Related Plugins and Tags

QGIS Planet

2.2.0 - Coordinated Capybara

Changes

🚀 Features

Building on its predecessor, QField 2.2 continues to improve its navigation functionality with a brand new precise view (i.e. stakeout) panel as well as adding the capability to cycle through feature vertices to set a destination.

22_showcase

Noteworthy changes include:

  • Navigation to feature vertices (#2997)
  • Precise view (i.e. stakeout) panel (#3003)
  • Averaged positioning functionality (#2926)
  • Large number of measuring tool improvements (#2934)
  • Displayed coordinate throughout QField now respect the opened projects' coordinate display unit type setting (#2945)
  • Layer opacity slider in the layer properties' panel (#2986)
  • Auto-setup of temporal context when opening individual datasets with a {date,datetime} field (i.e. GPX tracks) (#2991)
  • Support for animated symbology
  • Preview thumbnails while browsing local projects/datasets
  • New 'open project folder' action found in the main menu (quickly send individual cloud and non-cloud project datasets)

A number of serious issues have also been addressed in this release.

Detailed hillshading anywhere in the world!

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Create detailed hillshading anywhere on earth with the MapTiler Plugin and the latest version of QGIS, 3.26 Buenos Aires.

All you need is a free MapTiler Cloud account, version 3.26 of QGIS, and the MapTiler plugin. The plugin provides access to the Terrain RGB layer and a range of other vector and raster basemaps.

QGIS Hillshades at multiple scales

Hillshades made with the plugin look great for whole mountain ranges but are detailed enough to pick out gorges and volcanic craters. The data has a 30m resolution, and with the early resampling now available from MapTiler’s collaboration with Lutra Consulting you can now zoom in without the pixelation that occurred in earlier versions of QGIS.

Multi-scale Hillshades

Add the Terrain RGB layer to any map in QGIS and use the Hillshading renderer to make the landscape appear on your map. Hillshading can be found in the styling panel for terrain layers, just use the dropdown menu to change from the default Singleband to Hillshading.

I recommend using the following settings to make the landforms stand out:

  • Z Factor: 1.5 - to boost the effect
  • Multidirectional: tick - to make a much more realistic hillshade
  • Blending mode: Multiply - to add light and shadows without hiding the other layers
  • Brightness and Contrast: Adjust them up to suit your layers
  • Early resampling: tick - ensures the effect doesn’t pixelate as you zoom in.

Better maps for outdoor activities

The Outdoor and Winter layers available in the Plugin also benefit from the new hillshading. Not only do these layers look better than ever, you can modify the hillshading element to boost the effect or make it more subtle depending on how you want your map to look.

Multi-scale Hillshades

Global contour lines

Terrain RGB does not provide only great hillshading. You can use it for contours as well using the contour renderer.

Global contour lines

Use the same drop-down menu on the styling panel as you did with hillshading to change the rendering.

Master QGIS Hillshading Techniques at FOSS4G 2022

We will be running a workshop at FOSS4G 2022 Firenze, where you can learn more about making beautiful hillshades in QGIS: QGIS & MapTiler Workshop. As one of the sponsors of FOSS4G we also have a stand there. Make sure you drop by and find out more about our Cloud, Data, Server, and other products and opensource projects.

Setting up the plugin

Here is a quick 4 step guide to setting up the MapTiler Plugin:

  1. Get a MapTiler cloud account, they are free to set up and use for non-commercial purposes. Use the following link to sign up or sign in; the process is very simple and only takes a few minutes: Create Account.
  2. Go to your MapTiler Cloud Account page, and click Credentials on the left of the interface. Click on the New Credential button and copy the token (keep this token private – treat it the same way as a password).

MapTiler Cloud Credentials

  1. In the MapTiler plugin, paste this token into the account dialog:

MapTiler QGIS Plugin Authentication

  1. After you paste your token, it is saved in your QGIS Authentication database, which you can control using the Authentication manager in the QGIS Options… menu.

If you haven’t used QGIS Authentication manager before, QGIS will ask you for a master authentication password. The master password is used to protect all your connection details and is used by other plugins such as Mergin. You can find out more here: QGIS Docs: Authentication Overview

More about the MapTiler Plugin for QGIS

The MapTiler Plugin homepage

What is the MapTiler QGIS plugin

How to use the MapTiler plugin

MapTiler QGIS plugin - supported expressions

Book your place at the FOSS4G 2022 Workshop

QGIS 3.26 Buenos Aires is released!

