Diosan
  • Rene Giovanni Borella
  • Enrico Bertonati
  • Guido Ingwer
  • David Addy
  • Gerd Jünger
  • Andreas Neumann
  • Stefano Campus
  • Michael Jabot
  • Korto
  • Enrico Ferreguti
  • Carlo A. Nicolini
  • Salvatore Fiandaca
  • Alberto Grava
  • Hans van der Kwast
  • Ben Hur Pintor
  • Silvio Grosso
  • Nobusuke Iwasaki
  • Alasdair Rae
  • Manori Senanayake
  • Canton de Neuchâtel
  • Matthias Daues
  • Alteri Seculo
  • SunGIS Ltd.
  • Stu Smith
  • Keolis Rennes
  • Gabriel Diosan
  • Aiden Price
  • Giacomo Ponticelli
  • Diane Fritz
  • Gemio Bissolati
  • Claire Birnie
  • Nicolas Roelandt
  • Rocco Pispico
  • Gabriel Bengtsson
  • Birds Eye View
  • Barend Köbben
  • Roberto Marzocchi (GTER)
  • Yoichi Kayama
  • Alessandro Sarretta
  • Luca Angeli
  • Luca Bellani
  • giswelt
  • Stefan Giese
  • Ben Harding
  • Joao Gaspar
  • Romain Lacroix
  • Ryan Cooper
  • Daniele Bonaposta
  • QGIS Swedish User Group
  • Nino Formica
  • Michael Gieding
  • Amedeo Fadini
  • Andrew Hannell
  • Stefano
  • Phil Wyatt
  • Brett Edmond Carlock
  • Transitec

 

Using QGIS from Conda

QGIS recipes have been available on Conda for a while, but now, that they work for the three main operating systems, getting QGIS from Conda is s starting to become a reliable alternative to other QGIS distributions. Anyway, let’s rewind a bit…

What is Conda?

Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language.

Why is that of any relevance?

Conda provides a similar way to build, package and install QGIS (or any other software) in Linux, Windows, and Mac.

As a user, it’s the installation part that I enjoy the most. I am a Linux user, and one of the significant limitations is not having an easy way to install more than one version of QGIS on my machine (for example the latest stable version and the Long Term Release). I was able to work around that limitation by compiling QGIS myself, but with Conda, I can install as many versions as I want in a very convenient way.

The following paragraphs explain how to install QGIS using Conda. The instructions and Conda commands should be quite similar for all the operating systems.

Anaconda or miniconda?

First thing you need to do is to install the Conda packaging system. Two distributions install Conda: Anaconda and Miniconda.

TL;DR Anaconda is big (3Gb?) and installs the packaging system and a lot of useful tools, python packages, libraries, etc… . Miniconda is much smaller and installs just the packaging system, which is the bare minimum that you need to work with Conda and will allow you to selectively install the tools and packages you need. I prefer the later.

For more information, check this stack exchange answer on anaconda vs miniconda.

Download anaconda or miniconda installers for your system and follow the instructions to install it.

Windows installer is an executable, you should run it as administrator. The OSX and Linux installers are bash scripts, which means that, once downloaded, you need to run something like this to install:

bash Miniconda3-latest-Linux-x86_64.sh

Installing QGIS

Notice that the Conda tools are used in a command line terminal. Besides, on Windows, you need to use the command prompt that is installed with miniconda.

Using environments

Conda works with environments, which are similar to Python virtual environments but not limited only to python. Basically, it allows isolating different installations or setups without interfering with the rest of the system. I recommend that you always use environments. If, like me, you want to have more that one version of QGIS installed, then the use of environments is mandatory.

Creating an environment is as easy as entering the following command on the terminal:

conda create --name <name_of_the_environment>

For example,

conda create --name qgis_stable

To use an environment, you need to activate it.

conda activate qgis_stable

Your terminal prompt will show you the active environment.

(qgis_stable) aneto@oryx:~/miniconda3$

To deactivate the current environment, you run

conda deactivate

Installing packages

Installing packages using Conda is as simples as:

conda install <package_name>

Because conda packages can be stored in different channels, and because the default channels (from the anaconda service) do not contain QGIS, we need to specify the channel we want to get the package from. conda-forge is a community-driven repository of conda recipes and includes updated QGIS packages.

conda install qgis --channel conda-forge

Conda will download the latest available version of QGIS and all its dependencies installing it on the active environment.

