Here’s a quick run-down on two nice new styling options which I’ve recently added to QGIS 2.2.

Map styling for compositions

This little feature was suggested by Mathieu Pellerin, who is always pushing the boundaries of QGIS’ cartographic tools and coming up with great ideas for new styling features (you can check out some of his work via Flickr). Mathieu’s idea was for a new ‘$map‘ variable for the expression builder. This variable holds the id of the map item which is drawing the map, and allows for some nice tweaking of maps in the composer.

The $map variable is most useful when you have more than one map in your composition. The example below shows $map being used to change the styling of a single layer from the main map to the smaller inset map:

Using $map to style a single layer in two maps with different colours

In this example the composition has two maps, the larger has an id of “main_map” and the smaller has “inset_map“. The boundary layer has been styled using the rule based renderer, with one rule for $map=’main_map’ and one for $map=’inset_map’, as shown below:

Rule based rendering using the $map variable

The end result is that the layer will be rendered using the two different styles depending on which composer map item it is being drawn into. This trick can also be used to tweak labelling rules between the maps. In the example above I’ve restricted the labelling to only show in the main map. This is achieved by setting an expression for the data defined “Show label” property. I’ve used the expression “$map=’main_map’” so that labels are only shown in the main map and not the smaller inset map.

Tweaking label settings using the $map variable

This small addition to QGIS 2.2 allows for some rather powerful improvements to multi-map compositions!

Drawing polygon borders only inside the polygon

The second new feature I wanted to highlight is a new option for polygon outlines which causes the outline to be drawn only on the inside of a polygon feature. The usual behaviour is for outlines to be drawn directly over the centre of the feature boundary, so that half of the outline is drawn inside the feature and half on the outside.

This means that the outline in a simple line symbol layer overlaps into the neighbouring polygons, and the result is that outlines from these features blend together:

Shaded borders pre QGIS 2.2 – see how the colours bleed into the neighbouring features and overlap

This looks like a big muddy mess. A feature I’ve wanted for a long time is the ability to restrict these outlines so that they are only drawn inside the feature. This effect is commonly seen in world atlases and National Geographic maps, where each neighbouring country is shaded with it’s own unique outline colour. Now it’s possible to do this in QGIS just by ticking a single box!

The new “Draw line only inside polygon” option

As you can see in the above image, the simple line outline style has a new checkbox, “Draw line only inside polygon“. Ticking this box will clip the outline so that only the portion of it which falls inside the feature is rendered. Here’s the result:

Shaded borders with “Draw line only inside polygon” checked

So much nicer then the earlier output – now none of the borders overlap into their neighbouring regions! Ok, so it is possible to achieve a similar result by creating a specially crafted layer consisting of negatively buffered polygons subtracted from the original polygons, but this takes a lot of fiddling around. It also has the major disadvantage in that the result is scale dependant, and zooming in or out of the map will alter the size of the polygon outlines. But using this wonderful new checkbox in QGIS, we get proper scale-independent borders, and zooming in or out of the map keeps a consistent border width!

Zooming in keeps a consistent border width…

So there we go – two small new features added in QGIS 2.2 which have huge potential for your cartographic outputs! As per usual, if you come up with some fancy way of utilising these, don’t forget to add your maps to the QGIS Showcase on Flickr.

Since I finally managed to download the elevation model of the city of Vienna, I thought I’d share some eye candy with you: The map uses layer blending to combine hillshade and elevation raster, and the elevation raster’s color ramp is a modified “garish14″ from QGIS’ cpt-city color ramp collection.

wien_elevation, a photo by underdarkGIS on Flickr.

A comparion of CGIAR SRTM and open elevation data from the city of Vienna, a photo by underdarkGIS on Flickr.

Update

Here is how you get access to the “garish14″ color ramp:

Start by selecting the “new color ramp” option in the raster’s style window.

Chose the “cpt-city” color ramp type.

In the “cpt-city color ramp” window, you will find lots of different premade color ramps. “garish14″ is part of the “Topography” collection.

Inspired by Václav Petráš posting about “Did you know that you can see streets of downtown Raleigh in elevation data from NC sample dataset?” I wanted to try the new GRASS GIS 7 Addon r.shaded.pca which creates shades from various directions and combines then into RGB composites just to see what happens when using the new EU-DEM at 25m.