We are pleased to announce the release of QGIS 3.26 ‘Buenos Aires’!

Installers for all supported operating systems are already out. QGIS 3.26 comes with tons of new features, as you can see in our visual changelog. QGIS 3.26 Buenos Aires is named after last year’s FOSS4G host city.

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.

Swiss QGIS user group Meeting Berne 2022

Learn, Present, Discuss and MEET

In Summer 2022 the Swiss QGIS User community finally got together physically again to meet at the University of Berne, after 3 years of online meetings. Up to 90 QGIS users and contributors out of academia and engineering enjoyed and discussed the newest QGIS features and use cases.

After a warm welcome and introduction by Isabel Kiefer from OPENGIS.ch the presentations started.

QGIS Update

Marco Bernasocchi (OPENGIS.ch CEO and Qgis.org Chair) presented recent QGIS features out of the changelogs of current long term release 3.22, followed by versions 3.24 and 3.26. Among the enhancements are the new curve conversion vertex tool and improvements to the mesh editing, 3D-mode, WMS server and SQL logging, to name a few.

QGIS Animation Workbench

The real world is not static. Thus, often information can be understood more easily in animated form, like visualizing traffic on a map with moving vehicles. QGIS now supports dynamic renderings with the Animation Workbench Plugin. Tim Sutton (Kartoza) led through a Youtube Video showing the underlying mechanisms of the plugin and how to use it.

QGIS Model Baker Update

Starting with the new logo, Romedi Filli (GIS-Fachstelle, Kt. Schaffhausen) showed the latest improvements to the QGIS Model Baker plugin. Especially the data validator and UsabILIty Hub make QGIS project generation out of Interlis data even easier. Even more there is now a python package for those who prefer to python script it all together.

Using QGIS Model Baker for OEREB Cadastre

Adrian Weber (Dütschler + Partner) followed up presenting the use of QGIS Model Baker in migrating their management of municipal usage plans from proprietary software to QGIS driven workflow. Though they see the potential in this approach, they lack time and money, thus doing it step by step. In providing this public service the difficulty is that data is legally binding and system components need to meet this requirements.

Dynamic Forms and Widgets with QGIS Expressions

After a coffee break, Andreas Neumann (Amt für Geoinformation, Kt. Solothurn) gave an interesting technical speech on more dynamic QGIS forms and widgets. Form values can now be defined via expressions so they update automatically depending on other form values. Furthermore, action buttons can be included into forms and defined which can call external web-services, data dependent constraints be defined and more.

Analysis of Flight Trajectories

Driven by technical ambition and will to set some factual basis for political discussion, Yvo Weidmann (Geoidee) carried out a sophisticated analysis of descents to Zurich airport based on Open Source Flight Trajectories and swisstopo data. Therefore he processed data from opensky-network.org, the Aeronautical Information Publication after a lot of initial data validation and cleaning. He finally visualized the results in a nice QGIS driven animation of flight descents.

Teksi utilities application modules

Alexandre Bosshard (Ville de Pully) presented TEKSI, an association that has set itself the task of providing the operators of public infrastructure with decision-making support in the form of professional modules for controlling their activities, namely QGEP and QWAT for the moment with more to follow. Therefore they work on open-source software built mainly on top of QGIS and PostgresSQL/PostGIS.

QGEP (by Teksi) and hydraulic analysis with SWMM

Timothée Produit (Alpnetsystem SA (IG-Group)) gave a more technical speech on their approach of managing a central database to serve both, Teksi’s waste water management tool and QGIS extension QGEP and Stormwater Management Software SWMM to carry out hydraulic analysis in Swiss Romandie. He showed the necessary database and infrastructure setup and workflow steps to create the desired product.

The new Profile Tool in QGIS Core

Nyall Dawson (North Road) led through his Youtube video about QGIS project terrain settings and how they interact with 3d maps and the new elevation profile tool, which is only possible from version 3.26. Powerfull new possibilities to process and visualize elevation and 3D geodata worth watching. Nyall joined the conference virtually after the video to answer questions from the impressed audience.

