Waiting time is over, Gimp 2.8 is finally here. That is reason enough to take it for a quick test run!

How about a new look for the QGIS user map?

This “glowing hot” map was made using the Gimp filter of the same name:

For the user point layer, I selected a simple point style with high transparency and separately exported land and user points from print composer.

user points as exported from QGIS

In Gimp, I applied the “glowing hot” filter to the user points and combined the layers. The trick here is to first use “Color to alpha” on the user point layer and turn black to transparent. This way, the “glowing hot” filter will only be applied to the remaining points.

Gimp 2.8 RC1 is close enough to the previous version to get comfortable fast. I like the single-window mode even if it’s hard to tell which part of the GUI has the focus sometimes.

Open source GIS and image editing for a perfect work flow.

Today’s hot topic on the mailing list was a recently added feature which enables QGIS to load data directly from ZIP archives.

To get the contents of a ZIP archive display in the browser dock, it is necessary to activate this feature in the Options dialog. The setting is called “Scan for contents of compressed files (.zip) in browser dock” and is located right at the bottom of the first tab. Both “basic scan” and “full scan” settings seem to work fine:

Settings – Options

In the file browser panel, archives are now listed like any other folder and their content can be added to the map through both double click and drag and drop.

This can help save tons of disk space: The NaturalEarthData.zip in this example is 280 MB big while the unzipped folders take more than 700 MB.

This post summarizes my notes about installing QGIS Server on Ubuntu, adding a QGIS project file to the server and using the WMS in an OpenLayers application.

Installation

First, it’s useful to figure out the Ubuntu version:

lsb_release -a

Since my server runs “lucid”, I add the following package sources to /etc/apt/sources.list (as described in the QGIS installation documentation)

deb http://qgis.org/debian lucid main
deb-src http://qgis.org/debian lucid main

Before we can install anything, we need to add the key and update the package list

gpg --keyserver keyserver.ubuntu.com --recv 1F9ADD375CA44993
gpg --export --armor 1F9ADD375CA44993 | sudo apt-key add -
sudo apt-get update

Now we can install QGIS Server and the necessary Apache package

sudo apt-get install qgis-mapserver libapache2-mod-fcgid

It never hurts to restart Apache :)

sudo /etc/init.d/apache2 restart

Let’s test the installation before we proceed. The GetCapabilities request should already work

http://10.101.21.28/cgi-bin/qgis_mapserv.fcgi?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetCapabilities

Adding a QGIS project file

It’s time to add a QGIS project to our server. To do that, we move to the QGIS Server folder

cd /usr/lib/cgi-bin

where you should find qgis_mapserv.fcgi and wms_metadata.xml.
I’ve decided to have one folder for each project file. My first project is “vienna”.

sudo mkdir vienna
cd vienna

qgis_mapserv.fcgi and wms_metadata.xml can now be linked into this new folder

sudo ln -s ../qgis_mapserv.fcgi .
sudo ln -s ../wms_metadata.xml .

The only thing that’s missing anymore is a QGIS project file. It can be copied or linked into the folder. After restarting Apache, we should be good to go.

Let’s test the setup using “Add WMS Layer” in QGIS by adding the service URL such as

http://10.101.21.28/cgi-bin/vienna/qgis_mapserv.fcgi

and ticking “Ignore GetMap URI …” and “Ignore GetFeature URI …”.

After clicking “Connect”, all layers from the project file we added should get listed and we can select and load them.

QGIS Server can serve as many project files as you want. There a different ways to organize your server but I would simply add a new folder (like the “vienna” folder in this example) and link in the executable and project file.

Using QGIS Server WMS in OpenLayers

Of course QGIS Server doesn’t just talk to QGIS Desktop but to any other WMS client that conforms to the standard. One classic use case is to add the WMS layers to an OpenLayers application. This is rather simple but I’ll add it here for the sake of completeness:

I used to have a Geoserver WMS base layer in my application. The only lines of code that needed to be changed to migrate to QGIS Server were the service URL and the layer names.

    wms = new OpenLayers.Layer.WMS(
        'roads', "http://10.101.21.28/cgi-bin/vienna/qgis_mapserv.fcgi",
        {
            layers: 'roads', 
            format: 'image/png';
            bgcolor: '#fafafa'
        }, 
        {
            buffer: 1, 
            isBaseLayer: true, 
            graphicZIndex: 0, 
        }
    );

Standardized services are great!

