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QGIS Planet

Forget label buffers! Better maps with selective label masks in QGIS

Cartographers use all kind of tricks to make their maps look deceptively simple. Yet, anyone who has ever tried to reproduce a cartographer’s design using only automatic GIS styling and labeling knows that the devil is in the details.

This post was motivated by Mika Hall’s retro map style.

There are a lot of things going on in this design but I want to draw your attention to the labels – and particularly their background:

Detail of Mike’s map (c) Mike Hall. You can see that the rail lines stop right before they would touch the A in Valencia (or any other letters in the surrounding labels).

This kind of effect cannot be achieved by good old label buffers because no matter which color we choose for the buffer, there will always be cases when the chosen color is not ideal, for example, when some labels are on land and some over water:

Ordinary label buffers are not always ideal.

Label masks to the rescue!

Selective label masks enable more advanced designs.

Here’s how it’s done:

Selective masking has actually been around since QGIS 3.12. There are two things we need to take care of when setting up label masks:

1. First we need to enable masks in the label settings for all labels we want to mask (for example the city labels). The mask tab is conveniently located right next to the label buffer tab:

2. Then we can go to the layers we want to apply the masks to (for example the railroads layer). Here we can configure which symbol layers should be affected by which mask:

Note: The order of steps is important here since the “Mask sources” list will be empty as long as we don’t have any label masks enabled and there is currently no help text explaining this fact.

I’m also using label masks to keep the inside of the large city markers (the ones with a star inside a circle) clear of visual clutter. In short, I’m putting a circle-shaped character, such as ◍, over the city location:

In the text tab, we can specify our one-character label and – later on – set the label opacity to zero.
To ensure that the label stays in place, pick the center placement in “Offset from Point” mode.

Once we are happy with the size and placement of this label, we can then reduce the label’s opacity to 0, enable masks, and configure the railroads layer to use this mask.

As a general rule of thumb, it makes sense to apply the masks to dark background features such as the railways, rivers, and lake outlines in our map design:

Resulting map with label masks applied to multiple labels including city and marine area labels masking out railway lines and ferry connections as well as rivers and lake outlines.

If you have never used label masks before, I strongly encourage you to give them a try next time you work on a map for public consumption because they provide this little extra touch that is often missing from GIS maps.

Happy QGISing! Make maps not war.

Official Austrian basemap and cadastre vector tiles

The BEV (Austrian Bundesamt für Eich- und Vermessungswesen) has recently published the Austrian cadastre as open data:

The URLs for vector tiles and styles can be found on https://kataster.bev.gv.at under Guide – External

The vector tile URL is:

https://kataster.bev.gv.at/tiles/{kataster | symbole}/{z}/{x}/{y}.pbf

There are 4 different style variations:

https://kataster.bev.gv.at/styles/{kataster | symbole}/style_{vermv | ortho | basic | gis}.json

When configuring the vector tiles in QGIS, we specify the desired tile and style URLs, for example:

For example, this is the “gis” style:

And this is the “basic” style:

The second vector tile source I want to mention is basemap.at. It has been around for a while, however, early versions suffered from a couple of issues that have now been resolved.

The basemap.at project provides extensive documentation on how to use the dataset in QGIS and other GIS, including manuals and sample projects:

Here’s the basic configuration: make sure to set the max zoom level to 16, otherwise, the map will not be rendered when you zoom in too far.

The level of detail is pretty impressive, even if it cannot quite keep up with the basemap raster tiles:

Vector tile details at Resselpark, Vienna
Raster basemap details at Resselpark, Vienna

Building an interactive app with geocoding in Jupyter Lab

This post aims to show you how to create quick interactive apps for prototyping and data exploration using Panel.

Specifically, the following example demos how to add geocoding functionality based on Geopy and Nominatim. As such, this example brings together tools we’ve previously touched on in Super-quick interactive data & parameter exploration and Geocoding with Geopy.

