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QGIS Planet

QGIS Print Layouts Graphs and Charts — Beta Out Now!

Thanks to the success of our recent QGIS Print Layouts Graphs and Charts crowdfunding campaign, staff at North Road and Faunalia have been busy updating and improving the QGIS “DataPlotly” plugin with the goal of allowing beautiful charts inside your print layouts.

We’re super-excited to announce that the beta release of this new functionality is now available! With this beta installed, you’ll see a new icon in your QGIS Print Layout designer window:

Clicking this button will allow you to draw a new plot onto your print layout, just like you would any other standard layout item (like legends, scalebars, etc). Any print layout chart can be customised by right-clicking the chart and selecting “Item Properties“. This will open a panel with settings like position, size, frame, and other standard options. All the magic happens when you click the “Setup Plot” button inside this panel:

This exposes the vast array of styling and charting options available for use. If you’re an existing user of the DataPlotly QGIS plugin, you’ll recognise that these are the same settings you have available when creating interactive plots alongside the main map canvas. Every setting is now available for use in print layouts!

 

To grab the beta, head over to https://github.com/ghtmtt/DataPlotly/releases/tag/v3.9-beta and download the DataPlotly.zip file. Then, inside QGIS, select the Manage and Install Plugins option from the Plugins menu. Click on the “Install from ZIP” section, and point the dialog at your downloaded DataPlotly.zip file. Click “Install Plugin“, and then restart QGIS for good measure. When QGIS restarts you should see the new chart icon inside the print layout designer.

Note that you’ll need a recent QGIS release for this to work correctly — either QGIS 3.8.3 or 3.4.12. (The print layout functionality may not be compatible with earlier releases, as we’ve had to fix several issues inside QGIS itself to get this feature working as designed!). 

We are actively seeking feedback and user testing on this beta release. If you encounter any issues, just head over to https://github.com/ghtmtt/DataPlotly/issues and let us know.

We’ll be further refining this functionality, with the goal of releasing the final non-beta version of the plugin to coincide with the upcoming QGIS 3.10 release.

Happy charting!

PDAL 2.0.1 packaged for Fedora including vertical datums and grids

Cologne city shown as colorized 3D point cloud (data source: openNRW Germany)The latest PDAL release (Point Data Abstraction Library, http://www.pdal.io/, version 2.0.1) has now been packaged for Fedora Linux.
I have cleaned up the dependencies (also the annoying former installation bug with PDAL-devel has been resolved).

The installation is as simple as this (the repository is located at Fedora’s COPR):

# enable and install PDAL
sudo dnf copr enable neteler/pdal
sudo dnf install PDAL PDAL-libs PDAL-vdatums

# if you want to compile other software like GRASS GIS with PDAL support, then you also need
sudo dnf install PDAL-devel
# Now, run PDAL:
pdal-config --version
pdal --help

Enjoy!

The post PDAL 2.0.1 packaged for Fedora including vertical datums and grids appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

Movement data in GIS #24: MovingPandas hands-on tutorials

Last week, I had the pleasure to give a movement data analysis workshop at the OpenGeoHub summer school at the University of Münster in Germany. The workshop materials consist of three Jupyter notebooks that have been designed to also support self-study outside of a workshop setting. So you can try them out as well!

All materials are available on Github:

  • Tutorial 0 provides an introduction to the MovingPandas Trajectory class.
  • Tutorials 1 and 2 provide examples with real-world datasets covering one day of ship movement near Gothenburg and multiple years of gull migration, respectively.

Here’s a quick preview of the bird migration data analysis tutorial (click for full size):

Tutorial 2: Bird migration data analysis

You can run all three Jupyter notebooks online using MyBinder (no installations required).

Alternatively or if you want to dig deeper: installation instructions are available on movingpandas.org

The OpenGeoHub summer school this year had a strong focus on spatial analysis with R and GRASS (sometimes mixing those two together). It was great to meet @mdsumner (author of R trip) and @edzerpebesma (author of R trajectories) for what might have well been the ultimate movement data libraries geek fest. In the ultimate R / Python cross-over,  0_getting_started.Rmd

Both talks and workshops have been recorded. Here’s the introduction:

and this is the full workshop recording:

QGIS 3.10 Loves GeoPDF!

Recently, we’ve been working on an exciting development which is coming soon in QGIS 3.10… support for Geospatial PDF exports! This has been a long-desired feature for many QGIS users, and it was only made possible thanks to a group of financial backers (listed below). In this post, we’re going to explore these new features and how they improve your QGIS PDF outputs.

Geospatial PDFs can now be created either by exporting the main QGIS map canvas, or by creating and exporting a custom print layout. For instance, when you select the “Save Map as PDF” option from the main QGIS window, you’ll see a new group of Geospatial PDF related options:

At its most basic, Geospatial PDF is a standard extension to the PDF format which allows for vector spatial datasets to be embedded in PDF files. If the “Include vector feature information” checkbox is ticked when creating a Geospatial PDF output, then QGIS will automatically include all the geometry and attribute information from features visible within the page. So if we export a simple map to PDF, we’ll get an output file which looks just like any old regular PDF map output…

…but, we can also pull this PDF back into QGIS and treat it just like any other vector data source! In the screenshot below we’re using the Identify tool to query on of the polygons and see all the attribute information from the original source layer.