Cool Maps made with QGIS

Finally Marco Bernasocchi closed the presentations with a collection of incredibly creative QGIS results including Xmas wishes, sports statistics and human facial topology

Workshops

After a tasty lunch including a delicious cheese selection and fruitful conversations the lucky subscribers where asked to take action themselves in the four afternoon workshops. Among other interesting topics the users could get hands on working with QField and QFieldCloud or could get started with QGIS Model Baker and data validation, all tought by the experts and developers of OPENGIS.ch.

MovingPandas v0.10 released!

The latest v0.10 release is now available from conda-forge.

This release contains some really cool new algorithms:

If you have questions about using MovingPandas or just want to discuss new ideas, you’re welcome to join our recently opened discussion forum.

As always, all tutorials are available from the movingpandas-examples repository and on MyBinder:

Besides others examples, the movingpandas-examples repo contains the following tech demo: an interactive app built with Panel that demonstrates different MovingPandas stop detection parameters

To start the app, open the stopdetection-app.ipynb notebook and press the green Panel button in the Jupyter Lab toolbar:

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.

QGIS Userbase Analytics

Background

Understanding which regions QGIS is being used in, which versions are in active use, which platforms it is being used on, and how many users we have is hugely beneficial to our ability as a project to serve our users. Back in 2017 at the bi-annual QGIS hackfest in Nødebo, Denmark, we had a long discussion about key project goals and the need to better understand our user base in order to plan the future direction of the project, and allocate funding and resources to where they are needed most

Typically proprietary software vendors have ready access to detailed user data through telemetry code which they embed in their software. This telemetry code ‘phones home’ key metrics, which together with other techniques such as license sales analysis gives them a very detailed insight into their user base. The data these vendors collect is typically not shared, so their users do not benefit from being able to understand how their data is used.

For QGIS.org, having to resort to what are generally considered to be nefarious and privacy-invading techniques of siphoning user data from our users goes against the ethos we try to promote as an open project. Further, since QGIS is freely available and doesn’t require any self-registration, we do not have a user database we can consult for such analytics. Additional factors make understanding usage levels hard. For example, a single user can download a copy of a QGIS installer and distribute it to many other users, and conversely web crawlers and bots can download many copies of QGIS installers and never install them. Because of this, simply counting the number of downloads from our website does not give a useful picture of our user base.

So we needed to come up with an approach that:

  1. Does not invade our user’s privacy
  2. Does not require including telemetry code in QGIS which exfiltrates user information from their system
  3. Does not store any user-identifiable data on our servers
  4. Is open and transparent in the data collection methodology
  5. Openly shares the insights we gain from our analytics to the broader community

The most obvious privacy-respecting way we could find to understand more about our users was to collect metrics of access to the QGIS News Feed. In order to display the latest news on startup, QGIS Desktop makes a request to https://feed.qgis.org when it is opened. On the server that hosts the feed, we can then use the web server logs to understand which operating system and version of QGIS made the news feed request. Additionally, using the GeoIP library we can resolve each request to the country from which it originated. These pieces of information are included in the User-Agent headers sent by QGIS when it makes a request to the QGIS News Feed.

This process is anonymous, transparent, and simple to disable. It does not identify unique machines. Only one event is logged per unique network per hour. Only one event is logged per QGIS installation per day, and the event is only triggered when the user opens the QGIS Desktop application.

Operating system statistics are derived from QGIS version information, and no system fingerprinting or telemetry is implemented.

Location information is derived from the request source IP address, which is immediately discarded on the server after resolving it to the country of origin.

No logging on the QGIS News Feed server occurs with legacy installations that do not have the news feed feature, offline usage of QGIS, and installations for which feed collection is disabled (see below for info on how to disable it). It will also have statistics skewed in scenarios where atypical networking infrastructure is in effect, such as using a virtual private network.

Despite these caveats, the statistics should provide a good high-level overview of how QGIS is being used, such as the breakdown of QGIS across operating systems and versions – information that is incredibly useful to the QGIS developer team. Only the following four pieces of information are collected:

  • The date (aggregated by day)
  • The QGIS version
  • The Operating System
  • Country (based on IP which is immediately discarded)

Opting out

If you wish to opt-out of this data collection, simply disabling the feed retrieval, using QGIS offline, or blocking access to the QGIS RSS feed address (feed.qgis.org) on your network will exclude you from this process. QGIS Desktop provides options for disabling version checking and feed access under Settings ➔ Options ➔ General ➔ Application. Note that by default this setting is specific to each individual user profile.