To warm up, I registered the “normally” shaded DEM (previously generated with gdaldem) with r.external in a GRASS GIS 7 location (EPSG 3035, LAEA) and overlayed the OpenStreetMap layer using WMS with GRASS 7′s r.in.wms. An easy task thanks to University of Heidelberg’s www.osm-wms.de. Indeed, they offer a similar shading via WMS, however, in the screenshot below you see the new EU data being used for controlling the light on our own:

OpenStreetMap shaded with EU DEM 25m (click to enlarge)

Next item: trying r.shaded.pca… It supports multi-core calculation and the possibility to strengthen the effects through z-rescaling. In my example, I used:

r.shaded.pca input=eu_dem_25 output=eu_dem_25_shaded_pca nproc=3 zmult=50

The leads to a colorized hillshading map, again with the OSM data on top (50% transparency):

OpenStreetMap shaded with r.shaded.pca using EU DEM 25m (click to enlarge)

Yes, fun – I like it

Data sources:

• Shaded maps produced using Copernicus data and information funded by the European Union – EU-DEM layers
• © OpenStreetMap contributors

The post Using the 25m EU-DEM for shading OpenStreetMap layers appeared first on GFOSS Blog | GRASS GIS Courses.

This post describes the three simple steps necessary to create a vintage-looking map using the blending feature in QGIS 2.0′s print composer. This is what we are aiming for:

1. Prepare the map

Like any other map, this one starts in the QGIS main window. Try to stick with earthy colors which will go well with the old paper look. For labels, try fonts which look like handwriting.

Once you are happy with your map

2. Prepare the composition background

To get that vintage feel, we need a background image with a great texture. You can find such textures on sites like lostandtaken.com. Download one you like and add it to an empty print composer. Make sure it covers the whole paper:

Lock the image by right-clicking it once – a small lock icon should appear in the upper left corner.

3. Finish the composition

The final step is to add the map on top of the background image. To make our nice background texture shine through, we enable the “multiply” blending mode in the map’s rendering options:

Feel free to add north arrows or drawings of dragons as finishing touches.

Raster files consist of a grid of cells, each cell contains a numeric value, which is used to determine how to colour each cell.  This value may be based on the elevation of the cell, flood water depth, or soil quality. It is possible to extract this information by point sampling or using a terrain profile. Point sampling copies the cell’s value to the overlying point. A terrain profile tool plots a graph with the cell’s value (elevation) on the Y axis and the distance along the section on the X axis.

Point Sampling Tool

DEM’s are often used to then update the elevation values of overlying points, for example I have used data from DEM’s to update the elevation values of address points and utilities. This isn’t as accurate as surveying each point, but it is a lot quicker! This process is also referred to image extraction, raster/vector conversion.

For this tutorial, you will need:-

• The Point Sampling tool in QGIS is an optional plugin. You can download it by using the menus to select Plugins, Fetch Python Plugins.
• Nasa’s srtm data, which you can download from here: http://srtm.csi.cgiar.org/
• Some point data. If you can’t think of any, then they’re easy to create, for example use the Open Layers plugin to load Open Streetmap or Google Maps of your area, and then create points over a few cities.

I’m going to add the elevation value from the srtm rasters to a selection of UK towns and cities:-

1. Use the menus to select Plugins, Analyses, Point Sampling Tool
2. The Point Sampling Tool dialogue box opens. Select:-

• The layer that contains the points to be sampled
• The layer(s) with the field(s)/band(s) to get values from
• The output (results) file
• Press OK

The results file just contains the elevations:-

It is possible to add these to the original layer:-

• Create a buffer around the new points
• Use the menus to select Vector, Data Management Tools, Join Attributes By location
• Select the original points as the target and the buffer as the join layer

Another option is to update the x and y co-ordinates for both points using the Field calculator and then to match the rows in Excel on the co-ordinate column.

In QGIS 2.0, the old “size scale” field has been replaced by data-defined properties which enable us to control many more properties than jut size and rotation. One of the often requested features – for example – is the possibility for data-defined colors:

Today’s example map visualizes a dataset of known meteorite landings published on http://visualizing.org/datasets/meteorite-landings. I didn’t clean the data, so there is quite a bunch of meteorites at 0/0.

To create the map, I used QGIS 2.0 feature blending mode “multiply” as well as data-defined size based on meteorite mass:

Background oceans and graticule by NaturalEarthData.

Continuing from last week’s post, I will show you how to use terrain analysis to calculate:-

• slope,
• aspect
• hillshade
• ruggedness index

Slope

Slope is calculated by comparing the pixel value at a particular location relative to the surrounding 8 pixel values. This gives the steepness of the slope.