Here’s a quick preview of the resulting app in action:

To create this app, I defined a single function called my_plot which takes the address and desired buffer size as input parameters. Using Panel’s interact and servable methods, I’m then turning this function into the interactive app you’ve seen above:

import panel as pn
from geopy.geocoders import Nominatim
from utils.converting import location_to_gdf
from utils.plotting import hvplot_with_buffer

locator = Nominatim(user_agent="OGD.AT-Lab")

def my_plot(user_input="Giefinggasse 2, 1210 Wien", buffer_meters=1000):
    location = locator.geocode(user_input)
    geocoded_gdf = location_to_gdf(location, user_input)
    map_plot = hvplot_with_buffer(geocoded_gdf, buffer_meters, 
                                  title=f'Geocoded address with {buffer_meters}m buffer')
    return map_plot.opts(active_tools=['wheel_zoom']) 

kw = dict(user_input="Giefinggasse 2, 1210 Wien", buffer_meters=(0,10000))

pn.template.FastListTemplate(
    site="Panel", title="Geocoding Demo", 
    main=[pn.interact(my_plot, **kw)]
).servable();

You can find the full notebook in the OGD.AT Lab repository or run this notebook directly on MyBinder:

To open the Panel preview, press the green Panel button in the Jupyter Lab toolbar:

I really enjoy building spatial data exploration apps this way, because I can start off with a Jupyter notebook and – once I’m happy with the functionality – turn it into a pretty app that provides a user-friendly exterior and hides the underlying complexity that might scare away stakeholders.

Give it a try and share your own adventures. I’d love to see what you come up with.

GRASS GIS 8.2.0 released

The 8.2.0 release of GRASS GIS is now available with results from the GSoC 2021 and many other additions. A new grass.jupyter package is now included for interacting with Jupyter notebooks. Single window graphical user interface is available in GUI settings. r.series and three other modules are newly parallelized. Additionally, the release includes a series of scripting, packaging, and reproducibility improvements.

For all 220+ changes, see our detailed announcement with the full contributors and list of features and bugs fixed at GitHub / Releases / 8.2.0. Special thanks to GSoC students, their mentors, and first-time contributors!

Packages and installers are now available for Windows, macOS, Debian, Fedora, and Gentoo with more coming soon.

See more at grass.osgeo.org / News.

The post GRASS GIS 8.2.0 released appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

Dynamic Infographic Map Tutorial

This is a guest post by Mickael HOARAU @Oneil974

As an update of the tutorial from previous years, I created a tutorial showing how to make a simple and dynamic color map with charts in QGIS.

In this tutorial you can see some of interesting features of QGIS and its community plugins. Here you’ll see variables, expressions, filters, QuickOSM and DataPlotly plugins and much more. You just need to use QGIS 3.24 Tisler version.

Here is the tutorial.

New OGC Moving Features JSON support in MovingPandas

First time, we talked about the OGC Moving Features standard in a post from 2017. Back then, we looked at the proposed standard way to encode trajectories in CSV and discussed its issues. Since then, the Moving Features working group at OGC has not been idle. Besides the CSV and XML encodings, they have designed a new JSON encoding that addresses many of the downsides of the previous two. You can read more about this in our 2020 preprint “From Simple Features to Moving Features and Beyond”.

Basically Moving Features JSON (MF-JSON) is heavily inspired by GeoJSON and it comes with a bunch of mandatory and optional key/value pairs. There is support for static properties as well as dynamic temporal properties and, of course, temporal geometries (yes geometries, not just points).

I think this format may have an actual chance of gaining more widespread adoption.

Image source: http://www.opengis.net/doc/BP/mf-json/1.0

Inspired by Pandas.read_csv() and GeoPandas.read_file(), I’ve started implementing a read_mf_json() function in MovingPandas. So far, it supports basic MovingFeature JSONs with MovingPoint geometry:

You’ll need to use the current development version to test this feature.

Next steps will be MovingFeatureCollection JSONs and support for static as well as temporal properties. We’ll have to see if MovingPandas can be extended to go beyond moving point geometries. Storing moving linestrings and polygons in the GeoDataFrame will be the simple part but analytics and visualization will certainly be more tricky.

GRASS GIS 8.0.0 released! Finally…

1. Overview of changes

After more than 3 year of development the first stable release GRASS GIS 8.0.0 is available. Efforts have concentrated on making the user experience even better, providing many new useful additional functionalities to modules and further improving the graphical user interface.

Breaking news: new graphical user interface with entirely rewritten startup sequence!

This re-establishes user experience compatibility with QGIS and other connected software packages.

The GRASS GIS 8.0.0 release provides more than 1,400 fixes and improvements with respect to the release 7.8.6.

With the introduction of the semantic label raster metadata class, the temporal database was modified to version 3. Hence, to be able to read and process GRASS 7.x space-time datasets, users will be prompted to run t.upgrade. If users want to read newly created space-time datasets back in GRASS 7.x, they can run t.downgrade.

