Tag: osgeo

This blog post gives an overview of how to easily perform a geodata analysis of an online available dataset (here: a GeoTIFF file) with actinia and display the result in Leafmap browser-based.

The post Using leafmap with actinia in a Jupyter notebook appeared first on Markus Neteler Consulting.

The GRASS GIS Annual Report for 2023 highlights a year of significant achievements and developments in the GRASS GIS project, which celebrated its 40th anniversary. Here’s a summary of the report: Community Meeting: The GRASS GIS Community Meeting was held in June at the Czech Technical University in Prague, bringing together a diverse group of […]

The post GRASS GIS Annual Report 2023 appeared first on Markus Neteler Consulting.

We’ve recently had the opportunity to implement a very exciting feature in QGIS 3.34 — the ability to load and view 3D content in the “Cesium 3D Tiles” format! This was a joint project with our (very talented!) partners at Lutra Consulting, and was made possible thanks to a generous ecosystem grant from the Cesium project.

Before we dive into all the details, let’s take a quick guided tour showcasing how Cesium 3D Tiles work inside QGIS:

What are 3D tiles?

Cesium 3D Tiles are an OGC standard data format where the content from a 3D scene is split up into multiple individual tiles. You can think of them a little like a 3D version of the vector tile format we’ve all come to rely upon. The 3D objects from the scene are stored in a generalized, simplified form for small-scale, “zoomed out” maps, and in more detailed, complex forms for when the map is zoomed in. This allows the scenes to be incredibly detailed, whilst still covering huge geographic regions (including the whole globe!) and remaining responsive and quick to download. Take a look at the incredible level of detail available in a Cesium 3D Tiles scene in the example below:

Where can you get 3D tile content?

If you’re lucky, your regional government or data custodians are already publishing 3D “digital twins” of your area. Cesium 3D Tiles are the standard way that these digital twin datasets are being published. Check your regional data portals and government open data hubs and see whether they’ve made any content available as 3D tiles. (For Australian users, there’s tons of great content available on the Terria platform!).

Alternatively, there’s many datasets available via the Cesium ion platform. This includes global 3D buildings based on OpenStreetMap data, and the entirety of Google’s photorealistic Google Earth tiles! We’ve published a Cesium ion QGIS plugin to complement the QGIS 3.34 release, which helps make it super-easy to directly load datasets from ion into your QGIS projects.

Lastly, users of the OpenDroneMap photogrammetry application will already have Cesium 3D Tiles datasets of their projects available, as 3D tiles are one of the standard outputs generated by OpenDroneMap.

Why QGIS?

So why exactly would you want to access Cesium 3D tiles within QGIS? Well, for a start, 3D Tiles datasets are intrinsically geospatial data. All the 3D content from these datasets are georeferenced and have accurate spatial information present. By loading a 3D tiles dataset into QGIS, you can easily overlay and compare 3D tile content to all your other standard spatial data formats (such as Shapefiles, Geopackages, raster layers, mesh datasets, WMS layers, etc…). They become just another layer of spatial information in your QGIS projects, and  you can utilise all the tools and capabilities you’re familiar with in QGIS for analysing spatial data along with these new data sources.

One large drawcard of adding a Cesium 3D Tile dataset to your QGIS project is that they make fantastic 3D basemaps. While QGIS has had good support for 3D maps for a number of years now, it has been tricky to create beautiful 3D content. That’s because all the standard spatial data formats tend to give generalised, “blocky” representations of objects in 3D. For example, you could use an extruded building footprint file to show buildings in a 3D map but they’ll all be colored as idealised solid blocks. In contrast, Cesium 3D Tiles are a perfect fit for a 3D basemap! They typically include photorealistic textures, and include all types of real-world features you’d expect to see in a 3D map — including buildings, trees, bridges, cliffsides, etc.

What next?

If you’re keen to learn even more about Cesium 3D Tiles in QGIS, you can check out the recent “QGIS Open Day” session we presented. In this session we cover all the details about 3D tiles and QGIS, and talk in depth about what’s possible in QGIS 3.34 and what may be coming in later releases.

Otherwise, grab the latest QGIS 3.34 and start playing…. you’ll quickly find that Cesium 3D Tiles are a fun and valuable addition to QGIS’ capabilities!

Our thanks go to Cesium and their ecosystem grant project for funding this work and making it possible.

The QGIS 3.28 release is an extremely exciting release for all users who work in mixed software workplaces, or who need to work alongside users of ESRI software. In this post we’ll be giving an overview of all the new tools and features introduced in 3.28 which together result in a dramatic improvement in the workflows and capabilities in working with ESRI based formats and services. Read on for the full details…!