The Slope dialogue box is very simple:-

Calculate slope dialogue box

• Select the elevation layer (this will be the DTM raster)
• Select the output layer
• I have left the Ouput format and Z factor as default. If the ground is very flat, then exaggerating the z factor might make the slopes easier to visualise.

Aspect

The aspect shows the compass bearing of the slope

The raster has been given values from 0-360 depending on the slope aspect. The darker areas with the lower values are the north to north east facing slopes; the lightest areas with the highest values are the west to north west facing slopes.

Aspect shading

Hillshade

This calculates the amount of sun or shade for a 3D surface. The dialogue box is similar to the previous ones, however there are new options for the sun angle:-

DEM hillshade dialogue box

This analysis uses a fixed location of the sun to accurately simulate the effects of bare hillside and shaded valleys. I positioned the sun to the south west (200 degrees), the east facing slopes around the River Medina estuary in the north of the island are very shaded, in contrast to the brightly lit west facing slopes on the other side of the river.

A DEM with hillshading

This is the most visually appealing and easily understood result and so it is often used as a backdrop for maps with other layers added.

Ruggedness Index

The ruggedness index value is calculated for every location, by summarizing the change in elevation within the 3×3 pixel grid.  Ruggedness index values are grouped into categories to describe the different types of terrain.  The classifications are as follows:

The dialogue box for the ruggedness Index is the same as it is for the other types of analysis mentioned above. The IOW is all categorized as level or nearly level in the ruggedness index. This is despite it being quite hilly! I used the Stretch to MinMx contrast enhancement on the layer properties box:-

A DEM with ruggedness index displayed

The result is quite different to the relief and hill shade raster’s. This is because, it doesn’t attempt to show actual slopes, rather it shows the change in elevation categorised as shown in the ruggedness index table. It is still easy to see the line of hills that cross east to west across the island.

In the previous tutorial, I showed you how to create a raster terrain model. This is useful by itself for visualising the relief of an area. However, it can be even more useful when used as the basis of further analysis.

Over the next few tutorials, I will show you how to carry out the following types of analysis:-

• Slope
• Aspect
• Hillshade
• Ruggedness Index

I am going to use srtm data for the UK, you can download the file for your area from here: http://srtm.csi.cgiar.org/

Displaying the raster, resolving display problems

• Add the image to the project using the Add Raster Layer button. At first the image opens completely grey, to stretch the black to white gradient to fit between the minimum and maximum values found in the image:-
• Press the Stretch Histogram to Full Data Set on the Raster toolbar
• Alternatively, right click on the layer in the Layer Panel, and
  • Select Properties.
  • Select the Style tab.

  At the bottom, change the Contrast Enhancement pull down to Stretch to Min Max.

• If a grid displays as a continuous grey box, check the Transparency for null cells setting
  • Open the Layer Properties
  • Select the Transparency tab
    

    QGIS Layer Properties

• Check that the correct band is selected in the Transparency Band pull down
• Check the No data value and Percent Transparent entries in the Transparent Pixel list

The DEM Models plugin should appear on the Raster Menu. If it isn’t installed, it can be downloaded by using the menu to select Plugins, fetch Python plugins.

Its operation is similar for all the types of analysis that can be undertaken

1. Select the input raster layer
2. Select the output raster layer that will contain the results
3. Use the pull down to select the analysis:

• Hillshade
• Slope
• Aspect
• Color Relief
• Terrain Ruggedness Index
• Topographic Position Index
• Roughness

DEM Terrain Module

Introduction

Rasters are created from gridded data. Each pixel is coloured according to an interpolated value, e.g. triangulation (TIN), nearest neighbour analysis.

A raster file is comprised of a pixels arranged in a grid formation. Each pixel contains a colour value that instructs the computer as to what colour to use when displaying it. Raster images tend to be used for grids as they are a more efficient method of showing large areas of coloured pixels than vector maps.

The following illustrates how a raster grid represents terrain, and how the information might be extracted:-

For simplicity’s sake, imagine that we’re back in the days of 256 colours with 1 being white and 255 being black. I tend to display relief with the highest ground as white or red, then to show lower ground as green or blue.

Let’s take a cross section through a hill:

A grid raster image of the terrain would appear similar to below (please note that I have drawn this in Inkscape using a gradient fill to keep the demonstration simple!):-

The numeric values of the raster grid that the computer would see would be similar to this:

Note the values are not the actual elevation, just the colour values of the pixels. The elevation that each pixel value corresponds to (the legend) is contained in the accompanying shape file along with image registration (the x, y coordinates).