Launching the software

The user experience of the graphical user interface has been completely rewritten: no more clumsy selection screens – just enter the menu system directly! And on command line, GRASS GIS now starts versionless, i.e. as grass. Enjoy!

2. Download and detailed list of changes

Thanks to all contributors!

GRASS GIS 8.0.0RC2 contributors

The post GRASS GIS 8.0.0 released! Finally… appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

GRASS GIS 8.0.0RC2 released

1. Overview of changes

After more than 3 year of development the first stable release GRASS GIS 8.0.0 is available. Efforts have concentrated on making the user experience even better, providing many new useful additional functionalities to modules and further improving the graphical user interface.

Breaking news: new graphical user interface with entirely rewritten startup sequence!

This re-establishes user experience compatibility with QGIS and other connected software packages.

The GRASS GIS 8.0.0 release provides more than 1,300 fixes and improvements with respect to the release 7.8.6.

With the introduction of the semantic label raster metadata class, the temporal database was modified to version 3. Hence, to be able to read and process GRASS 7.x space-time datasets, users will be prompted to run t.upgrade. If users want to read newly created space-time datasets back in GRASS 7.x, they can run t.downgrade.

Launching the software

The user experience of the graphical user interface has been completely rewritten: no more clumsy selection screens – just enter the menu system directly!

And on command line, GRASS GIS now starts versionless, i.e. as grass.

2. Download and detailed list of changes

See https://github.com/OSGeo/grass/releases/tag/8.0.0RC2

Thanks to all contributors!

GRASS GIS 8.0.0RC2 contributors

The post GRASS GIS 8.0.0RC2 released appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

2021 wrap-up: baba und foi net!

What a ride.

This year has been both extremely rewarding and incredibly frustrating, sometimes both in very short succession.

I’ve finally finished my PhD dissertation and – between movement data analysis and open source and open data science talks in general – I’ve been counting over ten invited talks and conference presentations, including keynotes at FOSS4G and GI_Forum. Unfortunately, all of these were limited to virtual experiences and therefore often lacked much of the social interaction off stage that usually makes giving talks rewarding. But FOSS4G2021 was a refreshing exception to this rule:

One of the rare in-person events I attended this year was the Futurezone 2020 Award ceremony which – of course – had been postponed to 2021. The award took me completely by surprise:

This year’s focus on talks meant that there haven’t been many blog posts with original content this year, an unfortunate situation I hope to improve in 2022.

Without wanting to promise to much, there are quite a few interesting MovingPandas collaborations in the works that will hopefully result in exciting new features, demos, and tutorials:

On the plus side, with so many virtual events – from conferences to community events such as QGIS Open Days – much of the content formerly exclusively available to participants on-site have been recorded. Some worthwhile accounts and playlists include:

Happy streaming, happy new year, and – if you can – get vaccinated!

I’ll be having a nerd party.

New MovingPandas website

The last couple of days, I have been hacking away to improve the online presence of MovingPandas.

The new home page aims to be the central landing page that provides direct links to all important resources: from source code on Github, to documentation on ReadTheDocs, and – most importantly – all the tutorial and analysis example notebooks:

Additionally, all tutorial and analysis example notebooks now contain direct links to live versions on MyBinder, sources on Github and already executed pre-rendered HTML versions of the notebooks for quick browsing:

If you are using MovingPandas, I’d love to hear about it, particularly if you want to share one of your analysis examples with the community.

Open source for open spatial data science

Thanks to the FOSS4G2021 video team, all talks including my keynote are now available online.

I had the honor to be invited to give the closing keynote, talking about how open source can help open science, particularly data science:

I’m convinced that efforts towards more open data science are a worthwhile investment even if current scientific incentive structures are stacked against it.

Until incentive policies catch up, we all can help encourage more people to go the extra mile(s) by properly valuing their efforts, e.g. by celebrating and citing reproducible publications, open research datasets, and open scientific software.

