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Fri Sep 30 15:10:12 2016

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QGIS Planet

Notes from the QGIS-UK South West user group

Yesterday Dartmoor National Park was host to the third QGIS user group for the South West region. We a great range of talks from the worlds of academia, offshore exploration and local government to name but a few. The slides from these are below.

Teaching in QGIS

Using PostGIS within our Geospatial Workflows at Lloyd’s Register

The Adoption of QGIS at Plymouth Community Homes

Integrating QGIS functionality into a data workflow through both automated processing and a plugin

PopChange: An Academic Open Source Project

We are looking at having another meet up in the spring and are thinking of running some workshops on form designing and plugin building. Keep an eye on the main QGIS user group page on Google+ for any news.

Thanks again to everyone who attending and presented.  We also need to give a special thanks to Clear Mapping Company for sponsoring the event.

Cheers

Matt


6th QGIS UK user group meeting in Edinburgh

The 6th QGIS UK user group meeting in Scotland is happening on the 3rd November 2016.  It is being hosted by the EDINA University of Edinburgh at the Informatics Forum and is sponsored by thinkWhere, Ordnance Survey, Angus Council and Cawdor Forestry.  Tickets are available through Eventbrite.

The draft programme of presentations and lightning talks is as follows:

  • Phil Taylor (CEH) – How deep is your loch?
  • Fiona Hemsley-Flint – QGIS server
  • University of Edinburgh – packaging and deploying QGIS
  • Ross McDonald (Angus Council) – managing associated street data
  • Art Lembo (Salisbury University, MD) – terrain analysis with massively parallel processing techniques (embarrasingly so)
  • Neil Benny (thinkWhere) – finding the heart of Scotland / viewshed analysis
  • Tom Chadwin – qgis2web and coding a QGIS plugin
  • Saber Razmjooei – WMTS previews and XYZ support
  • Stephen Bathgate – decision support system in Forestry
  • Ordnance Survey – TBC
  • Andrew Whitelee – QGIS in forestry/ecology
  • Michal Michalski (The Origins of Doha and Qatar Project) – DOHA: Doha Online Historical Atlas
  • lightning talk – TBC
  • lightning talk – TBC

Doors open from 9:00. Registration shortly thereafter. Start and welcome at 9:45 and a planned finish at 16:30. Geobeers to follow.


Update on the QGIS Grant Programme

At the beginning of August this year, we put out a call for applications in our newly launched grant programme. The intent of the programme is to leverage donor and sponsor funding in order to support community members with great ideas to improve the underlying infrastructure of the QGIS project and code base.

We have had a really great response to the call for applications (detailed list of applications is here for your reading pleasure – 233KB download). There has also been some good discussion on the QGIS Developer mailing list about the evaluation process.

Given that we have 18 proposals and only 20,000 Euros to disburse, the QGIS voting members will need to make some tough, pragmatic choices. Its also noteworthy that this is the first time since establishing our community of QGIS Voting Members that we have asked them to vote on an issue. Our intent with the voting member system is to have a streamlined process for deciding on important issues whilst ensuring good representation of all members of the community. In case you are wondering who the QGIS Voting members are, I have prepared this little infographic below which lists the members and shows how they are elected  etc.

qgisoperationalstructure-votingmembersonlyThe voting for the grant proposals ends at the end of the September 2016, and we plan to announce the successful candidates soon after that – probably on the 4th of October. The PSC will arbitrate in the case of a dead heat or the proposal amounts of the top voted proposals not adding up to our funding target.

This round of grant proposals is special not only because it is the first time we are doing this, but also because the grant programme precedes the upcoming release of QGIS 3.0. Providing grants to facilitate this work will help to assure that QGIS 3.0 gets all the love and attention it needs in order to make it a success. That said, there is a huge amount of work to do, and it is mostly being done by a handful of very dedicated and generous (with their time) individuals. If you have the wherewithal to further support some of the grant proposals that did not make the cut, or the QGIS 3.0 effort in general, please get into contact with our treasurer, Andreas Neumann (finance [at] qgis.org) or head over to our sponsorship or donations page to support their work!

Lastly, I appeal to those QGIS Voting Members who have not yet cast their votes to check your email and head over to the voting form to cast your vote!