Before we begin, we’d like to credit the following organisations for helping fund these developments in QGIS 3.28:

• Naturstyrelsen, Denmark
• Provincie Gelderland, Netherlands
• Uppsala Universitet, Department of Archaeology and Ancient History
• Gemeente Amsterdam
• Provincie Zuid-Holland, Netherlands

FileGeodatabase (GDB) related improvements

The headline item here is that QGIS 3.28 introduces support for editing, managing and creating ESRI FileGeodatabases out of the box! While older QGIS releases offered some limited support for editing FileGeodatabase layers, this required the manual installation of a closed source ESRI SDK driver… which unfortunately resulted in other regressions in working with FileGeodatabases (such as poor layer loading speed and random crashes). Now, thanks to an incredible reverse engineering effort by the GDAL team, the open-source driver for FileGeodatabases offers full support for editing these datasets! This means all QGIS users have out-of-the-box access to a fully functional, high-performance read AND write GDB driver, no further action or trade-offs required.

Operations supported by the GDAL open source driver include:

• Editing existing features, with full support for editing attributes and curved, 3D and measure-value geometries
• Creating new features
• Deleting features
• Creating, adding and modifying attributes in an existing layer
• Full support for reading and updating spatial indexes
• Creating new indexes on attributes
• “Repacking” layers, to reduce their size and improve performance
• Creating new layers in an existing FileGeodatabase
• Removing layers from FileGeodatabases
• Creating completely new, empty FileGeodatabases
• Creating and managing field domains

On the QGIS side, the improvements to the GDAL driver meant that we could easily expose feature editing support for FileGeodatabase layers for all QGIS users. While this is a huge step forward, especially for users in mixed software workplaces, we weren’t happy to rest there when we  had the opportunity to further improve GDB support within QGIS!

So in QGIS 3.28 we also introduced the following new functionality when working with FileGeodatabases:

FileGeodatabase management tools

QGIS 3.28 introduces a whole range of GUI based tools for managing FileGeodatabases. To create a brand new FileGeodatabase, you can now right click on a directory from the QGIS Browser panel and select New – ESRI FileGeodatabase:

After creating your new database, a right click on its entry will show a bunch of available options for managing the database. These include options for creating new tables, running arbitrary SQL commands, and database-level operations such as compacting the database:

You’re also able to directly import existing data into a FileGeodatabase by simply dragging and dropping layers onto the database!

Expanding out the GDB item will show a list of layers present in the database, and present options for managing the fields in those layers. Alongside field creation, you can also remove and rename existing fields.

Field domain handling

QGIS 3.28 also introduces a range of GUI tools for working with field domains inside FileGeodatabases. (GeoPackage users also share in the love here — these same tools are all available for working with field domains inside this standard format too!) Just right click on an existing FileGeodatabase (or GeoPackage) and select the “New Field Domain” option. Depending on the database format, you’ll be presented with a list of matching field domain types:

Once again, you’ll be guided through a user-friendly dialog allowing you to create your desired field domain!

After field domains have been created, they can be assigned to fields in the database by right-clicking on the field name and selecting “Set Field Domain”:

Field domains can also be viewed and managed by expanding out the “Field domains” option for each database.

Relationship discovery

Another exciting addition in QGIS 3.28 (and the underlying GDAL 3.6 release) is support for discovering database relationships in FileGeodatabases! (Once again, GeoPackage users also benefit from this, as we’ve implemented full support for GeoPackage relationships via the “Related Tables Extension“).

Expanding out a database containing any relationships will show a list of all discovered relationships:

(You can view the full description and details for any of these relationships by opening the QGIS Browser “Properties” panel).

Whenever QGIS 3.28 discovers relationships in the database, these related tables will automatically be added to your project whenever any of the layers which participate in the relationship are opened. This means that users get the full experience as designed for these databases without any manual configuration, and the relationships will “just work”!

Dataset Grouping

Lastly, we’ve improved the way layers from FileGeodatabases are shown in QGIS, so that layers are now grouped according to their original dataset groupings from the database structure:

Edit ArcGIS Online / Feature Service layers

While QGIS has had read-only support for viewing and working with the data in ArcGIS Online (AGOL) vector layers and ArcGIS Server “feature service” layers for many years, we’ve added support for editing these layers in QGIS 3.28. This allows you to take advantage of all of QGIS’ easy to use, powerful editing tools and directly edit the content in these layers from within your QGIS projects! You can freely create new features, delete features, and modify the shape and attributes of existing features (assuming that your user account on the ArcGIS service has these edit permissions granted, of course). This is an exciting addition for anyone who has to work often with content in ArcGIS services, and would prefer to directly manipulate these layers from within QGIS instead of the limited editing tools available on the AGOL/Portal platforms themselves.

This new functionality will be available immediately to users upon upgrading to QGIS 3.28 — any users who have been granted edit capabilities for the layers will see that the QGIS edit tools are all enabled and ready for use without any further configuration on the QGIS client side.