This ability adds a lot of value to PDF exports. Anyone who has ever been supplied a non-spatial PDF as a “spatial dataset” will attest to the frustrations these cause… but if you create proper Geospatial PDFs instead, then there’s no loss of the valuable underlying spatial information or feature attributes! Furthermore, if these PDFs are opened within Acrobat Reader, tools are enabled which allow users to query features interactively.

Another nice benefit which comes with Geospatial PDF output is that layers can be interactively toggled on or off in the PDF viewer. The screenshot below shows a Geospatial PDF file created from a simple QGIS map. On the left we have a list of the layers in the PDF, each of which can be turned on or off inside the PDF viewer!

The really nice thing here is that, thanks to the underlying smarts in the GDAL library which is responsible for the actual Geospatial PDF creation, the PDF renders identically to our original QGIS map. While labels turn on and off alongside their corresponding map layer, they are still correctly stacked in the exact same way as you see in the QGIS window. Furthermore, the created PDFs keep labels and vector features as vector artwork… so there’s absolutely no loss in quality when zooming in to the map! These files look GREAT!

On that same note… the sponsorship allowed us to tackle another related issue, which is that in previous QGIS versions PDF (or SVG) exports would always export every single vertex from any visible feature! Ouch! This meant that if you had a complex polygon boundary layer, you would potentially be creating a PDF with millions of vertices per feature, even though most of these would be overlapping and completely redundant at the exported map’s scale. Now, QGIS automatically simplifies vector features while exporting them (using an appropriate, effectively invisible, level of simplification). The dramatically reduces the created file sizes and speeds up opening them and navigating them in other applications (especially Inkscape). (There’s an option at export time to disable this simplification, if you have a specific reason to!).

Creating Geospatial PDFs from print layouts gives even more options. For a start, whenever a print layout is exported to Geospatial PDFs, we ensure that the created PDF correctly handles stacking of layers alongside any other print layout items you have. In the image below we see a custom print layout which includes interactive layer visibility controls. If a layer is toggled, it’s hidden only from the map item — all the other surrounding elements like the title, north arrow and scalebar remain visible:

That’s not all though! When exporting a print layout to Geospatial PDF, QGIS also hooks into any map themes you’ve setup in your project. If you select to include these themes in your output, then the result is magical! The screenshot below shows the export options for a project with a number of themes, and we’ve chosen to export these themes in the PDF:

Opening the resultant PDF shows that our layer control on the left now lists the map themes instead of individual layers. Viewers can switch between these themes, changing the visibility of layers and their styling to match the QGIS map theme from the project! Additionally, you can even expand out a theme and expose layer-by-layer visibility control. Wow! This means you could create a single PDF output file which includes an environmental, social, cadastral, transport, …. view of your map, all in the one file.

Lastly, there’s even control for fine-tuning the combination of layers which are exposed in the output PDF file and which ones should be toggled on and off together. In the screenshot below we’ve opted to group the “Aircraft” and “Roads” map layers into a single logical PDF group called “Transport”. 

The resultant PDF respects this, showing an entry in the interactive layer tree for “Transport” which toggles both the aircraft and roads layers together:

So there you go — the power of Geospatial PDF, coming your way in QGIS 3.10!

One semi-related benefit of this work is that it gave us an opportunity to rework how “layered” exports from print layouts are created. This has had a significant flow-on impact on the existing ability to create layered SVG outputs from QGIS. Previously, this was a rather fragile feature, which created SVGs with lots of issues – overlapping labels, incorrectly stacked layers, and last-but-not-least, non-descriptive layer names! Now, just like Geospatial PDF exports, the layered SVG exports correctly respect the exact look of your map, and have much more friendly, descriptive layer names:

This should significantly reduce the amount of housekeeping required when working on these layered SVG exports. 

This work was funded by:

  • Land Vorarlberg
  • Municipality of Vienna
  • Municipality of Dornbirn
  • Biodiversity Information Service for Powys and BBNP Local
  • Kanton Zug
  • Canton de Neuchâtel
  • Canton de Thurgovia

North Road are leading experts in extending the QGIS application to meet your needs. If you’d like to discuss how you can sponsor development of features or fixes which you want in QGIS, just contact us for further details!

 

 

FOSS4G 2019 Bucharest

Reporting back from the annual international FOSS4G conference, which took place in Bucharest this year.

Select by location: what about those geometric predicates?

Currently, there’s around 20 persons in Bucharest working on QGIS development during the Contributors Meeting. And because the name of the hackfest is changed from developers meeting to contributors meeting, I now feel welcome too (as a non-coding contributor). So what can I do, as a non-coding QGIS fan? Write documentation! I just started with … Continue reading Select by location: what about those geometric predicates?

Introducing new QGIS macOS packages

We now have signed packages for macOS. You can find these packages published on the official QGIS download page at http://download.qgis.org.