Note: Because conda always try to install the latest version, if you want to use the QGIS LTR version, you must specify the QGIS version.

conda install qgis=3.4.8 --channel conda-forge

Uninstalling packages

Uninstalling QGIS is also easy. The quickest option is to delete the entire environment where QGIS was installed. Make sure you deactivate it first.

conda deactivate
conda env remove --name qgis_stable

Another option is to remove QGIS package manually. This is useful if you have other packages installed that you want to keep.

conda activate qgis_stable
conda remove qgis -c conda-forge

This only removes the QGIS package and will leave all other packages that were installed with it. Note that you need to specify the conda-forge channel. Otherwise, Conda will try to update some packages from the default channels during the removal process, and things may get messy.

Running QGIS

To run QGIS, in the terminal, activate the environment (if not activated already) and run the qgis command

conda activate qgis_stable
qgis

Some notes and caveats

Please be aware that QGIS packages on Conda do not provide the same level of user experience as the official Linux, Windows, and Mac packages from the QGIS.org distribution. For example, there are no desktop icons, and file association. It does not include GRASS and SAGA, etc …

On the other hand, QGIS installations on Conda it will share user configurations, installed plugins, with any other QGIs installations on your system.

(Nederlands) QGIS op de FOSS4GNL 2019 (20 juni in Delft)

Sorry, this entry is only available in the Dutch language

Movement data in GIS #23: trajectories in context

Today’s post continues where “Why you should be using PostGIS trajectories” leaves off. It’s the result of a collaboration with Eva Westermeier. I had the pleasure to supervise her internship at AIT last year and also co-supervised her Master’s thesis [0] on the topic of enriching trajectories with information about their geographic context.

Context-aware analysis of movement data is crucial for different domains and applications, from transport to ecology. While there is a wealth of data, efficient and user-friendly contextual trajectory analysis is still hampered by a lack of appropriate conceptual approaches and practical methods. (Westermeier, 2018)

Part of the work was focused on evaluating different approaches to adding context information from vector datasets to trajectories in PostGIS. For example, adding land cover context to animal movement data or adding information on anchoring and harbor areas to vessel movement data.

Classic point-based model vs. line-based model

The obvious approach is to intersect the trajectory points with context data. This is the classic point data model of contextual trajectories. It’s straightforward to add context information in the point-based model but it also generates large numbers of repeating annotations. In contrast, the line data model using, for example, PostGIS trajectories (LinestringM) is more compact since trajectories can be split into segments at context borders. This creates one annotation per segment and the individual segments are convenient to analyze (as described in part #12).

Spatio-temporal interpolation as provided by the line data model offers additional advantages for the analysis of annotated segments. Contextual segments start and end at the intersection of the trajectory linestring with context polygon borders. This means that there are no gaps like in the point-based model. Consequently, while the point-based model systematically underestimates segment length and duration, the line-based approach offers more meaningful segment length and duration measurements.

Schematic illustration of a subset of an annotated trajectory in two context classes, a) systematic underestimation of length or duration in the point data model, b) full length or duration between context polygon borders in the line data model (source: Westermeier (2018))

Another issue of the point data model is that brief context changes may be missed or represented by just one point location. This makes it impossible to compute the length or duration of the respective context segment. (Of course, depending on the application, it can be desirable to ignore brief context changes and make the annotation process robust towards irrelevant changes.)

Schematic illustration of context annotation for brief context changes, a) and b)
two variants for the point data model, c) gapless annotation in the line data model (source: Westermeier (2018) based on Buchin et al. (2014))

Beyond annotations, context can also be considered directly in an analysis, for example, when computing distances between trajectories and contextual point objects. In this case, the point-based approach systematically overestimates the distances.

Schematic illustration of distance measurement from a trajectory to an external
object, a) point data model, b) line data model (source: Westermeier (2018))

The above examples show that there are some good reasons to dump the classic point-based model. However, the line-based model is not without its own issues.

Issues

Computing the context annotations for trajectory segments is tricky. The main issue is that ST_Intersection drops the M values. This effectively destroys our trajectories! There are ways to deal with this issue – and the corresponding SQL queries are published in the thesis (p. 38-40) – but it’s a real bummer. Basically, ST_Intersection only provides geometric output. Therefore, we need to reconstruct the temporal information in order to create usable trajectory segments.

Finally, while the line-based model is well suited to add context from other vector data, it is less useful for context data from continuous rasters but that was beyond the scope of this work.

Conclusion

After the promising results of my initial investigations into PostGIS trajectories, I was optimistic that context annotations would be a straightforward add-on. The line-based approach has multiple advantages when it comes to analyzing contextual segments. Unfortunately, generating these contextual segments is much less convenient and also slower than I had hoped. Originally, I had planned to turn this work into a plugin for the Processing toolbox but the results of this work motivated me to look into other solutions. You’ve already seen some of the outcomes in part #20 “Trajectools v1 released!”.

References

[0] Westermeier, E.M. (2018). Contextual Trajectory Modeling and Analysis. Master Thesis, Interfaculty Department of Geoinformatics, University of Salzburg.