Viewing the analytics

We have made a public dashboard publicly available at https://analytics.qgis.org. The dashboard was made using the fantastic open-source Metabase analytics package.

Credits: This post was written by Charles Dixon-Paver and Tim Sutton

Point cloud and QGIS 3D improvements - progress report 3

This is a part of series of blog posts to update QGIS community with the outcome of the funding we had raised during late 2021 to improve elevation and point clouds in collaboration with North Road and Hobu. For other updates see part 1 and part 2.

Profile tool

With the new integrated profile tool, you can generate cross sections of point clouds, raster, vector and mesh data. For more information on this tool, you can see the excellent video introduction by North Road who implemented this part of the project.

To be able to view profiles from different data types, there is now a dedicated Elevation settings under layer properties. Users can set the elevation source, style and some other configurations. You can then enable elevation profile widget window by going to the main menu in QGIS, View > Elevation Profile.

Elevation Profile tool in QGIS

Support for COPC

Cloud Optimized Point Cloud (COPC) is a new format for point cloud data and QGIS 3.26 comes with support for it (for both local files and data hosted on remote servers).

COPC is a very exciting addition to the ecosystem, because it is “just” a LAZ file (a format well established in the industry) that brings some interesting extra features. This means all software supporting LAZ file format will also be able to read COPC files without any extra development. If you are familiar with Cloud Optimized GeoTIFF (COG) for rasters, COPC is an extension of the same concept for point cloud data. Read more at https://copc.io/

Ordinary LAS/LAZ files have an issue that it is not possible to efficiently read a subset of data without reading the entire file. This is less of an issue when processing point cloud data, but much more important for point cloud viewers, which typically show only a small portion of the data (e.g. zoomed in to a particular object or zoomed out to show the entire dataset). For that reason, viewers need to index (pre-process) the data before being able to show it - QGIS also needs to do the indexing when a point cloud file is first loaded. The new feature that COPC brings is that data is re-organized in a way that reading just some parts of data is efficient and easy. Therefore when loading COPC files, QGIS can immediately show them without any indexing (that takes time and extra storage).

In addition to that, COPC files can be efficiently used also directly from remote servers - clients such as QGIS can only request small portions of data needed, without the need to download the entire file (that can have size of many gigabytes). This makes dissemination of point cloud data easier than before - just make COPC files available through a static server and clients are ready to stream the data.

A small note: until now, QGIS indexed point cloud files to EPT format upon first load. From QGIS 3.26 we have switched to indexing to COPC - it has the advantage of being just a single file rather than lots of small files in a directory. If you have point cloud data indexed in EPT format already, QGIS will keep using EPT index (rather than indexing also to COPC).

Display of a remote COPC file

Display of a remote COPC file

Classified renderer improvements

Classified renderer for point clouds has been improved to:

  • Show only classes that are in the dataset (instead of hard-coded list) & show also non-standard classes
  • Show percentage of points for each class
  • Work also for other attributes (return number, number of returns, point source and few other classes)

Point cloud classification

Vector transparency in 3D scene

This improvement is not part of the crowdfunding campaign and was exclusively funded by the Swedish QGIS user group, but it is somehow relevant to the audience of this blog post!

With this feature, you can set polygon transparency in 3D scenes.

3D vector transparency

Want to see more features?

We are trying to improve QGIS to handle point clouds for visualisation and analysis. If you would like certain features to be added to QGIS, do not hesitate to contact us on [email protected] with your idea(s).

Point cloud and QGIS 3D improvements - progress report 3

This is a part of series of blog posts to update QGIS community with the outcome of the funding we had raised during late 2021 to improve elevation and point clouds in collaboration with North Road and Hobu. For other updates see part 1 and part 2.

Profile tool

With the new integrated profile tool, you can generate cross sections of point clouds, raster, vector and mesh data. For more information on this tool, you can see the excellent video introduction by North Road who implemented this part of the project.