Filtering Feature Service layers

We’ve also had the opportunity to introduce filter/query support for Feature Service layers in QGIS 3.28. This is a huge performance improvement for users who need to work with a subset of a features from a large Feature Service layer. Unfortunately, due to the nature of the Feature Service protocol, these layers can often be slow to load and navigate on a client side. By setting a SQL filter to limit the features retrieved from the service the performance can be dramatically increased, as only matching features will ever be requested from the backend server. You can use any SQL query which conforms to the subset of SQL understood by ArcGIS servers (see the Feature Service documentation for examples of supported SQL queries).

What’s next?

While QGIS 3.28 is an extremely exciting release for any users who need to work alongside ESRI software, we aren’t content to rest here! The exciting news is that in QGIS 3.30 we’ll be introducing a GUI driven approach allowing users to create new relationships in their FileGeodatabase (and GeoPackage!) databases.

At North Road we’re always continuing to improve the cross-vendor experience for both ESRI and open-source users through our continued work on the QGIS desktop application and our SLYR conversion suite. If you’d like to chat to us about how we can help your workplace transition from a fully ESRI stack to a mixed or fully open-source stack, just contact us to discuss your needs.

Our SLYR tool is the complete solution for full compatibility between ArcMap, ArcGIS Pro and QGIS. It offers a powerful suite of conversion tools for opening ESRI projects, styles and other documents directly within QGIS, and for exporting QGIS documents for use in ESRI software.

A lot has changed since our last SLYR product update post, and we’ve tons of very exciting improvements and news to share with you all! In this update we’ll explore some of the new tools we’ve added to SLYR, and discuss how these tools have drastically improved the capacity for users to migrate projects from the ESRI world to the open-source world (and vice versa).

ArcGIS Pro support

The headline item here is that SLYR now offers a powerful set of tools for working with the newer ArcGIS Pro document formats. Previously, SLYR offered support for the older ArcMap document types only (such as MXD, MXT, LYR, and PMF formats). Current SLYR versions now include tools for:

Directly opening ArcGIS Pro .lyrx files within QGIS

LYRX files can be dragged and dropped directly onto a QGIS window to add the layer to the current project. All the layer’s original styling and other properties will be automatically converted across, so the resultant layer will be an extremely close match to the original ArcGIS Pro layer! SLYR supports vector layers, raster layers, TIN layers, point cloud layers and vector tile layers. We take great pride in just how close the conversion results are to how these layers appear in ArcGIS Pro… in most cases you’ll find the results are nearly pixel perfect!

In addition to drag-and-drop import support, SLYR also adds support for showing .lyrx files directly in the integrated file browser, and also adds tools to the QGIS Processing Toolbox so that users can execute bulk conversion operations, or include document conversion in their models or custom scripts.

ArcGIS Pro map (mapx) and project (aprx) conversion

Alongside the LYRX support, we’ve also added support for the ArcGIS Pro .mapx and .aprx formats. Just like our existing .mxd conversion, you can now easily convert entire ArcGIS Pro maps for direct use within QGIS! SLYR supports both the older ArcGIS Pro 2.x project format and the newer 3.x formats.

Export from QGIS to ArcGIS Pro!

Yes, you read that correctly… SLYR now allows you to export QGIS documents into ArcGIS Pro formats! This is an extremely exciting development… for the first time ever QGIS users now have the capacity to export their work into formats which can be supplied directly to ESRI users. Current SLYR versions support conversion of map layers to .lyrx format, and exporting entire projects to the .mapx format. (We’ll be introducing support for direct QGIS to .aprx exports later this year.)

We’re so happy to finally provide an option for QGIS users to work alongside ArcGIS Pro users. This has long been a pain point for many organisations, and has even caused organisations to be ineligible to tender for jobs which they are otherwise fully qualified to do (when tenders require provision of data and maps in ArcGIS compatible formats).

ArcGIS Pro .stylx support

Alongside the other ArcGIS Pro documents, SLYR now has comprehensive support for reading and writing ArcGIS Pro .stylx databases. We’ve dedicated a ton of resources in ensuring that the conversion results (both from ArcGIS Pro to QGIS and from QGIS to ArcGIS Pro) are top-notch, and we even handle advanced ArcGIS Pro symbology options like symbol effects!

Take a look below how even very advanced ArcGIS Pro style libraries convert beautifully to QGIS symbol libraries:

ArcMap Improvements

While we’ve been focusing heavily on the newer ArcGIS Pro formats, we’ve also improved our support for the older ArcMap documents. In particular, SLYR now offers more options for converting ArcMap annotation layers and annotation classes to QGIS supported formats. Users can now convert Annotation layers and classes directly over to QGIS annotation layer or alternatively annotation classes can be converted over to the OGC standard GeoPackage format. When exporting annotation classes to GeoPackage the output database is automatically setup with default styling rules, so that the result can be opened directly in QGIS and will be immediately visualised to match the original annotation class.