By analysing the grid and determining the relationship between pixel values and the elevation that they represent the GIS software can accurately model the terrain. Once the terrain has been modelled, it is possible to undertake further analysis such as slope calculation, predicting hill shade or water runoff.

The Image toolbar

Firstly, let’s have a look at QGIS’ image tool bar:-

QGIS image bar

This can be added by right clicking on any toolbar and selecting Raster from the short cut menu. The buttons are from left to right:-

• Stretch Histogram to Full Data Set
• Local Histogram Stretch
• Geo-referencer
• Interpolation
• Zonal Statistics

Did you know that there is a version of ColorBrewer that does not require Flash?

Enjoy! Pure Javascript and zero loading times: http://colorbrewer2.org/js/

In the previous post (Queries in QGIS pt 1 – Attribute Queries), I showed how to select features based on their attribute data, e.g. select shops which have the address entered as London. Now let’s imagine there isn’t an address attribute, or alternatively, we want to select features within flood plains for example, or identify areas of woodland that have high voltage power lines running through them.

Spatial selection selects features in one layer based on their spatial relationship to features in another layer

To begin a spatial query either:

• Use the menu to select Vector, Spatial Query
  
• Press the Spatial Query button
  

The spatial query dialogue box:

Select Source Features From – this is the table that the selection will be from.

Where the feature – the options will change depending on the exact combination of point, line or region features that are being used for the selection. They include contains (e.g. a region layer may contain points), crosses (e.g. a line layer may cross a region layer), Is Within (e.g. a point layer may be within a region layer), Touches (where one object may touch another but not actually be within it).

Reference Features Of – this is the second layer. It will be used to select features from the first layer, but its features won’t be part of the selection

The selected geometries only will only use features that have already been selected. For example if I wanted to select all the woodland within a particular county I could set up the query to read

• Select Source Features from Farmland
  
• Where the Feature is Within
  
• Reference features of County Boundaries (selected features only)
  

Click OK, the query will run. Once it has completed, the results will appear:-

It is possible to run a further query based on the selection

Press the Create Layer from Selected to add the selection to the map as a new layer:-

Combined Spatial and Attribute Query

Quite often GIS is used to select features that contain a certain attribute within a certain area (e.g. all the A roads and motorways within Greater London).

These queries are carried out by combining the above Spatial and attribute selections. They can be carried out either order depending on which is most logical.

In this example I would overlay the roads and OS Boundary Line features. I would then select all the London Boroughs by clicking on them with the Select Single Feature tool

• Select Source Features from Roads
  
• Where the Feature is Within
  
• Reference features of OS Boundaryline (selected features only)
  
• Press OK
  
• In the Results box, press the Create Layer from Selected to create a new layer
  
• Right click on the newly added Selection from Roads layer in the Layer Panel
  
• Select Query
  
• Use the Query Builder to enter the following SQL “Classification” = ‘A Road’ OR “Classification” = ‘Motorway’
  
• Press OK
  

I’m going to look at the geo-processing tools. The geo-processing tools are found on the Vector menu under Geo-processing tools. These tools do not edit the input tables; instead you are prompted to create a new layer for the results. Therefore the input layers don’t need to be editable. You can choose to carry out the operation on every feature in the chosen layers, or just the selected features. These functions can be combined with attribute updates and calculations to carry out more complex analysis (e.g. calculate proportional overlap) or to count the address points within set distances of proposed new roads.

I will carry out most of the operations on the green square and red circle shown below:-

QGIS map window

Intersect creates a new feature based on the area of overlap (the intersection) between the two layers. The attributes from both source layers are copied to the new feature:-

To calculate the area of overlap, update the newly created feature’s attribute table with its area.

Union creates a new layer that covers the combined features

Symmetrical Difference creates new shapes based on the non overlapping areas of the original features:-

Clip creates a new shape based on the area of the input layer that is overlapped by the clipping layer. It is similar to the intersection but differs in that the attributes of the chosen layer only are copied to the new feature. It is similar to MapInfo’s Erase Outside function.

Difference creates a new feature based on the area of the input layer that isn’t overlapped by the clipping layer. It is similar to MapInfo’s Erase function.

Dissolve breaks apart overlapping regions in the same layer.