Rationale

In addition to being a very powerful and user-friendly open source GIS application, QGIS can be installed on different operating systems: MS Windows, macOS, various flavours of Linux and FreeBSD. 

Volunteers help with generating the installers for those platforms. The work is highly valuable and the scale of effort put into packaging over the years is often underappreciated. QGIS has also grown significantly over the years and so has its complexity to package relevant libraries and 3rd party tools to the end-users.

QGIS has been packaged on OSX/macOS for many years, making it one of the few GIS applications you can use on this platform. This is largely thanks to the tireless work of William Kyngesburye (https://www.kyngchaos.com/software/qgis/) who has shouldered the task of compiling QGIS and its dependencies and offering them as disk images on the official QGIS website. The packages for each new release are available within days for all supported macOS versions.

Unlike most other operating systems, macOS can only be run on Apple hardware. This is a barrier for developers on other platforms who wish to compile and test their code on macOS. For other platforms, QGIS developers have automated packaging, not only for the major releases but also for daily code snapshots (aka nightly or master builds). Availability of the daily packages has allowed testers to identify platform-specific issues, well before the official release.

Apple also has a system of software signing so that users can verify if the packages are securely generated and signed by the developers. Up until now, signed macOS packages were not available, resulting in users who are installing QGIS needing to go into their security preferences and manually allow the QGIS application to be run. 

A new approach

In October 2018, Lutra Consulting started their work on packaging QGIS for macOS. The work has been based on OSGeo tap on Homebrew. Homebrew is a ‘bleeding edge’ package manager similar to those provided by Gentoo or Arch Linux. The packages by Lutra bundle the various libraries and resources on which QGIS depends into a single QGIS.app application bundle.  The packages were made available in late 2018 for QGIS official releases and master. QGIS Mac users have eagerly tested and reported various issues and the majority of them were resolved in early 2019.

Following the successful launch of the prototype packages and in discussion with other developers, it was agreed to transfer the ownership of the packaging infrastructure and scripts (https://github.com/qgis/QGIS-Mac-Packager) to QGIS.org. Using the new infrastructure and OSGeo Apple developers certificate, all QGIS ‘disk images’ (installers) have been available since late May 2019.

What are the main difference between the new installers and the ones offered by Kyngchaos? The new installer offers:

  • 3 clicks to install: download, accept Terms & Conditionss, drop to /Application
  • All dependencies (Python, GDAL, etc)  are bundled within the disk image
  • Signed by OSGeo Apple certificate
  • Availability of nightly builds (master)
  • Scripts for bundling and packaging are available on a public repository
  • Possibility of installing multiple versions (e.g. 3.4 LTR, 3.8 and master) side-by-side

There are some known issues:

For a full list, see: https://github.com/qgis/QGIS-Mac-Packager

Further work

We hope that by providing the new installers, macOS users will have a better experience in installing and using QGIS. Ideally, with the availability of nightly builds and being more accessible to new users, more software bugs and issues will be reported and this will help to improve QGIS overall.

Maintaining and supporting macOS costs more compared with other platforms. As QGIS is one of the only viable GIS applications for macOS users in an enterprise environment, we encourage you and your organisation to become a sustaining member to help assure the continued availability and improvement of the macOS packages in the long term.

Future plans

In future we plan to migrate the packaging process to use Anaconda QGIS packages as the source for package binaries. We also would like to integrate macOS builds into the Travis-CI automated testing that happens whenever a new GitHub pull request is submitted so that we can validate that the macOS packages do not get any regressions when new features are introduced.

Conclusion

With this work, we now have nightly builds of the upcoming release (‘master’) branch available for all to use on macOS. We now have signed packages and we have an automated build infrastructure that will help to ensure that macOS users always have ready access to new versions of QGIS as they become available. You can find these packages published on the official QGIS download page at http://download.qgis.org. A huge thanks to the team at Lutra Consulting for taking this much-needed work, and to William Kyngesburye for the many years that he has contributed towards the macOS/OSX QGIS packaging effort!

 

QGIS 3.8 Zanzibar is released!

We are pleased to announce the release of QGIS 3.8 ‘Zanzibar’! Zanzibar was the location of our developer meeting before the international FOSS4G 2018 conference in Dar Es Salaam.

 

Installers for all supported operating systems are already out. QGIS 3.8 comes with tons of new features, as you can see in our visual changelog.

We would like to thank the developers, documenters, testers and all the many folks out there who volunteer their time and effort (or fund people to do so). From the QGIS community we hope you enjoy this release! If you wish to donate time, money or otherwise get involved in making QGIS more awesome, please wander along to qgis.org and lend a hand!

QGIS is supported by donors and sustaining members. A current list of donors who have made financial contributions large and small to the project can be seen on our donors list. If you would like to become a sustaining member, please visit our page for sustaining members for details. Your support helps us fund our six monthly developer meetings, maintain project infrastructure and fund bug fixing efforts.

QGIS is Free software and you are under no obligation to pay anything to use it – in fact we want to encourage people far and wide to use it regardless of what your financial or social status is – we believe empowering people with spatial decision making tools will result in a better society for all of humanity.