Coming soon…

While all the above improvements are already available for all SLYR license holders, we’ve got many further improvements heading your way soon! For example, before the end of 2022 we’ll be releasing another large SLYR update which will introduce support for exporting QGIS projects directly to ArcGIS Pro .aprx documents. We’ve also got many enhancements planned which will further improve the quality of the converted documents. Keep an eye on this blog and our social media channels for more details as they are available…

You can read more about our SLYR tool at the product page, or contact us today to discuss licensing options for your organisation.

Last week the first official “FOSS4G/SOTM Oceania” conference was held at Melbourne University. This was a fantastic event, and there’s simply no way I can extend sufficient thanks to all the organisers and volunteers who put this event together. They did a brilliant job, and their efforts are even more impressive considering it was the inaugural event!

Upfront — this is not a recap of the conference (I’m sure someone else is working on a much more detailed write up of the event!), just some musings I’ve had following my experiences assisting Nathan Woodrow deliver an introductory Python for QGIS workshop he put together for the conference. In short, we both found that delivering this workshop to a group of PyQGIS newcomers was a great way for us to identify “pain points” in the PyQGIS API and areas where we need to improve. The good news is that as a direct result of the experiences during this workshop the API has been improved and streamlined! Let’s explore how:

Part of Nathan’s workshop (notes are available here) focused on a hands-on example of creating a custom QGIS “Processing” script. I’ve found that preparing workshops is guaranteed to expose a bunch of rare and tricky software bugs, and this was no exception! Unfortunately the workshop was scheduled just before the QGIS 3.4.2 patch release which fixed these bugs, but at least they’re fixed now and we can move on…

The bulk of Nathan’s example algorithm is contained within the following block (where “distance” is the length of line segments we want to chop our features up into):

for input_feature in enumerate(features):
    geom = feature.geometry().constGet()
    if isinstance(geom, QgsLineString):
        continue
    first_part = geom.geometryN(0)
    start = 0
    end = distance
    length = first_part.length()

    while start < length:
        new_geom = first_part.curveSubstring(start,end)

        output_feature = input_feature
        output_feature.setGeometry(QgsGeometry(new_geom))
        sink.addFeature(output_feature)

        start += distance
        end += distance

There’s a lot here, but really the guts of this algorithm breaks down to one line:

new_geom = first_part.curveSubstring(start,end)

Basically, a new geometry is created for each trimmed section in the output layer by calling the “curveSubstring” method on the input geometry and passing it a start and end distance along the input line. This returns the portion of that input LineString (or CircularString, or CompoundCurve) between those distances. The PyQGIS API nicely hides the details here – you can safely call this one method and be confident that regardless of the input geometry type the result will be correct.

Unfortunately, while calling the “curveSubstring” method is elegant, all the code surrounding this call is not so elegant. As a (mostly) full-time QGIS developer myself, I tend to look over oddities in the API. It’s easy to justify ugly API as just “how it’s always been”, and over time it’s natural to develop a type of blind spot to these issues.

Let’s start with the first ugly part of this code:

geom = input_feature.geometry().constGet()
if isinstance(geom, QgsLineString):
    continue
first_part = geom.geometryN(0)
# chop first_part into sections of desired length
...

This is rather… confusing… logic to follow. Here the script is fetching the geometry of the input feature, checking if it’s a LineString, and if it IS, then it skips that feature and continues to the next. Wait… what? It’s skipping features with LineString geometries?

Well, yes. The algorithm was written specifically for one workshop, which was using a MultiLineString layer as the demo layer. The script takes a huge shortcut here and says “if the input feature isn’t a MultiLineString, ignore it — we only know how to deal with multi-part geometries”. Immediately following this logic there’s a call to geometryN( 0 ), which returns just the first part of the MultiLineString geometry.

There’s two issues here — one is that the script just plain won’t work for LineString inputs, and the second is that it ignores everything BUT the first part in the geometry. While it would be possible to fix the script and add a check for the input geometry type, put in logic to loop over all the parts of a multi-part input, etc, that’s instantly going to add a LOT of complexity or duplicate code here.

Fortunately, this was the perfect excuse to improve the PyQGIS API itself so that this kind of operation is simpler in future! Nathan and I had a debrief/brainstorm after the workshop, and as a result a new “parts iterator” has been implemented and merged to QGIS master. It’ll be available from version 3.6 on. Using the new iterator, we can simplify the script:

geom = input_feature.geometry()
for part in geom.parts():
    # chop part into sections of desired length
    ...

Win! This is simultaneously more readable, more Pythonic, and automatically works for both LineString and MultiLineString inputs (and in the case of MultiLineStrings, we now correctly handle all parts).