How to visualize bird migration data with QGIS TimeManager

A common use case of the QGIS TimeManager plugin is visualizing tracking data such as animal migration data. This post illustrates the steps necessary to create an animation from bird migration data. I’m using a dataset published on Movebank:

Fraser KC, Shave A, Savage A, Ritchie A, Bell K, Siegrist J, Ray JD, Applegate K, Pearman M (2016) Data from: Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology. Movebank Data Repository. doi:10.5441/001/1.5q5gn84d.

It’s a CSV file which can be loaded into QGIS using the Add delimited text layer tool. Once loaded, we can get started:

1. Identify time and ID columns

Especially if you are new to the dataset, have a look at the attribute table and identify the attributes containing timestamps and ID of the moving object. In our sample dataset, time is stored in the aptly named timestamp attribute and uses ISO standard formatting %Y-%m-%d %H:%M:%S.%f. This format is ideal for TimeManager and we can use it without any changes. The object ID attribute is titled individual-local-identifier.

movebank_data

The dataset contains 128 positions of 14 different birds. This means that there are rather long gaps between consecutive observations. In our animation, we’ll want to fill these gaps with interpolated positions to get uninterrupted movement traces.

2. Configuring TimeManager

To set up the animation, go to the TimeManager panel and click Settings | Add Layer. In the following dialog we can specify the time and ID attributes which we identified in the previous step. We also enable linear interpolation. The interpolation option will create an additional point layer in the QGIS project, which contains the interpolated positions.

timemanager_settings

When using the interpolation option, please note that it currently only works if the point layer is styled with a Single symbol renderer. If a different renderer is configured, it will fail to create the interpolation layer.

Once the layer is configured, the minimum and maximum timestamps will be displayed in the TimeManager dock right bellow the time slider. For this dataset, it makes sense to set the Time frame size, that is the time between animation frames, to one day, so we will see one frame per day:

timemanager_dock

Now you can test the animation by pressing the TimeManager’s play button. Feel free to add more data, such as background maps or other layers, to your project. Besides exploring the animated data in QGIS, you can also create a video to share your results.

3. Creating a video

To export the animation, click the Export video button. If you are using Linux, you can export videos directly from QGIS. On Windows, you first need to export the animation frames as individual pictures, which you can then convert to a video (for example using the free Windows Movie Maker application).

These are the basic steps to set up an animation for migration data. There are many potential extensions to this animation, including adding permanent traces of past movements. While this approach serves us well for visualizing bird migration routes, it is easy to imagine that other movement data would require different interpolation approaches. Vehicle data, for example, would profit from network-constrained interpolation between observed positions.

If you find the TimeManager plugin useful, please consider supporting its development or getting involved. Many features, such as interpolation, are weekend projects that are still in a proof-of-concept stage. In addition, we have the huge upcoming challenge of migrating the plugin to Python 3 and Qt5 to support QGIS3 ahead of us. Happy QGISing!


How to fix a broken Processing model with AttributeError: ‘NoneType’ object has no attribute ‘getCopy’

Broken Processing models are nasty and this error is particularly unpleasant:

...
File "/home/agraser/.qgis2/python/plugins/processing/modeler/
ModelerAlgorithm.py", line 110, in algorithm
self._algInstance = ModelerUtils.getAlgorithm(self.consoleName).getCopy()
AttributeError: 'NoneType' object has no attribute 'getCopy'

It shows up if you are trying to open a model in the model editor that contains an algorithm which Processing cannot find.

For example, when I upgraded to Ubuntu 16.04, installing a fresh QGIS version did not automatically install SAGA. Therefore, any model with a dependency on SAGA was broken with the above error message. Installing SAGA and restarting QGIS solves the issue.


QGIS2 compatibility plugin

Lately I’ve been spending time porting a bigger plugin from QGIS 2.8 to 3 while maintaining 2.8 compatibility. You can find it at https://github.com/opengisch/qgis2compat/ and http://plugins.qgis.org/plugins/qgis2compat/ One code to rule them all. My target was to have to edit the

Movement data in GIS: issues & ideas

Since I’ve started working, transport and movement data have been at the core of many of my projects. The spatial nature of movement data makes it interesting for GIScience but typical GIS tools are not a particularly good match.