(Fr) Oslandia recrute : développeur(se) C++ et Python

Sorry, this entry is only available in French.

Five QGIS network analysis toolboxes for routing and isochrones

In the past, network analysis capabilities in QGIS were rather limited or not straight-forward to use. This has changed! In QGIS 3.x, we now have a wide range of network analysis tools, both for use case where you want to use your own network data, as well as use cases where you don’t have access to appropriate data or just prefer to use an existing service.

This blog post aims to provide an overview of the options:

  1. Based on local network data
    1. Default QGIS Processing network analysis tools
    2. QNEAT3 plugin
  2. Based on web services
    1. Hqgis plugin (HERE)
    2. ORS Tools plugin (openrouteservice.org)
    3. TravelTime platform plugin (TravelTime platform)

All five options provide Processing toolbox integration but not at the same level.

If you are a regular reader of this blog, you’re probably also aware of the pgRoutingLayer plugin. However, I’m not including it in this list due to its dependency on PostGIS and its pgRouting extension.

Processing network analysis tools

The default Processing network analysis tools are provided out of the box. They provide functionality to compute least cost paths and service areas (distance or time) based on your own network data. Inputs can be individual points or layers of points:

The service area tools return reachable edges and / or nodes rather than a service area polygon:

QNEAT3 plugin

The QNEAT3 (short for Qgis Network Analysis Toolbox 3) Plugin aims to provide sophisticated QGIS Processing-Toolbox algorithms in the field of network analysis. QNEAT3 is integrated in the QGIS3 Processing Framework. It offers algorithms that range from simple shortest path solving to more complex tasks like Iso-Area (aka service areas, accessibility polygons) and OD-Matrix (Origin-Destination-Matrix) computation.

QNEAT3 is an alternative for use case where you want to use your own network data.

For more details see the QNEAT3 documentation at: https://root676.github.io/index.html

Hqgis plugin

Access the HERE API from inside QGIS using your own HERE-API key. Currently supports Geocoding, Routing, POI-search and isochrone analysis.

Hqgis currently does not expose all its functionality to the Processing toolbox:

Instead, the full set of functionality is provided through the plugin GUI:

This plugin requires a HERE API key.

ORS Tools plugin

ORS Tools provides access to most of the functions of openrouteservice.org, based on OpenStreetMap. The tool set includes routing, isochrones and matrix calculations, either interactive in the map canvas or from point files within the processing framework. Extensive attributes are set for output files, incl. duration, length and start/end locations.

ORS Tools is based on OSM data. However, using this plugin still requires an openrouteservice.org API key.

TravelTime platform plugin

This plugin adds a toolbar and processing algorithms allowing to query the TravelTime platform API directly from QGIS. The TravelTime platform API allows to obtain polygons based on actual travel time using several transport modes rather, allowing for much more accurate results than simple distance calculations.

The TravelTime platform plugin requires a TravelTime platform API key.

For more details see: https://blog.traveltimeplatform.com/isochrone-qgis-plugin-traveltime

QGIS Grant Programme 2019 Results

We are extremely pleased to announce the winning proposals for our 2019 QGIS.ORG grant programme. Funding for the programme was sourced by you, our project donors and sponsorsNote: For more context surrounding our grant programme, please see: QGIS Grants #4: Call for Grant Proposals 2019.

The QGIS.ORG Grant Programme aims to support work from our community that would typically not be funded by client/contractor agreements. For the first time, this year we did not accept proposals for the development of new features. Instead proposals should focus on infrastructure improvements and polishing of existing features.

Voting to select the successful projects was carried out by our QGIS Voting Members. Each voting member was allowed to select up to 6 of the 10 submitted proposals by means of a ranked selection form. The full list of votes are available here (on the first sheet). The second sheet contains the calculations used to determine the winner (for full transparency). The table below summarizes the voting tallies for the proposals:

A couple of extra notes about the voting process:

  • The PSC has an ongoing program to fund documentation so elected to fund the proposal “Open documentation issues for pull requests” even if this increases the total funded amount beyond the initial budget.
  • Although the budget for the grant programme was €20,000, the total amount for the winning proposals is €22,200. This increase is possible thanks to the generous support by our donors and sponsors this year.
  • Voting was carried out based on the technical merits of the proposals and the competency of the applicants to execute on these proposals.
  • No restrictions were in place in terms of how many proposals could be submitted per person / organization, or how many proposals could be awarded to each proposing person / organization.
  • Voting was ‘blind’ (voters could not see the existing votes that had been placed).

We received 31 votes from 16 community representatives and 15 user group representatives.

On behalf of the QGIS.ORG project, I would like to thank everyone who submitted proposals for this call!

A number of interesting and useful proposal didn’t make it because of our limited budget; we encourage organizations to pick up one of their choice and sponsor it.