Buffer creates a region around each feature in the source layer. I have used buffers to count address points within set distances of new roads, assign address points to local amenity catchment zones etc. In this case I’m going to apply a 100m buffer around overhead electricity lines. These can be downloaded from OS Open Data.:-

• Input vector Layer – the layer that contains the source objects
  
• Buffer Distance – the distance the buffer will extend from the source objects
  
• Buffer Distance Field – alternatively QGIS can use a value from a numeric field, this makes drawing variable width buffers for features in the same layer easy e.g. Sites rated High Sensitivity could be updated with a buffer distance of 1,000m, sites rated Medium Sensitivity could be updated with a buffer distance of 500m.
  
• Dissolve Buffer Results. The default is to combine the buffers into one region. Enabling this creates a separate region for each source object.
  

Editing Features

A lot of of GIS work involves editing polygons and polylines as well as calculating the size of any overlapping features. For example when I provided GIS support to a local authority’s planning team, I was regularly asked to calculate what proportion of proposed developments overlapped constraints such as flood zones.

In this tutorial, I first look at the re-shaping features on the Advanced Digitising Toolbar. Then I will go through geo-processing tools such as buffer, union, intersect etc.

Advanced Digitising Tools

QGIS Advanced Digitising Toolbar

From left to right, the tools are:

• Undo
  
• Redo
  
• Simplify feature
  
• Add ring
  
• Add part
  
• Delete ring
  
• Delete part
  
• Reshape features
  
• Merge selected features
  
• Merge attributes of selected features
  

To use any of the tools,

• Begin an edit session, by right click on the layer’s name in the Layer Panel and select toggle editing.
  
• Select the feature that you wish to edit
  
• Select the tool
  

This is the polygon at the start of the edit session, I will show a screen shot of each result:

Polygon in QGIS

Add Ring

is used to subtract a new internal region from the existing polygon. Select the tool and then draw the desired internal polygon:-

Add Ring in QGIS

Delete Ring

deletes the selected internal region. Select the tool, and then click on the internal region’s nodes.

Add Part

adds a new external region to the existing region. Select the tool, then draw the new region, ensure it snaps to the existing region:-

Add Part in QGIS

Delete Part

deletes the selected region. Select the tool, and then click on the region’s nodes.

Reshape Features

draws a temporary region which is then subtracted from the existing region:

Reshape Features in QGIS

Reshape Features in QGIS

Split Features

draws a line through the region. The region is then split along the line

Split Features in QGIS

Merge Features – merges two or more selected features. Note Ctrl+click is used to select multiple features

Merge Features in QGIS

A dialogue box allows you to choose which attributes to keep, discard. It is also possible to carry out mathematical operations such as sum, maximum on numeric fields and to concatenate text fields:-

Attributes in QGIS

Yesterday, I described my process to generate a basic population density map from the city of Vienna’s open government data. In the end of that post, I described some ideas for further improvement. Today, I want to follow-up on those ideas using what is known as dasymetric mapping. GIS Dictionary defines it well (much better than Wikipedia):

Dasymetric mapping is a technique in which attribute data that is organized by a large or arbitrary area unit is more accurately distributed within that unit by the overlay of geographic boundaries that exclude, restrict, or confine the attribute in question.
For example, a population attribute organized by census tract might be more accurately distributed by the overlay of water bodies, vacant land, and other land-use boundaries within which it is reasonable to infer that people do not live.

That’s exactly what I want to do: Based on subdistricts with population density values and auxiliary data – Corine Land Cover to be exact – I want to create an improved representation of population density within the city.

This is the population density map I start out with:

… and this is the Corine Land Cover dataset for the same area:

It shows built-up areas (red), parks and natural areas (green) as well as water-covered regions (blue). For further analysis, I follow the assumption that people only live in areas with Corine code 111 “Continuous urban fabric” and 112 “Discontinuous urban fabric”. Therefore, I use the Intersection tool to clip only these residential areas from the subdistrict polygons. The subdistrict population can now be distributed over these new, smaller areas (use Field Calculator) to create a more realistic visualization of population density:

For easier comparison, I put the original density and the dasymetric map into a looping animation. Some subdistricts change their population density values quite drastically, especially in regions where big parts covered by water or rail infrastructure were removed:

Corine Land Cover is not too detailed but I think it still usable on this scale. One thing to note is that I used data from 2006 with population data from 2012 so some areas in the outer districts will have been turned residential in the meantime. But I hope this doesn’t distort the overall picture too much.

The city of Vienna provides both subdistrict geometries and population statistics. Mapping the city’s population density should be straightforward, right? Let’s see …

We should be able to join on ZBEZ and SUB_DISTRICT_CODE, check! But what about the actual population counts? Unfortunately, there is no file which simply lists population per subdistrict. The file I found contains four lines for each subdistrict: females 2011, males 2011, females 2012 and males 2012. That’s not the perfect format for mapping general population density.