To be able to view profiles from different data types, there is now a dedicated Elevation settings under layer properties. Users can set the elevation source, style and some other configurations. You can then enable elevation profile widget window by going to the main menu in QGIS, View > Elevation Profile.

Elevation Profile tool in QGIS

Support for COPC

Cloud Optimized Point Cloud (COPC) is a new format for point cloud data and QGIS 3.26 comes with support for it (for both local files and data hosted on remote servers).

COPC is a very exciting addition to the ecosystem, because it is “just” a LAZ file (a format well established in the industry) that brings some interesting extra features. This means all software supporting LAZ file format will also be able to read COPC files without any extra development. If you are familiar with Cloud Optimized GeoTIFF (COG) for rasters, COPC is an extension of the same concept for point cloud data. Read more at https://copc.io/

Ordinary LAS/LAZ files have an issue that it is not possible to efficiently read a subset of data without reading the entire file. This is less of an issue when processing point cloud data, but much more important for point cloud viewers, which typically show only a small portion of the data (e.g. zoomed in to a particular object or zoomed out to show the entire dataset). For that reason, viewers need to index (pre-process) the data before being able to show it - QGIS also needs to do the indexing when a point cloud file is first loaded. The new feature that COPC brings is that data is re-organized in a way that reading just some parts of data is efficient and easy. Therefore when loading COPC files, QGIS can immediately show them without any indexing (that takes time and extra storage).

In addition to that, COPC files can be efficiently used also directly from remote servers - clients such as QGIS can only request small portions of data needed, without the need to download the entire file (that can have size of many gigabytes). This makes dissemination of point cloud data easier than before - just make COPC files available through a static server and clients are ready to stream the data.

A small note: until now, QGIS indexed point cloud files to EPT format upon first load. From QGIS 3.26 we have switched to indexing to COPC - it has the advantage of being just a single file rather than lots of small files in a directory. If you have point cloud data indexed in EPT format already, QGIS will keep using EPT index (rather than indexing also to COPC).

Display of a remote COPC file

Display of a remote COPC file

Classified renderer improvements

Classified renderer for point clouds has been improved to:

  • Show only classes that are in the dataset (instead of hard-coded list) & show also non-standard classes
  • Show percentage of points for each class
  • Work also for other attributes (return number, number of returns, point source and few other classes)

Point cloud classification

Vector transparency in 3D scene

This improvement is not part of the crowdfunding campaign and was exclusively funded by the Swedish QGIS user group, but it is somehow relevant to the audience of this blog post!

With this feature, you can set polygon transparency in 3D scenes.

3D vector transparency

Want to see more features?

We are trying to improve QGIS to handle point clouds for visualisation and analysis. If you would like certain features to be added to QGIS, do not hesitate to contact us on [email protected] with your idea(s).

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.

2.1.4 - Bumblebee 🐝

Changes

🐛 Bug Fixes

  • Fix longstanding regression on project file fonts loading

2.1.3 - Bumblebee 🐝

Changes

🚀 Features

  • The bookmark properties panel now offers a "copy bookmark details" button

🐛 Bug Fixes

  • Fix compass on rotated devices/screens
  • Fix saved bookmarks getting lost on upgrade
  • Fix localized path (a.k.a basemap) datasets not loaded when located on SD cards (i.e. /Android/data/ch.opengis.qfield/files/QField/basemaps/)
  • Fix PGSYSCONFIGDIR handling

2.1.0 - Bumblebee 🐝

Changes

🚀 Features in 2.1

  • Navigation to destination functionality (see documentation)
  • Bookmark addition and editing functionality (see documentation)
  • Map compass overlay
  • Copy map canvas location, current position location to clipboard (hint: long press on the map canvas)
  • Support full screen mode when running QField on Windows or OSX (hint: hit F11)
  • Support restricting image attachments to a specified maximum width/height

🐛 Bug Fixes

  • A serious crash on older arm(v7) devices has been fixed
  • Sharper graphics on low-density destkop screens
  • Updates to GDAL, PROJ, and QGIS libraries for a stabler experience

2.1.1 - Bumblebee 🐝

Changes

🐛 Bug Fixes

  • Fix virtual keyboard not showing up on first launch (on Android only)
  • Insure the "Locked Geometry" setting is respected when adding/deleting/duplicating/moving features (#2828)
  • Translations improvements

2.1.2 - Bumblebee 🐝

Changes

🐛 Bug Fixes

  • Fix broken relation editor widget view feature button

Inscribed and bounding circles in PostGIS

Today, I’m revisiting work from 2017. In Brezina, Graser & Leth (2017), we looked at different ways to determine the width of sidewalks in Vienna based on the city’s street surface database.