Exploring ZAMG’s new open weather data

The Central Institution for Meteorology and Geodynamics (ZAMG) is Austrian’s meteorological and geophysical service. And as such, they have a large database of historical weather data which they have now made publicly available, as announced on 28th Oct 2021:

The new ZAMG Data Hub provides weather and station data, mainly in NetCDF and CSV formats:

I decided to grab a NetCDF sample from their analysis and nowcasting system INCA. I went with all available parameters for a period of one day (the data has a temporal resolution of one hour) and a bounding box around Vienna:

https://frontend.hub.zamg.ac.at/grid/d512d5b5-4e9f-4954-98b9-806acbf754f6/historical/form?anonymous=true

The loading screen of QGIS 3.22 shows the different NetCDF layers:

After adding the incal-hourly layer to QGIS, the layer styling panel provides access to the different weather parameters. We can switch between these parameters by clicking the gradient icon next to the parameter names. Here you can see the air temperature:

And because the NetCDF layer is time-aware, we can also use the QGIS Temporal Controller to step through the hourly measurements / create an animation:

Make sure to grab the latest version of QGIS to get access to all the functionality shown here.

Movement data in GIS #36: trucks from space

Can we reliably measure truck traffic from space? Compared to private transport, spatiotemporal data on freight transport is even harder to come by. Detecting trucks using remote sensing has been a promising lead for many years but often required access to pretty specialized sensors, such as TerraSAR-X. That is why I was really excited to read about a new approach that detects trucks in commonly available Sentinel-2 imagery developed by Henrik Fisser (Julius-Maximilians-University Würzburg, Germany). So I reached out to him to learn more about the possibilities this new technology opens up. 

Vehicles are visible and detectable in Sentinel-2 data if they are large and moving fast enough (image source: ESA)

To verify his truck detection results. Henrik had already used data from truck counting stations along the German autobahn network. However, these counters are quite rare and thus cannot provide full spatial coverage. Therefore we started looking for more complete reference data. Fortunately, Nikolaus Kapser at the Austrian highway corporation ASFINAG offered his help. The Austrian autobahn toll system is gantry-based. It records when a truck passes a gantry. Using the timestamp of these truck passages and the current traffic speed, it is possible to estimate truck locations at arbitrary points in time, such as the time a Sentinel-2 image was taken. This makes it possible to assess the Sentinel-2-based truck detection along the autobahn network for complete Sentinel-2 images.

Overall, Sentinel-2-based detections tend to underestimate the number of trucks. Henrik found a strong correlation (with an average r value > 0.8) between German traffic counting stations and trucks detected by the Sentinel-2 method. These counting stations were selected for their ideal characteristics, including distance from volatile traffic situations such as a high number of highway intersections. This is very different from our comparison which covers autobahn sections in and near Vienna. We therefore expected larger detection errors. However, our new Austrian analysis reaches similar results (with r values of 0.79, 0.70, and 0.86 for three different days 2020-08-28, 2020-09-22, and 2020-11-06).

Thanks to the truck reference locations provided by ASFINAG, we were also able to analyze the spatial distribution of truck detections. We decided to compare ASFINAG data (truth) and Sentinel-2-based detections using a grid based approach with a cell size of 5×5 km. Confirming Henrik’s original results, grid cells with higher detection than ground truth values are clearly in the minority. Interestingly, many cells in Vienna (at the eastern border of the image extent) exhibit rather low relative errors compared to, for example, the cells along Westautobahn (the east-west running autobahn in the center of the image extent).

Some important remarks: The Sentinel-2-based detection method only works for large vehicles moving around 50km/h or faster. It is hence less suited to detect trucks in city traffic. Additionally, trucks in tunnel sections cannot be detected. To enable a fair comparison, we therefore flagged trucks in the ground truth dataset that were located in tunnels and excluded them from the analysis. Sentinel-2 captures the region around Vienna around 10:00 o’clock in the morning. As a result, it is not possible to assess other times of day. Finally, cloud cover will reduce the accuracy. Therefore we picked images with low reported cloud cover percentage (< 5%).

It is really exciting to finally see a truck detection method that works with readily available remote sensing data because this means that it is potentially transferable to other areas of the world where no official traffic counts are available. Furthermore, this method should be in line with data protection regulations (avoiding identification of individuals and potential reconstruction of movement trajectories) thus making it possible to use and publish the resulting data without further anonymization steps.


This post was written in collaboration with Henrik Fisser (Uni Würzburg / DLR) and Nikolaus Kasper (Asfinag MSG). Keep your eyes open for upcoming detailed publications on the Sentinel-2-based method by Henrik.


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

Video recommendations from FOSDEM 2021

The Geospatial Dev Room at FOSDEM 2021 was a great event that (virtually) brought together a very diverse group of geo people.