Here’s another pain-point. Looking at the block:

new_geom = part.curveSubstring(start,end)
output_feature = input_feature
output_feature.setGeometry(QgsGeometry(new_geom))

At first glance this looks reasonable – we use curveSubstring to get the portion of the curve, then make a copy of the input_feature as output_feature (this ensures that the features output by the algorithm maintain all the attributes from the input features), and finally set the geometry of the output_feature to be the newly calculated curve portion. The ugliness here comes in this line:

output_feature.setGeometry(QgsGeometry(new_geom))

What’s that extra QgsGeometry(…) call doing here? Without getting too sidetracked into the QGIS geometry API internals, QgsFeature.setGeometry requires a QgsGeometry argument, not the QgsAbstractGeometry subclass which is returned by curveSubstring.

This is a prime example of a “paper-cut” style issue in the PyQGIS API. Experienced developers know and understand the reasons behind this, but for newcomers to PyQGIS, it’s an obscure complexity. Fortunately the solution here was simple — and after the workshop Nathan and I added a new overload to QgsFeature.setGeometry which accepts a QgsAbstractGeometry argument. So in QGIS 3.6 this line can be simplified to:

output_feature.setGeometry(new_geom)

Or, if you wanted to make things more concise, you could put the curveSubstring call directly in here:

output_feature = input_feature
output_feature.setGeometry(part.curveSubstring(start,end))

Let’s have a look at the simplified script for QGIS 3.6:

for input_feature in enumerate(features):
    geom = feature.geometry()
    for part in geom.parts():
        start = 0
        end = distance
        length = part.length()

        while start < length:
            output_feature = input_feature
            output_feature.setGeometry(part.curveSubstring(start,end))
            sink.addFeature(output_feature)

            start += distance
            end += distance

This is MUCH nicer, and will be much easier to explain in the next workshop! The good news is that Nathan has more niceness on the way which will further improve the process of writing QGIS Processing script algorithms. You can see some early prototypes of this work here:

I couldn’t be at the community day but managed to knock out some of the new API on the plane on the way home. **API subject to change #FOSS4G_SotM_Oceania

cc @nyalldawson @underdarkGIS this will create a alg under the hood and check for double inputs, etc pic.twitter.com/49VpyuukGU

— Nathan Woodrow (@madmanwoo) November 23, 2018

So there we go. The process of writing and delivering a workshop helps to look past “API blind spots” and identify the ugly points and traps for those new to the API. As a direct result of this FOSS4G/SOTM Oceania 2018 Workshop, the QGIS 3.6 PyQGIS API will be easier to use, more readable, and less buggy! That’s a win all round!

Last week we kicked off the first (of hopefully many) Australian QGIS hackfests Developers Meetings. It was attended by 3 of the core QGIS development team: Nathan Woodrow, Martin Dobias and myself (Nyall Dawson), along with various family members. While there’s been QGIS hackfests in Europe for over 10 years, and others scattered throughout various countries (I think there was a Japanese one recently… but Twitter’s translate tool leaves me with little confidence about this!), there’s been no events like this in the Southern hemisphere yet. I’ve been to a couple in Europe and found them to be a great way to build involvement in the project, for both developers and non-developers alike.

In truth the Australian hackfest plans began mostly an excuse for Nathan and I to catch up with Martin Dobias before he heads back out of this hemisphere and returns to Europe. That said, Nathan and I have long spoken about ways we can build up the QGIS community in Australia, so in many ways this was a trial run for future events. It was based it in Noosa, QLD (and yes, we did manage to tear ourselves away from our screens long enough to visit the beach!).

Nathan Woodrow (@NathanW2), myself (@nyalldawson), and Martin Dobias (@wonder-sk)

Here’s a short summary of what we worked on during the hackfest:

• Martin implemented a new iterator style accessor for vertices within geometries. The current approach to accessing vertices in QGIS is far from optimal. You either have the choice of an inefficient methods (eg QgsGeometry.asPolyline(), asPolygon(), etc) which requires translations of all vertices to a different data structure (losing any z/m dimensional values in the process), or an equally inefficient QgsAbstractGeometry.coordinateSequence() method, which at least keeps z/m values but still requires expensive copies of every vertex in the geometry. For QGIS 3.0 we’ve made a huge focus on optimising geometry operations and vertex access is one of the largest performance killers remaining in the QGIS code. Martin’s work adds a proper iterator for the vertices within a geometry object, both avoiding all these expensive copies and also simplifying the API for plugins. When this work lands traversing the vertices will become as simple as

for v in geom.vertices():
   ... do something with the vertex!