Dealing with the temporal dynamics of geographic processes is one of the grand challenges for Geographic Information Science. Geographic Information Systems (GIS) and related spatial analysis methods are quite adept at handling spatial dimensions of patterns and processes, but the temporal and coupled space-time attributes of phenomena are difficult to represent and examine with contemporary GIS. (Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University)

It’s still a hot topic right now, as the variety of related publications and events illustrates. For example, just this month, there is an Animove two-week professional training course (18–30 September 2016, Max-Planck Institute for Ornithology, Lake Konstanz) as well as the GIScience 2016 Workshop on Analysis of Movement Data (27 September 2016, Montreal, Canada).

Space-time cubes and animations are classics when it comes to visualizing movement data in GIS. They can be used for some visual analysis but have their limitations, particularly when it comes to working with and trying to understand lots of data. Visualization and analysis of spatio-temporal data in GIS is further complicated by the fact that the temporal information is not standardized in most GIS data formats. (Some notable exceptions of formats that do support time by design are GPX and NetCDF but those aren’t really first-class citizens in current desktop GIS.)

Most commonly, movement data is modeled as points (x,y, and optionally z) with a timestamp, object or tracker id, and potential additional info, such as speed, status, heading, and so on. With this data model, even simple questions like “Find all tracks that start in area A and end in area B” can become a real pain in “vanilla” desktop GIS. Even if the points come with a sequence number, which makes it easy to identify the start point, getting the end point is tricky without some custom code or queries. That’s why I have been storing the points in databases in order to at least have the powers of SQL to deal with the data. Even so, most queries were still painfully complex and performance unsatisfactory.

So I reached out to the Twitterverse asking for pointers towards moving objects database extensions for PostGIS and @bitnerd, @pwramsey, @hruske, and others replied. Amongst other useful tips, they pointed me towards the new temporal support, which ships with PostGIS 2.2. It includes the following neat functions:

  • ST_IsValidTrajectory — Returns true if the geometry is a valid trajectory.
  • ST_ClosestPointOfApproach — Returns the measure at which points interpolated along two lines are closest.
  • ST_DistanceCPA — Returns the distance between closest points of approach in two trajectories.
  • ST_CPAWithin — Returns true if the trajectories’ closest points of approach are within the specified distance.

Instead of  points, these functions expect trajectories that are stored as LinestringM (or LinestringZM) where M is the time dimension. This approach makes many analyses considerably easier to handle. For example, clustering trajectory start and end locations and identifying the most common connections:

animation_clusters

(data credits: GeoLife project)

Overall, it’s an interesting and promising approach but there are still some open questions I’ll have to look into, such as: Is there an efficient way to store additional info for each location along the trajectory (e.g. instantaneous speed or other status)? How well do desktop GIS play with LinestringM data and what’s the overhead of dealing with it?


How to use Print Composer templates

In the previous post, Mickael shared a great map design. The download includes a print composer template, that you can use to recreate the design in a few simple steps:

1. Create a new composition based on a template

Open the Composer manager and configure it to use a specific template. Then you can select the .qpt template file and press the Add button to create a new composition based on the template.

2. Update image item paths

If the template uses images, the paths to the images most likely need to be fixed since the .qpt file stores absolute file paths instead of relative ones.

update_image_paths

With these steps, you’re now ready to use the design for your own maps. Happy QGISing!


Material design map tutorial for QGIS Composer

This is a guest post by Mickael HOARAU @Oneil974

For those wishing to get a stylized map on QGIS composer, I’ve been working on a tutorial to share with you a project I’m working on. Fan of web design and GIS user since few years, I wanted to merge Material Design Style with Map composer. Here is a tutorial to show you how to make simply a Material Design Map style on QGIS.

Click to view slideshow.

You can download tutorial here:

Tutorial Material Design Map

And sources here:

Sources Material Design Map

An Atlas Powered version is coming soon!


The EuroLST seamless and gap-free daily European maps of land surface temperatures

The EuroLST dataset is seamless and gap-free with a temporal resolution of four records per day and enhanced spatial resolution of 250 m. This newly developed reconstruction method (Metz et al, 2014) has been applied to Europe and neighbouring countries, resulting in complete daily coverage from 2001 onwards. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. In their paper, the authors provide examples for implications and applications of the new LST dataset, such as disease risk assessment, epidemiology, environmental monitoring, and temperature anomalies.