All talk recordings are now available publicly at: fosdem.org/2021/schedule/track/geospatial

In line with the main themes of this blog, I’d particularly like to highlight the following three talks:

MoveTK: the movement toolkit A library for understanding movement by Aniket Mitra

Telegram Bot For Navigation: A perfect map app for a neighbourhood doesn’t need a map by Ilya Zverev

Spatial data exploration in Jupyter notebooks by yours truly

Powerful and gentle QField 1.8 Selma sneaked in

Get fieldwork smoothly and nimbly done despite the ice and snow outside. Collect accurate data with freehand digitizing and improved form widgets, use the data from your external GNSS receivers without any third-party apps and enjoy the pleasant usability of QField 1.8 Selma.

This year started off hi-speed for us. There’s been already a lot of coding, designing and teaching, and we’ve thrown ourselves into these things we love to do. And we published another QField release last week that I completely forgot to announce in this blog. But here it is. It’s QField 1.8, Selma. And it’s packed with cool features.

Let’s have a look.

Freehand drawing

This might be a feature that brings a lot of fun and professionalism to your work. The freehand digitizing mode allows the user to “draw” lines and polygons with the stylus pen. The mode is available for adding line/polygon features as well as for the ring tool of the geometry editor.

Together with the powerful options in the topological editing where you can snap to existing features and avoid overlaps, it’s very convenient to digitize complex shapes.

Zoom in and out

Speaking of fun. One day, a guy from the QGIS community asked us if we could implement the functionality to zoom in and zoom out like he is able to do with an app called Maps from a company named Google. I didn’t know what he meant, but he explained: Single finger double tap-and-hold zoom gesture (which allows you to zoom smoothly from anywhere on the screen). Wow! Didn’t know it before, but it’s super neat! So we made it available in QField as well.

If you are used to it, it’s quite easy. But for beginners it can be a bit difficult. So for people who are not that deft – and to keep the UX self-explanatory and simple – we also added two buttons + / – to zoom in and zoom out with just one finger. So now even a clumsy pirate with a hook instead of a hand can collect data with QField 🙂

Powerful Relation Reference Widget

Let’s be a little bit more serious and talk about how powerful the relation reference widget has become.

View and Edit selected feature

The intuitive eye icon next to the widget lets you open the form of the referenced parent feature to view and edit it.

Autocomplete mode

When auto-complete is enabled, you can easily perform a search in all available parent features.

autocomplete

With space-separated input, you can search for the beginning of multiple words in the display name of the parent features. So in this example searching for “Ma” will find the name “Mae” and “Marie” and using the second word “buck” it finds the Buckfast bees – so the entries containing both values will be listed on top.

Integration of external GNSS receivers

In case you wondered, why we did not release 1.8 Selma earlier? Because we wanted to have it feature loaded and rocket proof. And one of this cool feature is the integration of external GNSS receivers.

QField can receive and decode NMEA sentences received via Bluetooth from an external GNSS receiver (such as an EMLID Reach RS2) without the need for any third party app.

nmea

Search for paired Bluetooth devices in the device settings, connect to the external device and receive the GNSS information.

Select vertical grid shift files

In the QField settings, you can select a grid file on your mobile device by placing it in a directory named QField/proj in the main folder of the internal storage to increase the vertical location accuracy.

Postgres Config File

If you once started using PostgreSQL configuration files, you don’t want to live without them anymore. And when you use it on your PC, I’m sure you want to use it on your mobile device as well.

Define Postgresql services in a pg_service.conf file and use it on QField by placing it directly in a directory named QField in the main folder of the internal storage.

Add reload data button

The layer properties have been polished and in addition, you will find a button to reload the layer data. This is especially useful if you use WFS layers from which you need to get updates.

nmea

Register extra fonts

Also, you can add TTF and OTF font files into a directory named QField/fonts at the main folder of the internal storage to use the nice fonts you like.

fonts

How beautiful is that!

Support of new raster file formats

By the way: Many new raster file formats are supported – most notably COG. While not yet supported as remote format streamed directly from the web, it is also a high performance format if used locally

What about the cloud?

You might be one of these people eagerly waiting and always receiving the same message: Keep calm, it’s coming soon. Sorry for that. But when we do something, we do it right. And we prefer to have a stable solution than to publish half baked stuff. We are still highly busy coding, testing and promoting QFieldCloud. It’s announced for this spring / early summer.

Also, keep an eye on the @QFieldForQgis and @QFieldCloud twitter accounts to stay updated.