This post is part of a series. Read more about movement data in GIS.

Slides FOSS4G 2017

Reporting back from FOSS4G 2017 in Boston, which started with the usual QGIS plugin programming workshop, this time at the Harvard University campus.

Mobile data collection with GeoPaparazzi and QGIS

Geopaparazzi 5.1.2 is a mobile app for Android which allows the user to quickly collect information on his or her surrounding area.

QGIS Instant Print Plugin

As a side product of a customer project, we’re publishing a QGIS plugin for printing maps to a file with just two mouse clicks.

Presentations at FOSS4G 2015 in Seoul

Slides from our presentations at FOSS4G 2015 in Seoul: Keynote: The QGIS project and its evolution from a desktop GIS to a GIS platform - Slides New QGIS functions for power users - Slides QGIS Plugins - From Must-Haves to insider tips - Slides Building an OpenLayers 3 map viewer with React - Slides Thanks to the organizers of this great conference! It was a pleasure to get in contact with so many users from around the world.

FOSSGIS 2019 in Dresden

Sourcepole war an der FOSSGIS 2019 in Dresden als Austeller, mit Vorträgen und einem Vektor Tile Workshop präsent.

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.

FOSSGIS 2017 in Passau

In zwei Wochen beginnt die alljährliche deutschsprachige FOSSGIS Konferenz zum Theme Open Source GIS und OpenStreetMap in Passau.

FOSSGIS 2015

Sourcepole war an der FOSSGIS 2015 in Münster unter anderem mit zwei QGIS-Vorträgen präsent: Neues von QGIS QGIS Plugins - Must-Haves, Fachlösungen und Geheimtipps

FOSS4G 2018 Dar es Salaam

This year’s FOSS4G edition took place in Dar es Salaam, Tanzania. As every year, Sourcepole was supporting this major event as a sponsor. We would like to thank for all the interesting discussions and feedback to our presentations!

Verarbeitung von Interlis-Daten mit QGIS

In der Schweiz werden amtliche Geodatenmodelle vorwiegend mit Interlis erstellt und häufig wird das Interlis-Transferformat für den offiziellen Datenaustausch vorgegeben. Das Interlis-Plugin für QGIS ermöglicht die einfache Verarbeitung von Interlis-Daten und bindet externe Java-Applikationen in die Processing-Toolbox ein.

Publish Image Tooltips with QGIS Cloud

A lot of people are using QGIS Cloud as a service with ready to use QGIS webclient. It’s very easy to publish data and share maps in this way. Publishing of georeferenced images can be done with QGIS Cloud in a few steps as well. But the main problems are: how to upload the images into the cloud database? how to manage them? how to display the results? QGIS and QGIS Cloud are offering all tools for this task.

QGIS Cloud - Speed up the loading time of the web client

QGIS Cloud is your personal geo-data infrastructure in the internet. Publish maps and data. Share geo-information with others. And all of this very easily, without server, infrastructure and expert knowledge. If you know QGIS Desktop, then you know QGIS cloud just as well. Just install the QGIS cloud plugin from the official QGIS plugin repository and you’re good to go. You can publish as many maps as you want. But the default settings of QGIS projects you like to publish via QGIS Cloud are not the best with respect to the performance of the QGIS Webclient / WMS.

Slides FOSS4G 2014

Slides from our presentations at FOSS4G 2014 in Portland/Oregon: From Nottingham to PDX: QGIS 2014 roundup State of QGIS Server Easy ETL with OGR Pirmin Kalberer (@implgeo)

Sourcepole at FOSS4G 2014 in Portland

In one week, the 2014 FOSS4G Conference will start in Portland/Oregon. Sourcepole supports this major event as a bronze sponsor. Our conference contributions: Workshop presented by Horst Düster (@moazagotl) Tuesday afternoon: QGIS Plugin Development with PyQt4 and PyQGIS Presentations by Pirmin Kalberer ((@implgeo)) Thursday, Session 2, Track 7, 13:00 - 13:25: State of QGIS Server Thursday, Session 2, Track 7, 13:30 - 13:55: From Nottingham to PDX: QGIS 2014 roundup Thursday, Session 3, Track 6, 16:25 - 13:25: Easy ETL with OGR Meet Pirmin and Horst at Sourcepole’s exhibition booth and have a look at our latest products.

Share and manage your Data with QGIS Cloud and WFS-T

A lot of people are using QGIS Cloud as a service with ready to use QGIS webclient. It’s very easy to publish data and share maps in this way. But QGIS Cloud has more power under the hood. A not so obvious feature of QGIS Cloud is the option to share your data via Web Feature Service (WFS) and manage them via Web Feature Service Transactional (WFS-T). “The basic Web Feature Service allows querying and retrieval of features.

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