Proj: Select Datum Transformations for EPSG:28992

(FOR REFERENCE, TODO: TO BE UPDATED AND TRANSLATED) If you startup QGIS 3.8 / Zanzibar the first time to load a data in our national CRS (EPSG:28992) you are being presented with the following dialog: I thought it had something todo with the fact that this OSGeo4W install maybe used the newer PROJ (6.0.1), but … Continue reading Proj: Select Datum Transformations for EPSG:28992

QGISnetworklogger plugin or what are QGIS and my service talking about…

Just released a ‘new’ plugin: QGIS Network Logger, install via the plugin manager of QGIS version 3.6 or higher (https://plugins.qgis.org/plugins/qgisnetworklogger/). One of the things QGIS is pretty good in is talking to OGC services (WebMapService/WMS, WebFeatureService/WFS etc etc), QGIS even talks to Esri web services. Something what was hard in this, is that if you … Continue reading QGISnetworklogger plugin or what are QGIS and my service talking about…

QGIS 3 and performance analysis

Context

Since last year we (the QGIS communtity) have been using QGIS-Server-PerfSuite to run performance tests on a daily basis. This way, we’re able to monitor and avoid regressions according to some test scenarios for several QGIS Server releases (currently 2.18, 3.4, 3.6 and master branches). However, there are still many questions about performance from a general point of view:

  • What is the performance of QGIS Server compared to QGIS Desktop?
  • What are the implications of feature simplification for polygons and lines?
  • Does the symbology have a strong impact on performance and in which proportion?

Of course, it’s a broad and complex topic because of the numerous possibilities offered by the rendering engine of QGIS. In this article we’ll look at typical use cases with geometries coming from a PostgreSQL database.

Methodology

The first way to monitor performance is to measure the rendering time. To do so, the Map canvas refreshis activated in the Settings of QGIS Desktop. In this way we can get the rendering time from within the Rendering tab of log messages in QGIS Desktop, as well as from log messages written by QGIS Server.

The rendering time retrieved with this method allows to get the total amount of time spent in rendering for each layer (see the source code).

But in the case of QGIS Server another interesting measure is the total time spent for a specific request, which may be read from log messages too. There are indeed more operations achieved for a single WMS request than a simple rendering in QGIS Desktop:

The rendering time extracted from QGIS Desktop corresponds to the core rendering time displayed in the sequence diagram above. Moreover, to be perfectly comparable, the rendering engine must be configured in the same way in both cases. In this way, and thanks to PyQGIS API, we can retrieve the necessary information from the Python console in QGIS Desktop, like the extent or the canvas size, in order to configure the GetMap WMS request with the appropriate WIDTH,, HEIGHT , and BBOX parameters.

Another way to examine the performance is to use a profiler in order to inspect stack traces. These traces may be represented as a FlameGraph. In this case, debug symbols are necessary, meaning that the rendering time is not representative anymore. Indeed, QGIS has to be compiled in Debug mode.

Polygons

For these tests we use the same dataset as that for the daily performance tests, which is a layer of polygons with 282,776 features.

Feature simplification deactivated

Let’s first have a look at the rendering time and the FlameGraph when the simplification is deactivated. In QGIS Desktop, the mean rendering time is 2591 ms. Using to the PyQGIS API we are able to get the extent and the size of the map to render the map again but using a GetMap WMS request this time.

In this case, the rendering time is 2469 ms and the total request time is 2540 ms. For the record, the first GetMap request is ignored because in this case, the whole QGIS project is read and cached, meaning that the total request time is much higher. But according to those results, the rendering time for QGIS Desktop and QGIS Server are utterly similar, which makes sense considering that the same rendering engine is used, but it is still very reassuring :).

Now, let’s take a look to the FlameGraph to detect where most of the time is spent.

 

Undoubtedly the FlameGraph’s are similar in both cases, meaning that if we want to improve the performance of QGIS Server we need to improve the performance of the core rendering engine, also used in QGIS Desktop. In our case the main method is QgsMapRendererParallelJob::renderLayerStatic where most of the time is spent in:

Methods Desktop % Server %
QgsExpressionContext::setFeature 6.39 6.82
QgsFeatureIterator::nextFeature 28.77 28.41
QgsFeatureRenderer::renderFeature 29.01 27.05

Basically, it may be simplified like:

Clearly, the rendering takes about 30% of the total amount of time. In this case geometry simplification could potentially help.

Feature simplification activated

Geometry simplification, available for both polygons and lines layers, may be activated and configured through layer’s Properties in the Rendering tab. Several parameters may be set:

  • Simplification may be deactivated
  • Threshold for a more drastic simplification
  • Algorithm
  • Provider simplification
  • Scale

Once the simplification activated, we varied the threshold as well as the algorithm in order to detect performance jumps:

The following conclusions can be drawn:

  • The Visvalingam algorithm should be avoided because it begins to be efficient with a high threshold, meaning a significant lack of precision in geometries
  • The ideal threshold for Snap To Grid and Distance algorithms seems to be 1.05. Indeed, considering that it’s a very low threshold, the precision of geometries is still pretty good for a major improvement in rendering time though

For now, these tests have been run on the full extent of the layer. However, we still have a Maximum scale parameter to test, so we’ve decreased the scale of the layer:

And in this case, results are pretty interesting too:

Several conclusions can be drawn:

  • Visvalingam algorithm should be avoided at low scale too
  • Snap To Grid seems counter-productive at low scale
  • Distance algorithm seems to be a good option

Lines

For these tests we also use the same dataset as that for daily performance tests, which is a layer of lines with 125,782 features.