So far, Time Manager has been limited to vector layers. Support for raster layers has been on the wish list for quite a while. I’ve been considering different approaches and for now I have settled with one that keeps the way how raster layers work as close to the workings of vector layers as possible:

All layers have to be loaded before they can be added to Time Manager. The layers are added one-by-one and start and end time values are defined. (This differs from vector layers where start/end attribute are defined instead.) All raster layers that are not within the current time frame are set to 100 % transparency.

I’m not certain yet whether this is a good approach though. I’ll probably keep trying different approaches for a while.

This is a screen cast of the current status:

The plugin source is available on Github, as usual. It’s still going to take a while until there will be a plugin package including this feature.

I’m looking forward to reading your comments here or on Youtube. Do you think this approach is usable?

This is a follow-up to my recent “Natural Earth Quick Start Kit” post in which I presented the great quick start kit provided by the Natural Earth team. The QGIS project file they provide was written in QGIS 1.4 with both old symbology and old labeling:

Original Natural Earth Quickstart map centered on the Mediterranean

Since then a lot has changed. QGIS has a new powerful labeling engine which avoids label collisions and more advanced layer symbology. If that’s not reason enough to switch, it is also worth noting that both old systems will most certainly be removed for QGIS 2.0 release. Luckily, switching is pretty easy:

Switching to new symbology

In QGIS 1.7.4, switching to new symbology is very straight forward: Click the “new symbology” button in the upper right corner of the style tab and confirm the popup message. That’s it.

Switching to new labeling

Changing from old to new labeling is less automated. It will help if you take notes about font, size and colors as well as scale ranges before deactivating labeling in layer properties. Enable new labeling from the labeling toolbar and fill in the settings you have written down.

One of the known issues with new labeling is that it is currently not possible to rotate the labels without also specifying the label position. In most projects, this won’t be an issue though.

Updating the Natural Earth project

Besides switching to the new infrastructure, I’ve applied some minor changes to increase readability:

• Buffers for city labels help where labels overlap with equally black country borders.
• Buffers for capital symbols (stars) make them stand out over border lines.
• Suppressed labeling for marine polygons smaller than 10mm avoids clutter.
• Thinner river line styles with rounded corners make the map look cleaner.
• A little halo around the land masses looks friendly.

The same map with new labeling and new symbology

I’ve uploaded the new project file version to QGIS Ressources on Github if you want to give it a try.

This year we are celebrating Gerardus Mercator’s 500th birthday. We have all grown very accustomed to his Mercator projection but I want to take the chance to explore some alternatives:

Radical Cartography features an extensive projection reference compiled by Bill Rankin. One of the more exotic projections is “Van der Grinten I” by Alphons J. van der Grinten, 1898. It has a “pleasant balance of shape and scale distortion”. The “boundary is a circle” and “all parallels and meridians are circular arcs (spacing of parallels is arbitrary)”.

Using the name, we can try to find the projection definition on Spatialreference.org. One of the definitions that works well in QGIS is “ESRI:53029 Sphere Van der Grinten I” with the following proj4 string:

+proj=vandg +lon_0=0 +x_0=0 +y_0=0 +R_A +a=6371000 +b=6371000 +units=m +no_defs

In QGIS Settings – Custom CRS, we can add this projection to the list of available CRS:

1. Press the “Star” button to add a new empty entry”
2. Add the Name and proj4 string
3. Press the “Save” button to make the changes permanent

Custom projection dialog

Once this is done, “Van der Grinten I” can be selected for on-the-fly reprojection. I’ve been using NaturalEarth’s land and ocean dataset. The result might not be a perfect circle (due to the coarseness of the dataset) but I find it very appealing:

Van der Grinten projection

Natural Earth is a great resource and you have probably already used it. One of the many nice things about Natural Earth is that it gets you started very quickly:
They offer a quick start kit that provides a sample of Natural Earth data as well as a QGIS project file. The project is really well done with appropriate scale-dependent styles and labels for all layers. A screenshot can capture only part of it:

The original Natural Earth Quick Start project

One potential point for improvement is labeling. The project uses old labeling and therefore suffers from label collisions. Changing to new labeling engine results in a clearer picture. (I’ve also added label buffers since both city symbols and labels are black and therefore can get difficult to distinguish.)

