Year End Sale – 20% off geospatial e-books

Save on our final sale of the year

Use this coupon code until Jan 2nd to get 20% off e-books.

Pick any Locate Press e-book and enter the coupon code: XMAS21
The 20% discount will be applied when you add it to your cart.

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Need books for an event or course? Normally we limit bulk discounts to larger orders, but anyone can place an order with us directly – for at least 5 books and we will give a 20% retail discount. That’s like buying four and getting one for free.

What you do with them after is up to you – resell, giveaway, or get them autographed at an event – your call!  

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QGIS Map Design – Free Christmas DLC

It’s that time of the year again. When it’s dark and cold on the northern hemisphere, let’s grab a warm beverage of your choice and get mapping.

Inspired by the latest QGIS Open Day Freestyle Mapping Challenge, I set out to explore the Quantarctica project dataset. Today, I want to share with you the resulting map series. Like all recipes in QGIS Map Design, this blog post walks you through the process of creating these maps and provides all the resources to reproduce them yourself.

The historic Antarctic expedition of Roald Amundsen is one of five expedition routes mapped by the Quantarctica project which provides the data for our Christmas DLC maps.

This recipe assumes familiarity with QGIS styling, labeling, and layout creating basics, so we can focus on exciting new tips and tricks.

Base Map

Our map uses six layers of the Quantarctica dataset. The geographic context is provided by the ADD Simple basemap and Overview place name layers together with the South pole and Antarctic circle layers.

The two fonts used on this map are Arial and Steelfish.

The historic context is provided by the Five historic expedition routes and Historic stations layers. To show the travel direction of the expedition routes, we can create a trail of small arrow symbols by combining a simple line with custom dash pattern and a marker line:

So far, the map is pretty crowded because we see all five expeditions at once.

Let’s set up the Atlas map series, so we can create a dedicated map for each expedition.

Atlas Map Series

The historic expeditions have been digitized as ten line features in order to be able to distinguish between sections of the routes traveled by sea, land, and even air:

Counting the distinct leader values, there are actually six expeditions in this layer, however only five of them include sea routes.

Focusing on the five earliest expeditions, all of them contain sea routes. We can therefore set up the Atlas by filtering the route features to get only the five sea routes:

This setup will ensure that our Atlas will generate a map series with one map per expedition leader.

Make sure to activate the Altas preview mode now.

Filtering Routes & Stations

Back in the main QGIS window, we now can access the @atlas_feature to filter the routes layer accordingly. We want to show the sea route feature, as well as any other route feature that belongs to the same expedition leader:

Fun With Labels

So far, our map only shows basic labels for geographic features. In the following steps, we will add labels to the expedition routes and historical stations. Finally, we’ll use a rule-based labeling hack to put the finishing touches on our map.

Smooth Route Labels

Many of the expedition routes are anything but straight. They twist and turn and so do the letters of any labels we try to put on them. This effect becomes particularly prominent, when using large label fonts.

To create smoother looking labels, we can use the Geometry Generator (in the Label Placement tab) to create a smoother base line for labeling using an expression like:

smooth(simplify($geometry, 100000), 2)
The map in the background shows the generated simplified line geometry for illustration purposes.

Multi-color Labels

To show the station names and operating years in different colors, we can use HTML label formatting. To do so, we need to enable “Allow HTML formatting” (in the Label Text tab). Then we can build our label expression.

For this label, we combine three column (name, year_start, and year_end). Since year_end is empty (NULL) for some stations, it is important that we use the concat function (instead of the || operator). Otherwise, stations with a NULL value would not be labeled at all:

concat("name",
       '? <span style="color:#307f7f">(',
          "year_start", '-', "year_end",
       ')</span>' )

The ? character is used to insert a line break using the “Wrap on character” setting (in the Label Formatting tab).

Snowflakes *❆❅*

Last, but not least, for some (completely optional) Christmas spirit: let’s make it snow!

The snowflake styling trick used in this map is based solely on Unicode snowflake symbols and rule-based labeling.

As shown in this screenshot, the preview label color is red but it is over-ruled by the project variable flake_color.

By creating multiple rules that all apply to the same (land) polygons in our base map, we can create a random snowflake pattern. Randomness is introduced on different levels:

  • The label font size is randomized, e.g. rand(15, 60).
  • The label rotation is randomized by using the “free (angled)” placement mode.

Different rules have different priorities, with higher priority values assigned to label rules with larger font sizes.

The snowflake color can be adjusted by changing the project variable flake_color (in Project Properties | Variables). This way, we can change the color of all snow flakes at once, without having to edit every individual rule.