Reconstructed MODIS Land Surface Temperature Dataset, at 250m pixel resolution (click figure to enlarge):
MODIS lst time series reconstructed

Section 1. Article and data citation:

EuroLST has been produced by the former PGIS group at Fondazione Edmund Mach, DBEM based on daily MODIS LST (Product of NASA) maps.

Metz, M.; Rocchini, D.; Neteler, M. 2014: Surface temperatures at the continental scale: Tracking changes with remote sensing at unprecedented detail. Remote Sensing. 2014, 6(5): 3822-3840 (DOI | HTML | PDF)

Section 2. Used software

Open Source commands used in processing (GRASS GIS 7):
links to the related manual pages involved in the data preparation

  • i.pca: Principal Components Analysis (PCA) for image processing.
  • r.regression.multi: it calculates multiple linear regression from raster maps
  • v.surf.bspline: it performs bicubic or bilinear spline interpolation with Tykhonov regularization.

Furthermore:

  • r.bioclim: calculates various bioclimatic indices from monthly temperature and optional precipitation time series (install in GRASS GIS 7 with “g.extention r.bioclim”)
  • pyModis: Free and Open Source Python based library to work with MODIS data

Section 3. Metadata

Map projection: EPSG 3035, prj file
PROJCS["Lambert Azimuthal Equal Area",
    GEOGCS["grs80",
        DATUM["European_Terrestrial_Reference_System_1989",
            SPHEROID["Geodetic_Reference_System_1980",6378137,298.257222101]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433]],
    PROJECTION["Lambert_Azimuthal_Equal_Area"],
    PARAMETER["latitude_of_center",52],
    PARAMETER["longitude_of_center",10],
    PARAMETER["false_easting",4321000],
    PARAMETER["false_northing",3210000],
    UNIT["Meter",1]]

1. Selected open data derived from EuroLST

Section 1. BIOCLIM derived from reconstructed MODIS LST at 250m pixel resolution

BIO1: Annual mean temperature (°C*10) BIO2: Mean diurnal range (Mean monthly (max - min tem)) BIO3: Isothermality ((bio2/bio7)*100) BIO4: Temperature seasonality (standard deviation * 100) BIO5: Maximum temperature of the warmest month (°C*10) BIO6: Minimum temperature of the coldest month (°C*10) BIO7: Temperature annual range (bio5 - bio6) (°C*10) BIO10: Mean temperature of the warmest quarter (°C*10) BIO11: Mean temperature of the coldest quarter (°C*10)

BIOCLIM-like European LST maps following the “Bioclim” definition (Hutchinson et al., 2009) – derived from 10 years of reconstructed MODIS LST (download to be completed) as GeoTIFF files, 250m pixel resolution, in EU LAEA projection:

Each ZIP file contains the respective GeoTIFF file (for cell value units, see below), the color table as separate ASCII file and a README.txt with details.

Section 2. WMS/WCS Server

Using this URL, you can read the EuroLST BIOCLIM data directly via OGC WMS and WCS protocol:

http://geodati.fmach.it/production/ows_europe_lst

Section 3. OpenData License

The data published in this page are open data and released under the ODbL (Open Database License).

The full EuroLST dataset is not released online as open data (size: 18TB), please ask Luca Delucchi or Roberto Zorer for more info


2. Acknowledgments

The MOD11A1.005, MYD11A1.005 were retrieved from the online web site, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, http://e4ftl01.cr.usgs.gov/

The post The EuroLST seamless and gap-free daily European maps of land surface temperatures appeared first on GFOSS Blog | GRASS GIS Courses.

Updating PyQt signals that use lambda in QGIS with 2to3

Just for the sake of documenting things, when running qgis 2to3 on a plugin I encountered a tricky situation regarding signals. [crayon-57eaacc7defeb890288374/] The original code: [crayon-57eaacc7deffb229217251/] The generated code: [crayon-57eaacc7df004417540092/] so in do_load_project we get False instead of “my test

Using threads in QGIS python plugins

Here an example on how to work with threads in a consistent and clean manner in QGIS python plugins

Point cluster renderer crowdfunding – successful!

Great news! Thanks in part to some generous last minute pledges, our QGIS Point Cluster Renderer campaign has successfully reached its target. This means that QGIS 3.0 will now include a full feature and flexible cluster renderer.