Another nice trick is to suppress labels for small features. The minimum size of features to be labeled can be set on the “advanced” tab of the new labeling dialog. In this case, I went with 10 mm to avoid labeling small marine polygons.

Changed to new labeling engine

I haven’t worked through all layers yet, but I am planning to share the updated project file back to the Natural Earth team.

Most maps of night time lights show the land masses lit brightly by city lights. But the oceans are not as dark as these maps suggest. NOAA/NGDC datasets available through edenextdata.com show very bright spots in the North Sea:

Night time lights trace the coast but illuminate the sea too.

The dataset description mentions that the sensors pick up moonlit clouds, lights from human settlements, fires, gas flares, heavily lit fishing boats, lightning and the aurora. So might these spots be fishing boats?

A very common approach for mapping point density is to use heat maps. If you are aiming for a different style, give hexagonal grids a try. The workflow is very simple in QGIS:

1. Load the point layer
2. Create a hexagonal grid using MMQGIS – Create Grid Layer
3. Count points per polygon (Vector menu)

I’ve applied this method to an OGD dataset of the Viennese tree cadastre containing 119,744 tree positions:

Default style: One dot per tree

Rendering tree counts per hexagonal grid cell reveals some of Vienna’s greenest spots, such as the Prater or Türkenschanzpark.

Tree density in a hexagonal grid

There’s also a printable version.

Some notes on the necessary steps:

MMQGIS – Create Grid Layer performs great. Creating the 18,400 hexagons in this map was very fast. Note though, that this tool doesn’t seem to write correct projection information to the resulting Shapefile. Therefore it is necessary to set the projection manually after loading the file.

As a result, it is very likely that the Points in Polygon tool will warn you that the point and polygon layer are not in the same projection. I ignored the warning and everything went fine. This step was reasonably fast considering the number of points (119,744) and polygons (18,400).

The default look of OSM plugin layers is not well-suited for background map usage. It’s too colorful and any overlay just gets lost:

Default look of OSM plugin layers

A good background map has enough detail to allow the reader to orient himself but at the same time must not overwhelm the overlays. For data to be overlaid onto OSM plugin layers, a more neutral style is needed. And this is my first attempt at such a style, containing rules for the polygon and line layers (no point style yet):

Light OSM style applied to polygon and line layer

As before, these styles can be downloaded from my “QGIS-resources” Github repository. They are called osm_light_line.qml and osm_light_polygon.qml and require QGIS trunk. They do not work in QGIS 1.7.x because they contain some new features that are only available in the developer version (aka trunk).

Inspired by the “OSM Bright Minimal” style for Tilemill, I’ve created a similar background map style for osm2po layers in QGIS trunk (uses features unavailable in 1.7.3): osm2po_light_style.qml. Together with a grey background (RGB:232,232,232) and an OSM natural layer (RGB:208,208,208 for water), the style looks like this:

example map using osm2po_light_style.qml

It’s plain and bright, so any overlay will stand out nicely. Hope you find it useful.

Ever had to apply the same style to multiple map layers? It’s a tedious task … if you don’t have MultiQML plugin by Gislab.

With MultiQML, you can apply a style to multiple raster or vector layers: First, select the layers you want to style (use shift/ctrl to select multiple layers), press “Apply style …” button and select the appropriate QML. That’s it!

MultiQML dialog

The tool even has an “undo” functionality called “Restore initial style”, which will certainly prove useful.

If you want to see MultiQML in action, Gislab provides a video tutorial on their plugin page.

I’ve added a new style for osm2po layers to the QGIS Resources Github repo. It contains the following rules:

the rules

and looks like this:

the map

Enjoy!

This week Sourcepole released a new addition to the Raster Terrain Analysis plugin: a sophisticated Relief tool. (More info in their announcement) This plugin is shipped with QGIS (developer version, not in 1.7.3 release) by default but you might have to activate it in Plugin Manager:

The plugin dialog is quite self-explanatory. You can chose the elevation file, output path and any of the numerous raster formats. The z factor is a bit more mysterious. We will have a look at that in a second. The rest of the dialog is the relief color editor. Pressing Create automatically will give you a color gradient to start with.