Feature simplification activated

In the same way as for polygons we have tested the effect of the geometric simplification on the rendering time, as well as algorithms and thresholds:

In this case we have exactly the same conclusion as for polygons: the Distance algorithm should be preferred with a threshold of 1.05.

For QGIS Server the mean rendering time is about 1180 ms with geometry simplification compared to 1108 ms for QGIS Desktop, which is totally consistent. And looking at the FlameGraph we note that once again most of the time is spent in accessing the PostgreSQL database (about 30%) and rendering features (about 40%).

 

 

 

 

 

Symbology

Another parameter which has an obvious impact on performance is the symbology used to draw the layers. Some features are known to be time consuming, but we’ve felt that a a thorough study was necessary to verify it.

 

Firstly, we’ve studied the influence of the width as well as the Single Symbol type on the rendering time.

Some points are noteworthy:

Simple Line is clearly the less time consuming

– Beyond the default 0.26 line width, rendering time begins to raise consequently with a clear jump in performance

 

Another interesting feature is the Draw effects option, allowing to add some fancy effects (shadow, glow, …).

However, this feature is known to be particularly CPU consuming. Actually, rendering all the 125,782 lines took so long that we had to to change to a lower scale, with just some a few dozen lines. Results are unequivocal:

 

The last thing we wanted to test for symbology is the effect of the Categorized classification. Here are the results for some classifications with geometry simplification activated:

  • No classification: 1108 ms
  • A simple classification using the column “classification” (8 symbols): 1148 ms
  • A classification based on a stupid expression “classification x 3″ (8 symbols): 1261 ms
  • A classification based on string comparison “toponyme like ‘Ruisseau*'” (2 symbols): 1380 ms
  • A classification with a specific width line for each category (8 symbols): 1850 ms

Considering that a simple classification does not add an excessive extra-cost, it seems that the classification process itself is not very time consuming. However, as soon as an expression is used, we can observe a slight jump in performance.

Labeling

Another important part to study regarding performance is labeling and the underlying positioning. For this test we decreased the scale and varied the Placement parameter without tuning anything.

Clearly, the parallel labeling is much more time consuming than the other placements. However, as previously stated, we used the default parameters for each positioning, meaning that the number of labels really drawn on the map differs from a placement to another.

Points

The last kind of geometries we have to study is points. Similarly to polygons and lines, we used the same dataset as that of performance tests, that is a layer with 435588 points.

In the case of points geometries geometry simplification is of course not available. So we are going to focus on symbology and the impact of marker size.

Obviously Font Marker must be used carefully because of the underlying jump in performance, as well as SVG Symbols. Moreover, contrary to Simple Marker, an increase of the size implies a drastic augmentation in time rendering.

General conclusion

Based on this factual study, several conclusions can be drawn.

Globally, FlameGraph for QGIS Desktop and QGIS Server are completely similar as well as rendering time.

It means that if we want to improve the performance of QGIS Server, we have to work on the desktop configuration and the rendering engine of the QGIS core library.

Extracting generic conclusions from our tests is very difficult, because it clearly depends on the underlying data. But let’s try to suggest some recommendations :).

Firstly, geometry simplification seems pretty efficient with lines and polygons as soon as the algorithm is chosen cautiously, and as long as your features include many vertices. It seems that the Distance algorithm with a 1.05 threshold is a good choice, with both high and low scale. However, it’s not a magic solution!

Secondly, a special care is needed with regards to symbology. Indeed, in some cases, a clear jump in performance is notable. For example, fancy effects and Font Marker SVG Symbol have to be used with caution if you’re picky on rendering time.

Thirdly, we have to be aware of the extra cost caused by labeling, especially the Parallel  placement for line geometries. On this subject, a not very well-known parameter allows to drastically reduce labeling time: the PAL candidates option. Actually, we may decrease the labeling time by reducing the number of candidates. For an explicit use case, you can take a look at the daily reports.

In any case, improving server performance in a substantial way means improving the QGIS core library directly.

Especially, we noticed thanks to FlameGraph that most of the time is spent in drawing features and managing the data from the PostgreSQL database. By the way, a legitimate question is: “How much time do we spend on waiting for the database?”. To be continued 😉

If you hit performance issues on your specific configuration or want to improve QGIS awesomeness, we provide a unique QGIS support offer at http://qgis.oslandia.com/ thanks to our team of specialists!

New Year’s present – QField 1.0 RC1

It was a long and winding road but we are very excited to announce the general availability of QField 1.0 Release Candidate 1.

Packed with loads of useful features like online and offline features digitizing, geometry and attributes editing, attribute search, powerful forms, theme switching, GPS support, camera integration and much more, QField is the powerful tool for those who need to edit on the go and would like to avoid standing in the swamp with a laptop or paper charts.

With a slick user interface, QField allows using QGIS projects on tablets and mobile devices. Thanks to the QGIS rendering engine, the map-results are identical and come with the full range of styling possibilities available on the desktop.