In the meantime, we’d like to extend our warmest thanks to the following generous contributors, whose pledges have made this work possible:

  • Andreas Neumann
  • Qtibia Engineering (Tudor Barascu)
  • Karl-Magnus Jönsson
  • Geonesia (Nicolas Ponzo)

Plus numerous additional anonymous backers whose generous contributions are also highly valued. If you run into any of these funders at a QGIS user group or conference, make sure you treat them like the GIS rock-stars they are!

Keep an eye out on our social media accounts as we’ll be posting more video demonstrations of this work as it lands in the QGIS codebase.

BOTH

Editing Raster Cell Values in QGIS Using Serval Plugin

Users can directly edit raster cell values using Serval plugin in QGIS.

Read more for how to use this plugin…

How to use Serval

Serval is available from QGIS plugin repository. Note that you will need to restart QGIS if you upgrade Serval from an earlier version.

Once installed, Serval functions and settings will be available from the toolbar.

Serval Toolbar in QGIS

Serval supports Undo/Redo for editing values of raster. But it is recommended to make a copy of your raster.

Currently, the following functionalities are available:

  • Probe mode Displays raster bands values in boxes.
  • Draw mode Draw/Edit mode: bands values can be modified in the boxes and written to the current raster cell by hitting the Enter key. In this mode the values will be also assigned to any other raster cell clicked by user.
  • Write nodata To replace a cell value with the NODATA value.
  • Define nodata To define or replace the NODATA value.
  • Color picker To pick a color using QGIS color picker (3-bands rasters only).
  • Undo Redo To Undo/Redo the cell edit. Edits history is saved separately for each raster, that is, undo/redo is always done for current raster layer.

Future developments

We’d like to add support to edit values using spatial and expression selection tools.

For any problems or feedback, please consider to file a ticket here.

Reporting back from Bonn & Oslo

Over the last two weeks, I had the pleasure to attend both the international FOSS4G conference in Bonn, Germany, as well as the regional FOSS4G-NOR in Oslo, Norway. Both events were superbly organized and provided tons of possibilities to share experiences and find new inspiration.

Talks at both conferences have been recorded and can be watched online: Bonn / Oslo

I enjoyed having the opportunity to give two very different talks. In Bonn, I presented work on pedestrian routing and navigation, which was developed within the PERRON project:

It was particularly nice that we had plenty of time for Q&A after this presentation since only two talks were scheduled for this session rather than the usual three. I’d also like to thank everyone for the great feedback – both in person and on Twitter!

In Oslo, I had the honor to give the opening keynote on OpenSource in general and the QGIS project in particular:

2 – Anita Graser – QGIS – A Community-powered GIS Project from krokskogstrollet on Vimeo.

Both conferences were packed with great sessions and talks. If I had to pick favorites from last week’s presentations, I would have to opt for Iván Sánchez presenting his latest projects, including what3fucks and geohaiku:

6 – Iván Sánchez Ortega, Mazemap – Addressing NSFW Geodesic Grids from krokskogstrollet on Vimeo.

Followed closely by the impressive project presentations of the student organizers of FOSS4G-NOR:

10 – Program Committee – What are the results when students use Open Source? from krokskogstrollet on Vimeo.

All three projects: OPPTUR, GISTYLE, and the flexible traffic web viewer were great demos of what can be achieved with open source tools. Mathilde’s GISTYLE project is also available on Github.

An inspiring GISummer comes to an end, but with so many videos to watch and workshop materials to explore, I’m convinced that the autumn will be no less exciting.


GRASS GIS PSC election 2016 results

The new GRASS GIS Project Steering Committee (PSC) is composed of the following nine members (ranking, name, votes):

1 Markus Neteler 62
2 Helena Mitasova 53
3 Martin Landa 52
4 Anna Petrasova 45
5 Moritz Lennert 41
6 Margherita Di Leo 39
7 Michael Barton 35
8 Peter Löwe 33
9 Vaclav Petras 31

More details in earlier announcement sent to the “grass-psc” mailing list:
https://lists.osgeo.org/pipermail/grass-psc/2016-August/001571.html.