• Martin is also planning on extending this work to allow simple iteration over the parts and rings within geometries too. When this lands in QGIS we can expect to see much faster geometry operations.
• Nathan fixed a long standing hassle with running standalone PyQGIS scripts outside of the QGIS application on Windows. In earlier versions there’s a LOT of batch file mangling and environment variable juggling required before you can safely import the qgis libraries within Python. Thanks to Nathan’s work, in QGIS 3.0 this will be as simple as just making sure that the QGIS python libraries are included in your Python path, and then importing qgis.core/gui etc will work without any need to create environment variables for OSGEO/GDAL/PLUGINS/etc. Anyone who has fought with this in the past will definitely appreciate this change, and users of Python IDEs will also appreciate how simple it is now to make the PyQGIS libraries available in these environments.
• Nathan also worked on “profiles” support for QGIS 3.0. This work will add isolated user profiles within QGIS, similar to how Chrome handles this. Each profile has it’s own separate set of settings, plugins, etc. This work is designed to benefit both plugin developers and QGIS users within enterprise environments. You can read more about what Nathan has planned for this here.
• I continued the ongoing work of moving long running interface “blocking” operations to background tasks. In QGIS 3.0 many of these tasks churn away in the background, allowing you to continue work while the operation completes. It’s been implemented so far for vector and raster layer saving, map exports to images/PDF (not composers unfortunately), and obtaining feature counts within legends. During the hackfest I moved the layer import which occurs when you drag and drop a layer to a destination in the browser to a background task.
• On the same topic, I took some inspiration from a commit in Sourcepole’s QGIS fork and reworked how composer maps are cached. One of my biggest gripes with QGIS’ composer is how slow it is to work with when you’ve got a complex map included. This change pushes the map redrawing into a background thread, so that these redraws no longer “lock up” the UI. It makes a HUGE difference in how usable composer is. This improvement also allowed me to remove those confusing map item “modes” (Cache/Render/Rectangle) – now everything is redrawn silently in the background whenever required.
• Lastly, I spent a lot of time on a fun feature I’ve long wanted in QGIS – a unified search “locator” bar. This feature is heavily inspired by Qt Creator’s locator bar. It sits away down in the status bar, and entering any text here fires up a bunch of background search tasks. Inbuilt searches include searching the layers within the current project (am I the only one who loses layers in the tree in complex projects!?), print layouts in the project, processing algorithms, and menu/toolbar actions. The intention here is that plugins will “take over” and add additional search functionality, such as OSM place names searching, data catalog searches, etc. I’m sure when QGIS 3.0 is released this will quickly become indispensable!

The upcoming QGIS 3.0 locator bar

Big thanks go out to Nathan’s wife, Stacey, who organized most of the event and without whom it probably would never have happened, and to Lutra Consulting who sponsored an awesome dinner for the attendees.

We’d love this to be the first of many. The mature European hackfests are attended by a huge swath of the community, including translators, documentation writers, and plugin developers (amongst others). If you’ve ever been interested in finding out how you can get more involved in the project it’s a great way to dive in and start contributing. There’s many QGIS users in this part of the world and we really want to encourage a community of contributors who “give back” to the project. So let Nathan or myself know if you’d be interested in attending other events like this, or helping to organize them locally yourself…

A lot of cartographers have a love/hate relationship with label halos. On one hand they can be an essential technique for improving label readability, especially against complex background layers. On the other hand they tend to dominate maps and draw unwanted attention to the map labels.

In this post I’m going to share my preferred techniques for using label halos. I personally find this technique is a good approach which minimises the negative effects of halos, while still providing a good boost to label readability. (I’m also going to share some related QGIS 3.0 news at the end of this post!)

Let’s start with some simple white labels over an aerial image:

These labels aren’t very effective. The complex background makes them hard to read, especially the “Winton Shire” label at the bottom of the image. A quick and nasty way to improve readability is to add a black halo around the labels:

Sure, it’s easy to read the labels now, but they stand out way too much and it’s difficult to see anything here except the labels!

We can improve this somewhat through a better choice of halo colour:

This is much better. We’ve got readable labels which aren’t too domineering. Unfortunately the halo effect is still very prominent, especially where the background image varies a lot. In this case it works well for the labels toward the middle of the map, but not so well for the labels at the top and bottom.

A good way to improve this is to take advantage of blending (or “composition”) modes (which QGIS has native support for). The white labels will be most readable when there’s a good contrast with the background map, i.e. when the background map is dark. That’s why we choose a halo colour which is darker than the text colour (or vice versa if you’ve got dark coloured labels). Unfortunately, by choosing the mid-toned brown colour to make the halos blend in more, we are actually lightening up parts of this background layer and both reducing the contrast with the label and also making the halo more visible. By using the “darken” blend mode, the brown halo will only be drawn for pixels were the brown is darker then the existing background. It will darken light areas of the image, but avoid lightening pixels which are already dark and providing good contrast. Here’s what this looks like:

The most noticeable differences are the labels shown above darker areas – the “Winton Shire” label at the bottom and the “Etheridge Shire” at the top. For both these labels the halo is almost imperceptible whilst still subtly doing it’s part to make the label readable. (If you had dark label text with a lighter halo color, you can use the “lighten” blend mode for the same result).