We ♥ our Beta Testers

The Beta Testers are our secret heroes. They report bugs and inconveniences before the normal users are bothered with them. Thanks to the Beta Testers QField is so stable. And at this point we would like to say: Thank you, test heroes!

And what do the beta testers get in return? Well, they can be the very first to try out the great new features. This is exciting and fun. So don’t hesitate. Join the beta.

In the Play Store you should find this section under the “QField for QGIS” app listing. Enjoy the feature frenzy and report the problems at qfield.org/issues

And if you wondered…

… why this release is called “Selma”. It’s of course because of the Mount Selma in Australia… And because it’s the name of my beloved cat. That’s her – Selma Eulenkopf – staring at me while I’m coding QField.

Introducing the open data analysis OGD.AT Lab

Data sourcing and preparation is one of the most time consuming tasks in many spatial analyses. Even though the Austrian data.gv.at platform already provides a central catalog, the individual datasets still vary considerably in their accessibility or readiness for use.

OGD.AT Lab is a new repository collecting Jupyter notebooks for working with Austrian Open Government Data and other auxiliary open data sources. The notebooks illustrate different use cases, including so far:

  1. Accessing geodata from the city of Vienna WFS
  2. Downloading environmental data (heat vulnerability and air quality)
  3. Geocoding addresses and getting elevation information
  4. Exploring urban movement data

Data processing and visualization are performed using Pandas, GeoPandas, and Holoviews. GeoPandas makes it straighforward to use data from WFS. Therefore, OGD.AT Lab can provide one universal gdf_from_wfs() function which takes the desired WFS layer as an argument and returns a GeoPandas.GeoDataFrame that is ready for analysis:

Many other datasets are provided as CSV files which need to be joined with spatial datasets to use them in spatial analysis. For example, the “Urban heat vulnerability index” dataset which needs to be joined to statistical areas.

 

Another issue with many CSV files is that they use German number formatting, where commas are used as a decimal separater instead of dots:

Besides file access, there are also open services provided by other developers, for example, Manfred Egger developed an elevation service that provides elevation information for any point in Austria. In combination with geocoding services, such as Nominatim, this makes is possible to, for example, find the elevation for any address in Austria:

Last but not least, the first version of the mobility notebook showcases open travel time data provided by Uber Movement:

The utility functions for data access included in this repository will continue to grow as new data sources are included. Eventually, it may make sense to extract the data access function into a dedicated library, similar to geofi (Finland) or geobr (Brazil).

If you’re aware of any interesting open datasets or services that should be included in OGD.AT, feel free to reach out here or on Github through the issue tracker or by providing a pull request.

GRASS GIS 7.8.5 released

What’s new in a nutshell Zanzibar Mapping Initiative data processed in OpenDroneMap and interpolated respecting building footprints using v.surf.icw

As a follow-up to the previous GRASS GIS 7.8.4 we have published the new release GRASS GIS 7.8.5 with more than 80 improvements. This minor release offers new wxGUI fixes across the tree. Also the addon extension manager received various stability fixes. VRT raster map with tiled raster maps can now be properly exported and imported in the native GRASS GIS raster format.

The overview of new features in the 7.8 release series is available at new features in GRASS GIS 7.8. See also our detailed announcement with the full list of changes and bugs fixed at https://trac.osgeo.org/grass/wiki/Release/7.8.5-News.

Binaries/Installer download:

Source code download:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics or in the cloud. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, Dec 2020

The post GRASS GIS 7.8.5 released appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

MapScaping podcast: GRASS GIS probably doesn’t get the attention it deserves

THE MAPSCAPING PODCAST, GRASS GIS episode

In episode 86 of MapScaping podcast, Markus Neteler talks about the functionality offered (the topological vector engine, 2D and 3D raster analysis, image processing and available programming interfaces), and whom GRASS GIS is for.

Markus is the cofounder of mundialis, a Bonn based business. He is also the Chairman of the GRASS GIS Project Steering Committee.

Enjoy the podcast and the transcript!

The post MapScaping podcast: GRASS GIS probably doesn’t get the attention it deserves appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

QField 1.7 Rockies hits the stage

Be ready for the cold weather with a smooth coordinate search, filters in the value relation widget, fancy new QML and HTML widgets, enhanced geometry editing functionalities and an expandable legend. Right when Autumn starts, QField 1.7 Rockies hits the stage.

As usual get it now on the play store or on github!