Image source: Brezina, Graser & Leth (2017)

Inscribed and circumscribed circles were a natural starting point. Circumscribed or bounding circle tools (the smallest circle to enclose an input polygon) have been commonly available in desktop GIS and spatial databases. Inscribed circle tools (the largest circle that fits into an input polygon) used to be less readily available. Lately, support has improved since ST_MaximumInscribedCircle has been added in PostGIS 3.1.0 (requires GEOS >= 3.9.0).

The tricky thing is that ST_MaximumInscribedCircle does not behave like ST_MinimumBoundingCircle. While the bounding circle function returns the circle geometry, the inscribed circle function returns a record containing information on the circle center and radius. Handling the resulting records involves some not so intuitive SQL.

Here is what I’ve come up with to get both the circle geometries as well as the radius values:

WITH foo AS 
(
	SELECT id, 
		ST_MaximumInscribedCircle(geom) AS inscribed_circle,
		ST_MinimumBoundingRadius(geom) AS bounding_circle
	FROM demo.sidewalks 
)
SELECT
	id,
	(bounding_circle).radius AS bounding_circle_radius,
	ST_MinimumBoundingCircle(geom) AS bounding_circle_geom, 
	(inscribed_circle).radius AS inscribed_circle_radius,
	ST_Buffer((inscribed_circle).center, (inscribed_circle).radius) AS inscribed_circle_geom
FROM foo

And here is how the results look like in QGIS, with purple shapeburst fills for bounding circles and green shapeburst fills for inscribed circles:

References

Brezina, T., Graser, A., & Leth, U. (2017). Geometric methods for estimating representative sidewalk widths applied to Vienna’s streetscape surfaces database. Journal of Geographical Systems, 19(2), 157-174, doi:10.1007/s10109-017-0245-2.

Save the date: QGIS contributor meeting in Firenze

After a long hiatus, we are happy to announce that there will be a another international QGIS Contributor Meeting in conjunction with this year’s FOSS4G in Firenze, Italy from 18 to 22 August 2022.

QGIS Contributors Meetings are volunteer-driven events where contributors to the QGIS project from around the world get together in a common space – usually a university campus. The event is normally three days in duration and we hold two such events each year. During these events, contributors to the QGIS project take the opportunity to plan their work, hold face-to-face discussions and present new improvements to the QGIS project that they have been working on. Everybody attending the event donates their time to the project for the days of the event. As a project that is built primarily through online collaboration, these meetings provide a crucial ingredient to the future development of the QGIS project. The event is planned largely as an ‘unconference’ with minimal structured programme planning. We do this to allow attendees the freedom to meet dynamically with those they encounter at the event. Those sessions that are planned are advertised on the event web page and we try to enable remote participation through video conferencing software. Although our hosts are not funded and donate the working space to us, we show our appreciation by making one of our software release’s splash screens in honour of that host, which is a great way to gain exposure of your institution and country to the hundreds of thousands of users that make use of QGIS.

For more details and to sign up, please visit the corresponding wiki page.

MF-JSON update & tutorial with official sample

Since last week’s post, I’ve learned that there is an official OGC Moving Features JSON Encodings repository with more recent sample datasets, including MovingPoint, MovingPolygon, and Trajectory JSON examples.

The MovingPoint example seems to describe a storm, including its path (temporalGeometry), pressure, wind strength, and class values (temporalProperties):

You can give the current implementation a spin using this MyBinder notebook

An exciting future step would be to experiment with extending MovingPandas to support the MovingPolygon MF-JSON examples. MovingPolygons can change their size and orientation as they move. I’m not yet sure, however, if the number of polygon nodes can change between time steps and how this would be reflected by the prism concept presented in the draft specification:

Image source: https://ksookim.github.io/mf-json/

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.

2.0.16 - Arctic Fox ❄️🦊

🐛 Bug Fixes

  • Fix import project from ZIP action with some ZIP files containing sub-folders.

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