The only issue with this map is that the halo is still very obvious around “Shire” in “Richmond Shire” and “McKinlay” on the left of the map. This can be reduced by applying a light blur to the halo:

There’s almost no loss of readability by applying this blur, but it’s made those last prominent halos disappear into the map. At first glance you probably wouldn’t even notice that there’s any halos being used here. But if we compare back against the original map (which used no halos) we can see the huge difference in readability:

Compare especially the Winton Shire label at the bottom, and the Richmond Shire label in the middle. These are much clearer on our tweaked map versus the above image.

Now for the good news… when QGIS 3.0 is released you’ll no longer have to rely on an external illustration/editing application to get this effect with your maps. In fact, QGIS 3.0 is bringing native support for applying many types of live layer effects to label buffers and background shapes, including blur. This means it will be possible to reproduce this technique directly inside your GIS, no external editing or tweaking required!

It’s been a long time since I last blogged here. Let’s just blame that on the amount of changes going into QGIS 3.0 and move on…

One new feature which landed in QGIS 3.0 today is a processing algorithm for automatic coloring of a map in such a way that adjoining polygons are all assigned different color indexes. Astute readers may be aware that this was possible in earlier versions of QGIS through the use of either the (QGIS 1.x only!) Topocolor plugin, or the Coloring a map plugin (2.x).

What’s interesting about this new processing algorithm is that it introduces several refinements for cartographically optimising the coloring. The earlier plugins both operated by pure “graph” coloring techniques. What this means is that first a graph consisting of each set of adjoining features is generated. Then, based purely on this abstract graph, the coloring algorithms are applied to optimise the solution so that connected graph nodes are assigned different colors, whilst keeping the total number of colors required minimised.

The new QGIS algorithm works in a different way. Whilst the first step is still calculating the graph of adjoining features (now super-fast due to use of spatial indexes and prepared geometry intersection tests!), the colors for the graph are assigned while considering the spatial arrangement of all features. It’s gone from a purely abstract mathematical solution to a context-sensitive cartographic solution.

The “Topological coloring” processing algorithm

Let’s explore the differences. First up, the algorithm has an option for the “minimum distance between features”. It’s often the case that features aren’t really touching, but are instead just very close to each other. Even though they aren’t touching, we still don’t want these features to be assigned the same color. This option allows you to control the minimum distance which two features can be to each other before they can be assigned the same color.

The biggest change comes in the “balancing” techniques available in the new algorithm. By default, the algorithm now tries to assign colors in such a way that the total number of features assigned each color is equalised. This avoids having a color which is only assigned to a couple of features in a large dataset, resulting in an odd looking map coloration.

Balancing color assignment by count – notice how each class has a (almost!) equal count

Another available balancing technique is to balance the color assignment by total area. This technique assigns colors so that the total area of the features assigned to each color is balanced. This mode can be useful to help avoid large features resulting in one of the colors appearing more dominant on a colored map.

Balancing assignment by area – note how only one large feature is assigned the red color

The final technique, and my personal preference, is to balance colors by distance between colors. This mode will assign colors in order to maximize the distance between features of the same color. Maximising the distance helps to create a more uniform distribution of colors across a map, and avoids certain colors clustering in a particular area of the map. It’s my preference as it creates a really nice balanced map – at a glance the colors look “randomly” assigned with no discernible pattern to the arrangement.

Balancing colors by distance

As these examples show, considering the geographic arrangement of features while coloring allows us to optimise the assigned colors for cartographic output.

The other nice thing about having this feature implemented as a processing algorithm is that unlike standalone plugins, processing algorithms can be incorporated as just one step of a larger model (and also reused by other plugins!).

QGIS 3.0 has tons of great new features, speed boosts and stability bumps. This is just a tiny taste of the handy new features which will be available when 3.0 is released!

I’ve recently spent some time optimising the performance of various QGIS plugins and algorithms, and I’ve noticed that there’s a few common performance traps which developers fall into when fetching features from a vector layer. In this post I’m going to explore these traps, what makes them slow, and how to avoid them.

As a bit of background, features are fetched from a vector layer in QGIS using a QgsFeatureRequest object. Common use is something like this:

request = QgsFeatureRequest()
for feature in vector_layer.getFeatures(request):
    # do something

This code would iterate over all the features in layer. Filtering the features is done by tweaking the QgsFeatureRequest, such as:

request = QgsFeatureRequest().setFilterFid(1001)
feature_1001 = next(vector_layer.getFeatures(request))

In this case calling getFeatures(request) just returns the single feature with an ID of 1001 (which is why we shortcut and use next(…) here instead of iterating over the results).