For completeness, all relevant candidacy communications, as well as details about the voting process, are published at
https://trac.osgeo.org/grass/wiki/PSC/Election2016

Cited from the original announcement email:
https://lists.osgeo.org/pipermail/grass-announce/2016-September/000119.html

The post GRASS GIS PSC election 2016 results appeared first on GFOSS Blog | GRASS GIS Courses.

Point cluster renderer crowdfunding – the final countdown!

At North Road we are currently running a crowdfunding campaign to sponsor work on a new “Point Cluster Renderer” for QGIS. This is a really exciting new feature which would help make possible some neat styling effects which just aren’t possible in QGIS at the moment. The campaign is now in its final hours and we’ve still got some way to go to reach the campaign goals. If you’re interested in seeing this feature happen, now’s the time to jump onboard and contribute to the campaign!

Before time runs out we’d like to share some more details on how the cluster renderer can be enhanced through the use of data defined symbol overrides. Data defined overrides are where a huge part of QGIS’ symbology power resides. If you’re not familiar with them, we’d suggest grabbing a copy of Anita Graser and Gretchen Peterson’s reference “QGIS Map Design” (seriously – buy this book. You won’t regret it!). Basically, data defined properties allow you to set rules in place which control exactly how each individual feature in a layer is rendered. So, for instance, you can create an override which makes just a single feature render in a different color, or with a larger label, or so that all features with a value over 100 render with a bold label.

We’ve designed the point cluster renderer to take full advantage of QGIS data defined symbology. What this means is that the cluster symbol (ie, the marker which is rendered when 2 or more points are sufficiently close together) will respect any data defined overrides you set for this symbol, and each individual cluster symbol can have a different appearance as a result.

To make this even more flexible, the clusterer will also provide two additional new variables which can be used in data defined overrides for the symbol. The first of these, @cluster_size, will be preset to equal the number of features which have been clustered together at that point. Eg, if the cluster consists of 4 individual neighbouring features, then @cluster_size will be 4 when the cluster symbol is rendered. This can be used to alter the appearance of the cluster symbol based on the number of associated points. The mockup below shows how this could be used to scale the cluster symbol size so that clusters with more points are rendered larger than clusters with less points:

symbol_sizeIn this mockup we’ve also used a font marker symbol layer to render the actual cluster size inside the symbol too. Of course, because almost every property of symbols in QGIS can be data defined there’s almost no limit how @cluster_size could be used – you could use it to change the symbol color by pairing it with QGIS’ ramp_color function, or alter the symbol opacity, or the outline width… basically anything!

The second new expression variable which would be introduced with the cluster renderer is @cluster_color. This variable allows you to access the color of the points contained within each cluster. Since the cluster renderer is built “on top” of an existing renderer, any point which is NOT contained within a cluster is rendered using the specified renderer. For example, if you use a categorized symbol renderer then all points which aren’t in clusters will be drawn using these categorized classes. In this case isolated points will be drawn using different colors to match the predefined classes.

When multiple points are clustered together, @cluster_color will be set to match the color of any contained points. The points must all have the same color, if they differ then @cluster_color will be null. It’s easiest to illustrate this concept! In the below mockup, we’ve used a categorized render to shade points by an attribute (in this case rail line segment name), and used an uninspiring dark grey circle for the cluster markers:

clusters_categorized

Using @cluster_color together with a data defined color override, we can force these cluster markers to retain the colors from the points within each cluster:

clusters_categorized2

Much nicer! You’ll note that a single dark grey point remains, which is where the cluster consists of stations from multiple different line segments. In this case @cluster_color is null, so the data defined override is not applied and the marker falls back to the dark grey color.

Of course, both @cluster_size and @cluster_color can be combined to create some very nice results:

BOTH

So there we have it – using data defined overrides with the cluster marker renderer allows for extremely flexible, powerful cartography!

Now’s the time to get involved… if you’re wanting to see this feature in QGIS, head over to the crowd funding page to find out how YOU can contribute!

 

OSGeo Code Sprint in Bonn

It’s been a great week in Bonn! I joined the other members of the QGIS project at the pre-FOSS4G code sprint at the Basecamp, the weirdest location we’ve had for a developer meeting so far. We used this opportunity to have a face-to-face meeting of the QGIS PSC  with special guests Matthias Kuhn (on QGIS 3.0 and bug tracker discussions) and Lene Fischer (on community team issues)  – notes here.

picture by Tim Sutton

QGIS PSC meeting in action (from left to right: Otto Dassau, Paolo Cavallini, Anita Graser, Andreas Neumann, Jürgen E. Fischer), picture by Tim Sutton

I also finally took the time to compile a blog post on the results of the QGIS user survey 2015.