Relief tool dialog

But what’s the z factor good for?

I’ve tried a few different settings using free NASA SRTM data and it seems that higher values lead to a smoother relief (Please ignore the water areas):

Update:

As Marco noted in the comments: The z factor is used if the x/y units are different from the z unit.

• If everything is in meters, use z factor 1.0 (default).
• If x/y is in degree and z in meters, use z factor 111120.
• If x/y degree and z is feet, use z factor 370400.

In the example above SRTM rasters are in WGS84 with heights in meters. That’s why the result using a z factor of 100000 looks so good.

In my opinion the results look great even with the coarse SRTM dataset I used. Looking forward to all the great QGIS maps we will see in the future.

With Martin’s latest addition of conditional statements it’s now even easier to get conditional labels in QGIS. Following up on the example used in my previous post, we can simplify

substr(osm_name, 0, (clazz = 11 or clazz = 13 or clazz = 15 or clazz = 21)*-1)

to

CASE WHEN (clazz = 11 or clazz = 13 or clazz = 15 or clazz = 21) THEN osm_name END

which is much easier to read and remember.

To avoid roads from being labeled with only their road numbers, I added an additional check that the “osm_name” is longer than six characters. Thanks to Nathan’s syntax highlighting this new and powerful expression based labeling is also comfortable to use.

Conditional labels for an osm2po layer

The latest QGIS development build (1.9.90) has a new feature “expression based labeling” which can be used to create conditional labels. One typical use case would be if you want to label only certain (high-level) road classes in your road layer. By default, QGIS labels the features rather randomly:

default labeling

How can we label only the more important roads? Here is an example using OSM data imported into PostGIS using osm2po:

If you have loaded OSM using osm2po, your OSM table will contain a “clazz” attribute. (Check osm2po.config for the exact mapping.) To label only motorways, trunks, primary and secondary roads and nothing else, I wrote this labeling expression:

substr(osm_name, 0, (clazz = 11 or clazz = 13 or clazz = 15 or clazz = 21)*-1)

If clazz equals 11, 13, 15 or 21, the expression returns the value of osm_name. Otherwise it returns an empty string. (All checks will return false or 0 which causes the function to evaluate to substr(osm_name,0,0).) Kudos to Giuseppe Sucameli who explained this on the mailing list.

expression based "conditional" labels

For “QGIS Users Around the World” Gary Sherman collected and geocoded a few weeks of accesses to the plugin repositories. This map is my first attempt at mapping the data for use in QGIS publications:

Considering the coarse resolution of geocoded IP addresses, I’ve decided to count the number of unique IP addresses within each area (5×5 degrees). We can make out a lot of activity in Europe, Japan, Brazil and the US. The high number of accesses from the US mid west are due to IPs being mapped to country-level only.

I would love to hear your feedback on this one!

TimeManager is now available on Github (right beside pgRoutingLayer).

If you want to try it, you can install version 0.4 from the new QGIS Plugin Repository. I’ve also uploaded some test data such as the twitter file used for the animation I presented recently.

This post summarizes how to install sphinx on Windows to contribute to PyQGIS Cookbook. I’m writing this as I go, so this most likely won’t be perfect.

I used my Python 2.6 stand-alone installation (not the one in OSGeo4W).

1. Get the Sphinx egg from http://pypi.python.org/pypi/Sphinx
2. If you don’t have it, install setuptools to get the easy_install script http://pypi.python.org/pypi/setuptools
3. In C:\Python26\Scripts run easy_install -U sphinx
4. Get the PyQGIS source from https://github.com/qgis/QGIS-Developer-Cookbook
5. Create a build folder inside the QGIS-Developer-Cookbook
6. Now you can build the Cookbook: sphinx-build "C:\Users\Anita\QGIS\QGIS-Developer-Cookbook\source" "C:\Users\Anita\QGIS\QGIS-Developer-Cookbook\build"

The build folder should now contain the Cookbook .html files.

I’ve set up a GitHub repo where I will publish QGIS resources such as symbols and styles: https://github.com/anitagraser/QGIS-resources

You can already find symbols/styles described in these posts:

• Colorbrewer color ramp symbols
• “Google Maps” road symbols
• “Google Maps” style for OSM roads