So Much More

There’s much more to discover in this project. For example, the label substitutions used to shorten the island labels or the decorations added in the layout:

So go grab the project and happy mapping!

Please ensure that you acknowledge or cite Quantarctica and the Norwegian Polar Institute if you use their dataset in your work.

GIS Week Sale – 35% off geospatial e-books

11-day sale to Celebrate November and GIS Week

Enjoy a 35% discount on all Locate Press e-books from Nov 8-19th:

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What is Geospatial Technology?

Geospatial technology brings tools and data together to describe, map, and analyze the world around us and worlds yet to be discovered.


The term geospatial is a relatively new invention at least in the parlance of mainstream developers. Geospatial can refer to types of data or to types of technology. The word itself is a combination of geographic and spatial – indicating an alignment between geography and the general idea of spatial/locational properties. Spatial concepts (think geometry and statistics) do not necessarily represent a place on a planet until they are combined with ideas of geography in general.

Built on the history of Geographic of Information Systems (GIS)

GIS is a technical domain, usually for geographers, that allows users to make digital maps and subject them to various types of analysis. Sources of GIS data may include satellite or aerial imagery (raster data) or line map data (vector data) delineating points, lines, or regions of interest – created by surveyors, engineers, photo interpreters, etc.

While many GIS projects output maps, their primary goal is to develop observations about a project area and overlapping properties and values. For example, land-use planning typically requires a GIS process to compare/contrast all the competing values – economic, social, environmental, etc. These are thought of as layers of spatial data that overlap one another and can be combined to show different management priorities or scenarios.

Where do deer live in the winter compared to a planned highway development in a popular tourist corridoor – many values in one location often need advanced tools to build a complete picture.

Geography made digital

While GIS helps bring geography into the digital domain, geospatial technology helps bring it to life for more people. Beyond specific GIS projects, there are many more data sources, cartographic products and ways to output maps for different consumers . Collectively, these fall into the region of geospatial data and technology.

Web-based mapping really helped propel the generalized use of geographic data into the mainstream. Before Google Maps was introduced in 2005, there were only a handful of common web-based mapping tools available for the public to use. Developers started to build their own open-source platforms to share information and collect input.

This required a whole stack of technology including geographic data, web servers, spatial databases, rendering libraries, web-interaction libraries (zoom/click/pan), and the internet itself. Geographers or GIS users may only be a small part of the overall project or not involved at all.

In the end, a handful of different technologies are needed to bring digital geospatial data to life.

Broader than just spatial analytics

Building new geospatial web-mapping tools was one part of the journey. Naturally, the more people use mapping tools, the more questions they want to answer. For example, consider how popular Google Maps became due to its driving directions. This level of spatial analytics was profoundly useful for those driving in a new location. But only a small set of built-in analytics was really ever possible with this platform – or so it seemed.

Data analysts and GIS users are used to running specific types of routines on data to get an answer. For example, calculate an optimal route from A to B. Or what is the expected water course derived from this elevation model?

However, with modern geospatial technology, the user may view and interact with the data in a more real-time approach to build understanding before they ever run an analytical routine.

They may never click a “analyze” button but can use a 3D map view to get a sense of where water will flow, or look at the streets around them to compute their own driving path in their head. In this sense, geospatial tools help them leverage geographic data in a context that is intensely personal.

Collection of mapping technology

So what tools and technology are considered geospatial in nature? As noted in the “stack” of technology above, it is a wide-ranging set of technology. It can be helpful to look at the two types of end-users that typically leverage geospatial technology: software developers and data analysts.

Geospatial developers take data of interest, depending on their domain, and create applications that allow their target audience to interact with the data in a meaningful way. This may mean taking data that is not always spatial in nature – like a list of addresses or stores running sales – and turn it into a component on a map for viewing and querying.

Location-based applications using GPS tracking on a device are also used by developers to give localized awareness of nearby data or attributes the developer wants to expose.

Geospatial analysts – often work more behind-the-scenes and provide types of data analysis outputs that get used by application developers, GIS users, or even in reports or web sites for general public consumption.

Geospatial analytics for all

Analysis with geospatial data components is not limited to one domain of analyst anymore. Data scientists or business analysts may combine data from many sources – spatial or not – to provide a common operating picture of a business or project.

Therefore, libraries and processes for analyzing geospatial data have become ubiquitous or are at least a common subset of analytical routines that many have access to. Both desktop and web-based approaches to sharing data along with analytical tools continues to grow in popularity.


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