The code sprint was also a great opportunity to present the results of Akbar Gumbira’s Google Summer of Code project: the QGIS Resource Sharing plugin. This plugin makes it possible to easily share resources (such as SVG icons, symbol definitions, QGIS styles, and Processing scripts) with other QGIS users through an interface that closely resembles the well-known QGIS Plugin Manager. Akbar has also prepared great presentation with background info and screencasts showcasing his project.

QGIS Resource Sharing presentation, picture by @foss4g

QGIS Resource Sharing presentation, picture by @foss4g

The plugin is now available in the Plugin Repository and we have created the official QGIS Resources repository on Github. If you have symbols or styles that you want to share with the community, please create a resource collection and send us a pull request to add it to the official list.

Thanks to all the organizers who worked hard to make this one of the most well-organized and enjoyable code sprints I’ve ever been to. You are awesome!


What are trusted plugins?

The core team of QGIS strives hard to provide the most advanced and user friendly GIS for free use by everyone. In the core QGIS project, every line of code that gets committed is subject to peer review when contributed by a non core developer. This gives us an opportunity to identify and correct inadvertent (or intentional) security issues that a developer may introduce into the code base. By contrast, all of the plugins that are published via the QGIS plugin repository are reviewed by the plugin developers themselves and we don’t have good insight into how much due diligence is applied to plugin code management.

The vast majority of our plugins (listed in http://plugins.qgis.org/ and inside your copy of QGIS) are developed by third parties, either individuals, companies, and institutions. As such, they are outside our direct control and the developers often relatively unknown to the QGIS community. We view this as a potential security risk. We are convinced the risk is small, because of many factors including the “many eyes” principle (the code is visible to everybody, and in use by thousands of people), but cannot exclude the possibility that someone tries to inject malicious code into a plugin.

In order to address this situation, we looked into the opportunity of implementing automatic tools to scan plugins, before their publication, and spot potential problems. Our research indicated that this approach would be difficult and costly, and easy to circumvent.

We (the PSC) therefore decided to implement a simple yet robust approach to security, based on the ‘web of trust’ principle: we trust people we know well in the community. You will see on the http://plugins.qgis.org web site that there is a ‘Trusted Author’ tag has been applied to plugins created by those members of the community that we know and trust.

The criteria for ‘Trusted Authors’ includes those community members that regularly meet at our QGIS developer meetings, and and those that are in almost daily contact with the core team via our developer mailing lists or background project discussions. The remaining plugins (and there are wonderful, reliable, robust, and useful plugins in the list) have not been given the ‘trusted’ label.

We would be delighted if a side effect of this choice would be to stimulate more active and direct involvement of plugin developers in the QGIS community. All plugin developers are therefore invited to join us at one of the next developer meetings (AKA HackFest), or otherwise become a recognized, active member of the community, so they can be integrated as ‘trusted’ plugin developers.


Results of the QGIS user survey 2015

In autumn last year, we ran a rather large-scale user survey, which was translated into many languages and advertised here on this blog. The final reports can be found here:

(Let me know if you have links to other language versions which were not sent to the mailing list.)

Looking at the English report, most responses were filed by regular (49.7%) and advanced users (35.9%) who use QGIS at least several times per week. One interesting result is that responders feel that the project should still prioritise new features when spending funds:

Top 3 “highest priority for spending QGIS funds”

  1. Important features that are missing (50%)
  2. More bugfixing (24.1%)
  3. Improved user documentation (12.4%)

This is also confirmed by the free comments section were roughly 23% of responders were asking for new features, 19% called for more stability (fewer releases and new features), and 9% for better documentation.

Documentation improvements were followed closely by calls for a more structured approach to plugins (making it easier to find the right tool for the job), stricter plugin documentation requirements, consolidation of plugins with similar functionality, and integration of key plugins into core.

When interpreting these results, it’s important to keep in mind that responses are skewed towards experienced users, who are more likely to require specialist functionality. Beginners on the other hand might rank stability, ease of use of core functionality, and good documentation higher.


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