Now, here’s the trap: calling getFeatures is expensive. If you call it on a vector layer, QGIS will be required to setup an new connection to the data store (the layer provider), create some query to return data, and parse each result as it is returned from the provider. This can be slow, especially if you’re working with some type of remote layer, such as a PostGIS table over a VPN connection. This brings us to our first trap:

Trap #1: Minimise the calls to getFeatures()

A common task in PyQGIS code is to take a list of feature IDs and then request those features from the layer. A see a lot of older code which does this using something like:

for id in some_list_of_feature_ids:
    request = QgsFeatureRequest().setFilterFid(id)
    feature = next(vector_layer.getFeatures(request))
    # do something with the feature

Why is this a bad idea? Well, remember that every time you call getFeatures() QGIS needs to do a whole bunch of things before it can start giving you the matching features. In this case, the code is calling getFeatures() once for every feature ID in the list. So if the list had 100 features, that means QGIS is having to create a connection to the data source, set up and prepare a query to match a single feature, wait for the provider to process that, and then finally parse the single feature result. That’s a lot of wasted processing!

If the code is rewritten to take the call to getFeatures() outside of the loop, then the result is:

request = QgsFeatureRequest().setFilterFids(some_list_of_feature_ids)
for feature in vector_layer.getFeatures(request):
    # do something with the feature

Now there’s just a single call to getFeatures() here. QGIS optimises this request by using a single connection to the data source, preparing the query just once, and fetching the results in appropriately sized batches. The difference is huge, especially if you’re dealing with a large number of features.

Trap #2: Use QgsFeatureRequest filters appropriately

Here’s another common mistake I see in PyQGIS code. I often see this one when an author is trying to do something with all the selected features in a layer:

for feature in vector_layer.getFeatures():
    if not feature.id() in vector_layer.selectedFeaturesIds():
        continue

    # do something with the feature

What’s happening here is that the code is iterating over all the features in the layer, and then skipping over any which aren’t in the list of selected features. See the problem here? This code iterates over EVERY feature in the layer. If you’re layer has 10 million features, we are fetching every one of these from the data source, going through all the work of parsing it into a QGIS feature, and then promptly discarding it if it’s not in our list of selected features. It’s very inefficient, especially if fetching features is slow (such as when connecting to a remote database source).

Instead, this code should use the setFilterFids() method for QgsFeatureRequest:

request = QgsFeatureRequest().setFilterFids(vector_layer.selectedFeaturesIds())
for feature in vector_layer.getFeatures(request):
    # do something with the feature

Now, QGIS will only fetch features from the provider with matching feature IDs from the list. Instead of fetching and processing every feature in the layer, only the actual selected features will be fetched. It’s not uncommon to see operations which previously took many minutes (or hours!) drop down to a few seconds after applying this fix.

Another variant of this trap uses expressions to test the returned features:

filter_expression = QgsExpression('my_field > 20')
for feature in vector_layer.getFeatures():
    if not filter_expression.evaluate(feature):
        continue

    # do something with the feature

Again, this code is fetching every single feature from the layer and then discarding it if it doesn’t match the “my_field > 20” filter expression. By rewriting this to:

request = QgsFeatureRequest().setFilterExpression('my_field > 20')
for feature in vector_layer.getFeatures(request):
    # do something with the feature

we hand over the bulk of the filtering to the data source itself. Recent QGIS versions intelligently translate the filter into a format which can be applied directly at the provider, meaning that any relevant indexes and other optimisations can be applied by the provider itself. In this case the rewritten code means that ONLY the features matching the ‘my_field > 20’ criteria are fetched from the provider – there’s no time wasted messing around with features we don’t need.

Trap #3: Only request values you need

The last trap I often see is that more values are requested from the layer then are actually required. Let’s take the code:

my_sum = 0
for feature in vector_layer.getFeatures(request):
    my_sum += feature['value']

In this case there’s no way we can optimise the filters applied, since we need to process every feature in the layer. But – this code is still inefficient. By default QGIS will fetch all the details for a feature from the provider. This includes all attribute values and the feature’s geometry. That’s a lot of processing – QGIS needs to transform the values from their original format into a format usable by QGIS, and the feature’s geometry needs to be parsed from it’s original type and rebuilt as a QgsGeometry object. In our sample code above we aren’t doing anything with the geometry, and we are only using a single attribute from the layer. By calling setFlags( QgsFeatureRequest.NoGeometry ) and setSubsetOfAttributes() we can tell QGIS that we don’t need the geometry, and we only require a single attribute’s value:

my_sum = 0
request = QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes(['value'], vector_layer.fields() )
for feature in vector_layer.getFeatures(request):
    my_sum += feature['value']

None of the unnecessary geometry parsing will occur, and only the ‘value’ attribute will be fetched and populated in the features. This cuts down both on the processing required AND the amount of data transfer between the layer’s provider and QGIS. It’s a significant improvement if you’re dealing with larger layers.

Conclusion

Optimising your feature requests is one of the easiest ways to speed up your PyQGIS script! It’s worth spending some time looking over all your uses of getFeatures() to see whether you can cut down on what you’re requesting – the results can often be mind blowing!