The road from Longa Tonga to Willa Minga Honga: Shetland and Orkney placenames

Shetland: Near Scalloway (CC-BY-SA rodtuk)
Shetland: Near Scalloway (CC-BY-SA rodtuk)

I can attest: Shetland (and presumably also Orkney) is a great place to visit. Not just for its landscapes, Shetland ponies and puffins, but also for its people, their language and – turns out – placenames!

On Shetland, you can travel from Rumblings to Quilse of Hageneap, from Poverty not to riches but at least to Longa Tonga, from Willa Minga Honga via Pund of Grutin and Cuppa Water to Drooping Point. Turns out, Mid Dublin is on Shetland as well as something called Fografiddle! If you are a cunning geographer, you should at least once travel to Cunning Geo!

Shetland placenames (Source: Big Think)
Shetland placenames (Source: Big Think)

Orkney features similarly curious placenames: Continue reading “The road from Longa Tonga to Willa Minga Honga: Shetland and Orkney placenames”

Creating a hexagonal cartogram

Some weeks ago I visualised the Swiss cantons (states) and their population numbers using what information visualization scientists call a linked view. You can click through to the actual, interactive visualization: here in German or here in French. In what follows I’ll describe the steps of data preparation for this visualization. I decided to keep the specifics on the implementation in D3.js for a third post in order to spare your scroll-wheel and -finger (so stay tuned for that one). 


Welcome to the second part of this series in which I describe the production of this linked view with a population cartogram (top right):


In case you missed it: in the first post of this series, you can read about the conceptual thinking that went into this visualization. But now let’s dive into some geodata-crunching:


GIS pre-processing

In what follows, I’ll try to give you a thorough description of my approach at data processing. I’ll include some screenshots of intermediate results. Obviously, I don’t know how familiar you are with GIS and spatial analysis terminology, so please bear with me if my description is too exhaustive. Conversely, speak up in the comments section, if I have forgotten something or something is not clear. I did all of the GIS analysis in Esri ArcGIS, however, any GIS that can handle vector data will do.

I started off with the following input data:

  • Outlines of administrative units (cantons and cities)
  • Spatially distributed population data from Swiss census

The preparation of the administrative units was quite straightforward: I applied a Union operation in GIS (ArcGIS Help Topic here). Then I did some tidying of the attributes and applied a set of geometric simplifications (polygon outline generalisations). The purpose of these is basically weeding out vertices from the geometries while preserving shape as well as possible. The bigger goal being, of course, simplifying the geometries enough for a fluid web experience down the line.

Swiss census data comes as a point grid at 100 meters resolution. Precise data characteristics don’t matter too much. And one could also use a thematic variable that comes at the same resolution as the display units – cantons and cities in this case. While the handling of canton/city level thematic data would be much easier, the spatially distributed thematic variable in this case allows for a more representative cartogram. If you wonder why, consider, for example, a US setting: Salt Lake City would cause a big local distortion in a cartogram using spatially distributed data, whereas its population would be spread out uniformly throughout all of Utah, if you use state-level data. This effect causes visible differences in the cartogram in regions where population distribution is not spatially uniform.

The GIS processing chain starts with these steps:

  • Generation of a grid (in my case at 5 km resolution, but that number is a bit dependent on the resolution of your input data, your area of interest and maybe your application; as a rule of thumb, I’d suggest a grid resolution that is similar to the size of your hexagons). Any regular tesselation other than a rectangular grid will also do.
  • Union operation on the grid cells and the administrative units. This yields smaller spatial analysis units, that follow the boundaries between administrative units.
  • Spatial join of thematic variable to the new spatial units. A spatial join is a GIS operation where the spatial relationship of entities in two different datasets is evaluated. If a specified relationship is fulfilled, the characteristics of the features in the join dataset are joined to the features in the target dataset. The spatial relationship for this operation was containment (i.e. the criterion was: is a given census data point within the spatial unit at hand?). The join operation encompassed summing up the values. The overall process yields the sum of the population at all census data points which fall within a given spatial analysis unit – or, without the GIS lingo: the total population per unit).


For distortions you need a Scape… toad

The resulting data in Shapefile format was then transferred to the cartogram software Scapetoad. Scapetoad is a freely available Java software developed in the Choros Laboratory at EPFL in Lausanne. It employs the diffusion-based cartogram algorithm by Gastner–Newman. I did several model runs and iteratively tuned the algorithm parameters. That encompassed mainly striking an acceptable balance between subjective quality of the result and cartogram computation time. Unfortunately, I cannot give heuristics for this, you’ll really simply have to try with your data.

When I was happy with the result, I re-imported the cartogram dataset from Scapetoad into the GIS and used a Dissolve operation to aggregate the units back into regions (again, any GIS will do, but the precise name for the operation may vary).

Cartogram production part 1: (1) Preparation of cantons and cities dataset (2) Union of dataset with grid (3) Import into Scapetoad and distorting (4)
Cartogram production part 1: (1) Preparation of cantons and cities dataset (2) Union of dataset with grid (3) Import into Scapetoad and distorting (4) Re-import into GIS and dissolving the geometries

Continue reading “Creating a hexagonal cartogram”

Colour me well-informed

When designing a map or a visualisation, sooner or later there is the point where you have to choose a range of colours (except in very specific circumstances which may require you to produce a black-and-white or greyscale visualisation). What is there to consider in such a situation?


Appropriate use of colours

According to Bertin‘s (1918–2010) seminal work, Semiologie Graphique, colour (defined as hue with constant value) as a visual variable is both selective and associative. These mean, respectively, that an object with slightly differing hue can be selected with ease out of a group of objects and that objects with identical colour but differing values for other visual variables (e.g., in the case of shape as the other variable: a red circle, a red square and a red triangle) can easily be grouped mentally. Continue reading “Colour me well-informed”

Sad map: Conflicts of humankind

The Dutch web development Studio TecToys built, a map and timeline of all important human conflicts. The base data for the visualization comes from Freebase and is enriched with Wikipedia content. The timeline lets you slice the data at adjustable interval widths. I’m not sure, just how exhaustive and geographically un-biased the coverage of the data … Continue reading Sad map: Conflicts of humankind