The days are getting shorter and the wind blows colder. It’s always good to be in a good company outside while getting your mapping work done. QField will be your reliable companion.

We know, QField 1.6 Qinling has only been out two months and with its amount of new features and stability improvements, it would have deserved a longer primetime. But we just couldn’t withhold you all the new great stuff we’ve been building lately.

So let’s welcome QField 1.7 Rockies. And yes, we mean THE Rockies, where QField is looking for plenty of new buddies.

Let’s have a look.

Merging features

Splitting of a feature has been possible for quite some time. Now the merging of features of multipolygon-layer is possible as well. Select them and merge them – easy like that. The first selected feature gets the new geometry and keeps its attributes.

Filters in the Value Relation Widget

The value relation widgets provide an easy selection of a related feature. Often it’s used for lookup tables but sometimes the related tables contain a lot of entries and the list of the possible values is long.

Using filters in the value relation drop-down can increase the efficiency in selecting the correct value. It can be configured by expressions in QGIS, so it’s possible to have the content of the drop down depend on the values entered previously in other fields.

In the screenshot above there is a Map Value Widget with “forest” and “meadow” as values. On selecting “forest”, only the trees appear in the Field “Plant Species”. On selecting “meadow” there would be listed flowers instead.

Go to coordinates in the Search

The search has not only been improved in its appearance, but it’s handling is much more comfortable with a button to clear the text and easy opening and closing.

Additionally, we added the possibility to jump to coordinates. Searching a place you know the coordinates of is now super simple. And this means that digitizing that precise geometry with known coordinates is finally possible.

coordinates

QML and HTML Widget

You might remember when we introduced the QML widget in QGIS. Now it’s in QField as well. And it’s not alone. HTML widgets are supported too.

This provides a lot of possibilities to display information with texts, images and charts and it even allows you interaction.
Do you need help setting up complex forms? Don’t hesitate to get in touch with us!


Expandable legend icons

The legend items are now expandable and collapsible.

Wait a minute… Wasn’t this possible before? Yes. It was possible in earlier versions. But why it’s announced here as a new feature?

Because now it is built in a future proof manner thanks to all the people and organisations who care for QField and bought a support contract with the sustainability initiative or committed to a recurring sponsorship.

Some technical background: As you may be aware QField uses QGIS under the hood and QGIS uses Qt under the hood. Qt is currently used in version 5. Qt 5 is not that young any more and has a lot of functionality which is no longer supported by Qt. The old legend was based on the tree view, a deprecated module. Using it had some implications like the suboptimal support of HiDPI. Furthermore, these deprecated modules will disappear in the soon-to-come Qt 6.

As you can see, keeping QField at the quality we and you expect requires a lot of maintenance work. It is of utmost importance and only possible thanks to sponsoring since paying for fixing already existing features is less attractive for most people.

What will the future bring

In the last weeks, we have been highly busy on coding, testing and promoting QFieldCloud and we are very happy to be able to announce it very soon. So be prepared.

Also, keep an eye on the @QFieldForQgis and @QFieldCloud twitter accounts to stay updated.

Open Source

QField is an open source project. This means that whatever is produced is available free of charge. To anyone. Forever. This also means that everyone has the chance to contribute. You can write code, but you don’t need to. You can also help translating the app to your language or help out writing documentation or case studies or by sponsoring a new feature.

And now…

… enjoy QField 1.7 Rockies and have a nice autumn!

GRASS GIS 7.8.4 released

1. What’s new in a nutshell

Processed LiDAR data in GRASS GIS (screenshot by @neteler)As a follow-up to the previous GRASS GIS 7.8.3 we have published the new release GRASS GIS 7.8.4 with more than 170 improvements. This minor release again focuses on wxGUI fixes, especially in the animation export, the layer management, 3D visualization and the data catalogue. Many display modules received fixes as well, and the vector digitizer now works as expected.

The overview of new features in the 7.8 release series is available at new features in GRASS GIS 7.8. See also our detailed announcement with the full list of changes and bugs fixed at https://trac.osgeo.org/grass/wiki/Release/7.8.4-News.

Binaries/Installer download:

Source code download:

First time users may explore the first steps tutorial after installation.

About GRASS GIS

The Geographic Resources Analysis Support System (https://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics or in the cloud. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The GRASS Development Team, Oct 2020

The post GRASS GIS 7.8.4 released appeared first on GFOSS Blog | GRASS GIS and OSGeo News.

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