We ask you to help us test as much as possible this Release Candidate so that we can iron out as many bugs as possible before the final release of QField 1.0.

You can easily install QField using the playstore (http://qfield.org/get), find out more on the documentation site (http://qfield.org) and report problems to our issues tracking system (http://qfield.org/issues)

QField, like QGIS, is an open source project. Everyone is welcome to contribute to make the product even better – whether it is with financial support, enthusiastic programming, translation and documentation work or visionary ideas.

If you want to help us build a better QField or QGIS, or need any services related to the whole QGIS stack don’t hesitate to contact us.

Plugin for tracking QGIS project files in git

We often have QGIS project files that are part of a customer project. To be able to manage versions of these project files or have multiple people working on it, they are managed inside a git repository.

This is however not easy, because with every save of a project file, thousands of lines change, even if the real change is minimal. Like a change of a layer name.

Current situation

This blows up the git repository for no reason. And worse: it makes it impossible to review changes, because the signal to noise ratio is horrible.

OPENGIS.ch has just released a shiny jewel to make your life easier. The Trackable QGIS Projects plugin will automatically rewrite the saved project into a much more stable format.

Understandable changes thanks to the trackable QGIS plugin

Just download the plugin, install it and you are done. No user interface available, no configuration needed.

Win a QField jump-start package, use #MyQField

Do you want to win a QField jump-start package worth 550€?

We are launching the #MyQField challenge. Follow us on Twitter and show us how you use @QFieldForQGIS by tagging your tweets with #MyQField and #dataisoutside. The tweet with most likes and retweets by 24.4.19 wins the training!

Rules

Fine boring prints:

  • Recourse to the courts is not permitted
  • There will be no correspondence regarding the competition
  • No cash payouts can be made
  • Participants have no enforceable claims to the transfer, payment or exchange of winnings

QField 1.0 is here

Let’s get straight to the point

It’s official, QField for QGIS 1.0 is out!

Get it while it’s hot on the Playstore (qfield.org/get) or on GitHub

We are incredibly pleased and proud of just having released such a jewel and are convinced that, thanks to all its features and conscious design choices, QField will make your field digitizing work much more efficient and pleasant.

Packed with loads of useful features like online and offline features digitizing, geometry and attributes editing, attribute search, powerful forms, theme switching, GPS support, camera integration and much more, QField is the powerful tool for those who need to edit on the go and would like to avoid standing in the swamp with a laptop or paper charts.

Let’s see what makes QField probably* the best mobile GIS in the world.

Work efficiently

QField focuses on efficiently getting GIS field work done and combines a minimal design with sophisticated technology to get data from the field to the office in a comfortable and easy way.

Fast and reactive

Thanks to the underlying QGIS engine and a lot of optimizations, QField is powerful and snappy. Even with complex projects, QField is a joy to work with.

Easy handling

Conscious design choices and a continuous focus on a minimal user interface drive QField’s development. This allows us to deliver a product wich is uncluttered and extremly user-friendly

Quickly digitise

Allowing a seamless digitizing experience is a paramount goal of QField. Thanks to a cleverly designed adaptive user interface and specific features like real-time attribute checks and snapping support, QField allows its users to be extremely time efficient.

Unmatched feature set

To be the best, you need to be clever but also skillful.

QField’s efficiency is matched only by its featureset that allows its users to make the most out of their fieldwork time.

Powerful cartography combined with full text search

The beauty of GIS is that maps are dynamic. Layers can individually be shown and hidden and information can be presented more or less prominently based on the task at hand. QField supports the endless styling possibilities offered by QGIS and thanks to a well placed theme switcher you can change the looks of the entire project with a single click. For even more customizability, QField allows hiding and showing layers by simply long-pressing on the layer name.

Furthermore, QField boasts a fully configurable attribute text search that will allow you to geolocate and edit that exact object you were looking for.

Geometry editing

Editing Geometries on the field is probably the most complex task an operator has to deal with. QField simplifies this process through an adaptive toolbar that appears only when necessary, snapping support and a crosshair digitizer.

Thanks to these enhancements, QField allows reducing the error rate significantly.

Support for high precision GNSS

Simple internal GPS accuracy might be enough for basic projects but cadastral surveying and other high accuracy digitizations have much higher requirements. QFields natively listens to the Android location services so it can take advantage of the best location provided by external devices.

Generate PDF

Thanks to QField’s native support for generating PDFs based on QGIS’s print layouts, your on the fly daily report map is just one click away.

Intuitive project chooser

When dealing with multiple projects, quickly being able to switch between them is key. QField comes with a beautiful file selector with favorite directories (long press on a folder to add it to the favorites and long press on the favorites list to remove it) and an automatic list of the last three opened projects that will save you heaps of time while looking for your projects.

Your data – Your decisions

QField does not impose any constraint on the data model, it is your data and you decide what they should look like and what values are acceptable. QField can enforce constraints for you and you can choose among various type of widgets to represent your data. QGIS will preconfigure some field types automatically, all you’ll have then to do is tweak the settings if you want and your project is ready for mobile prime time. Our documentation has all the information you need.