A quick way to prepare our input data is applying pivot tables, eg. in Open Office: The goal is to have one row per subdistrict and columns for population in 2011 and 2012:

Export as CSV, add CSVT and load into QGIS. Finally, we can join geometries and CSV table:

A quick look at the joined data confirms that each subdistrict now has a population value. But visualizing absolute values results in misleading maps. Big subdistricts with only average density will overrule smaller but much denser subdistricts:

That’s why we need to calculate population density. This is easy to do using Field Calculator. The subdistrict file already contains area values but even if they were missing, we could calculate it using the $area operator: "pop2012" / ($area / 10000). The resulting population density in population per ha finally shows which subdistricts are the most densely populated:

One could argue that this is still no accurate representation of population density: Big parts of some subdistricts are actually covered by water – especially along the Danube – and therefore uninhabited. There are also big parks which could be excluded from the subdistrict area. But that’s going to be the topic of another post.

If you want to use my results so far, you can download the GeoJSON file from Github.

Today, I’ve finished my submission for the Hubway Data Visualization Challenge. All parts of the resulting dataviz were created using open source tools. My toolbox for this work contains: QGIS, Spatialite, Inkscape, Gimp and Open Office Calc. To see the complete submission and read more about it, check the project page.

QGIS Cloud is a great cloud hosting service for QGIS Server by Sourcepole. After online registration and installation of an (experimental) plugin, QGIS projects can be uploaded to the cloud quite comfortably.

For a quick test, I tried to recreate the map from “Open Data for Physical Maps”. Right now, one of the limitations is that raster layers cannot be uploaded. Instead of the SRTM data, I therefore chose OCM landscape from OpenLayers plugin to provide some hillshade. The process of uploading data and publishing the project went smoothly and I didn’t encounter any problems.

You can explore the map online at qgiscloud.com/anitagraser/corine_austria.

Considering the complexity of the Corine dataset, rendering is quite fast – certainly much better than on my notebook.

Corine Land Cover is a European program to create a land cover inventory of Europe. The data is freely available and a valuable input for many analyses. In this post, we’ll be using it to create a physical map.

For the background, reused the hillshade presented in “Mapping Open Data With Open GIS”

Instead of the standard grayscale, I defined a sand-colored colormap that looks warmer and more natural:

On top of this hillshade, I put the Corine land cover layer. Instead of the official, rather bright colors I selected a more neutral color palette and varying transparency values: Water areas are drawn with no transparency while forests are set to 50 % and built-up areas to up to 80 % transparency. I also skipped classes such as “bare rock” by setting them to be totally transparent:

On top of the land cover, I added a river dataset and styled it with the same color used for water surfaces in the Corine layer. Obviously, this is an optional step. Big rivers are visible within the land cover data too.

After adding a mask and labels, the map is ready to add the finishing touches in Print Composer: Title, explanatory text and a scale bar. I decided against adding a legend to this particular map since I hope that the color choices are intuitive enough.

If you want to create your own physical map, you can use Corine Land Cover for European regions or the National Vegetation Classification in the U.S.

This post explores some cartographic features of QGIS while mapping the river network of Tirol, Austria. All data used is freely available.

For the background, I downloaded NASA SRTM elevation data from CGIAR-CSI and created a hillshade using Terrain Analysis tools in QGIS 1.8.

To emphasize both state borders and the fact that Tirol consists of two separate areas, I created a mask using the Difference tool and styled it a transparent white.

The river network is too dense to label all rivers on an A4 map. Expression-based labels make it possible to only label selected features. For this dataset, the expression limits labeling to features with certain values of GEW_WRRL attribute:

CASE WHEN (GEW_WRRL = '10.000 km2 Fluss' OR "GEW_WRRL"= '4.000 km2 Fluss' OR "GEW_WRRL"='1.000 km2 Fluss') AND length("GEW_NAME_A") < 10 THEN "GEW_NAME_A" END

Labels of neighboring areas together with map title, descriptions and scale bar were added in Print Composer.

Working with Print Composer, it is useful to know that you can use Copy&Paste to duplicate map components and right-click to lock them from being moved. Also, every new component by default comes with a black outline and white background which can (and should) be disabled/changed in “General options”.

This is the final QGIS Print Composer output – without any further post-processing in Inkscape or Gimp:

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.