Extends your Geo Data Infrastructure seamlessly

QField uses QGIS to set up maps and forms so it automatically supports a wide variety of data formats. Thanks to this, you can comfortably prepare your project once and then deploy it everywhere. And since QGIS also has a server component, your project can be served on a WebGIS with the very same beautiful looks.

In fact you can see this exact infrastructure up and running under demo.qfield.org and with the “online_survey.qgs” project included in the QField demo projects.

Synchronize with WiFi, Cable or Network

You can synchronize your project and data (in case you are not using a centralized online database) using various methods thanks to our QFieldSync plugin.

Future cloud integration

In the near future we will add a cloud synchronization functionality, so that you will be able to seamlessly manage your project online and have them automatically deployed to your devices.

Installing and contributing

You can easily install QField using the Playstore (qfield.org/get), find out more on the documentation site (qfield.org), watch some demo videos on our channel (qfield.org/demo) and report problems to our issues tracking system (qfield.org/issues). Please note that the Playstore update can take some hours to roll out and if you had installed a version directly from GitHub, you might have to uninstall it to get the latest Playstore update.

QField, like QGIS, is an open source project. Everyone is welcome to contribute to making the product even better – whether it is with financial support, translation, documentation work, enthusiastic programming or visionary ideas.

We would like to thank our fantastic community for all the great translations, documentations, bug reports and general feedback they gave us. Thanks to all this, we were able to fix plenty of bugs, address performance issues and even add some super cool new features.

Development and deployment services

As masterminds behind QField and core contributor to QGIS, we are the perfect partner for your project. If you want to help us build a better QField or QGIS, or if you need any services related to the whole QGIS stack, don’t hesitate to contact us.

OPENGIS.ch

OPENGIS.ch helps you setting up your spatial data infrastructure based on seamlessly integrated desktop, web, and mobile components.
We support your team in planning, developing, deploying and running your infrastructure. Thanks to several senior geodata infrastructure experts, QGIS core developers and the makers of the mobile data acquisition solution QField, OPENGIS.ch has all it takes to make your project a success. OPENGIS.ch is known for its commitment to high-quality products and its continuous efforts to improve the open source ecosystem.

* We might be biased, but we do believe it

QGIS on the road

We are extremely pleased to announce the QGIS on the road tour with three free events this spring all over Switzerland. Limited places available so act fast reserve your place for the location you want.

Telling the story of Ms Maya Mielina, a retired GIS analyst and passionate beekeeper, our QGIS experts will show you features that you might not even have imagined existed in QGIS and that will allow you to dramatically increase your efficiency.

The format of the event is not that of a classic workshop but rather a prolonged presentation of extremely useful features. To keep the presentation interesting we will not focus on details or give a step by step tutorial, instead, will give you the gist of the idea thanks to our videos.

We advise you to take notes and to rewatch the published videos after the event.

QField RC5 – Last call for testing

We are really happy to announce the fifth and (hopefully) last 1.0 release candidate in QField’s history! This means that QField 1.0 is closer than ever.

Get it while it’s hot on the Playstore (https://qfield.org/get) or on GitHub

Thanks to all the feedback by the fantastic community we were able to fix plenty of bugs, address performance issues and even add some super cool new features.

New file selector

Among the new features, the most important is the flashy new file selector with favorite directories (long press on a folder to add it to the favorites and longpress on the favorites list to remove it) and an automatic list of the last three opened projects that will save you heaps of time while looking for your projects.

Another lifesaver is the newly added support for pasting text from the clipboard in the search bar. Finally, we added a smart and unobtrusive “rate this app” dialog to make it easier for you to give QField the ★★★★★ you always wanted to give it 🙂

Search functionality

List of improvements since RC3

  • New Custom file selector (#476)
  • Favorite directories in file selector (#507)
  • Recent projects in file selector (#499)
  • Ripple effect in file selector (#505)
  • Smart unobtrusive “rate this app” dialog (#510)
  • clear value in date/time if invalid when losing focus (#464)
  • fix crash when switching layer (#498)
  • Respect DPI in multiline fontsize
  • Value Map compatibility with QGIS 2 and lazy loading for performance improvements
  • Use external valuemap model
  • allow to copy text from clipboard in search bar
  • respect keep scale option in locator
  • optimize scale when searching points (#472)
  • add frame to search results
  • Update to Qt 5.12.1 (for android 6+)

You can easily install QField using the Playstore (https://qfield.org/get), find out more on the documentation site (https://qfield.org), watch some demo videos on our channel (https://qfield.org/demo) and report problems to our issues tracking system (https://qfield.org/issues). Please note that the Playstore update can take some hours to roll out and if you had installed a version directly from GitHub, you might have to uninstall it to get the latest playstore update.

QField, like QGIS, is an open source project. Everyone is welcome to contribute making the product even better – whether it is with financial support, enthusiastic programming, translation and documentation work or visionary ideas.

If you want to help us build a better QField or QGIS, or need any services related to the whole QGIS stack don’t hesitate to contact us.

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