{"id":1151,"date":"2013-05-06T20:50:45","date_gmt":"2013-05-06T20:50:45","guid":{"rendered":"http:\/\/visurus.wordpress.com\/?p=1151"},"modified":"2019-12-11T10:06:16","modified_gmt":"2019-12-11T10:06:16","slug":"conceptualisation-of-a-d3-linked-view-with-hexagonal-cartogram","status":"publish","type":"post","link":"https:\/\/www.ralphstraumann.ch\/blog\/2013\/05\/conceptualisation-of-a-d3-linked-view-with-hexagonal-cartogram\/","title":{"rendered":"Conceptualisation of a D3 linked view with a hexagonal cartogram"},"content":{"rendered":"

Some weeks\u00a0ago I visualised the Swiss cantons (states) and their population numbers using what information visualization scientists call a\u00a0linked view<\/em>.\u00a0You can click through to the actual, interactive visualization:\u00a0here in German<\/a>\u00a0or\u00a0here in French<\/a>. In what follows I want to give a bit more detail about what led to this visualization and what conceptual thinking went into the design.<\/strong><\/p>\n

In a subsequent post I will also describe the toolset I used to produce this visualization, so that you can build your own. If you’re not interested in the Background<\/em>, you can skip to the\u00a0Conceptually<\/em> section. If that’s neither your cup of tea and you’re here primarily because you want to know how to produce such a visualization yourself, you’ll unfortunately have to patience yourself and wait for the second part of this series (it’s here!<\/a>).<\/p>\n

\"Image\"<\/a><\/p>\n

Background<\/h1>\n

Why population sizes matter \u2013 in such a small country<\/strong><\/h3>\n

Why is the particular piece of information that is visualised here important or interesting? Well, in the Swiss political system cantons are represented at the federal level, whereas cities aren’t. However, some of the big cities represent a considerably larger number of people than quite some of the smaller cantons. There have been many debates if and how cities ought to be represented in the political system, about the specificity of urban issues and how those are dealt with or ignored in Swiss politics and if weighting of the cantons should be adapted to better match their population size. The issue crops up both in relation to elections and polls (Switzerland having a direct democracy there are really many of the latter).<\/p>\n

\"Cartogram<\/a>
Cartogram on Swiss TV<\/figcaption><\/figure>\n

When I published the visualization Switzerland has just held such a poll. The poll did not pass, it achieved only\u00a054.3% of “yes” votes.<\/b><\/p>\n

\u2013 Wait, what? Yep, the vote won a solid\u00a0majority of the people<\/em>, but too many cantons said “nay” and thus, by the rules, it was a “nay”. \u00a0Now, one can argue that this is not sensible or that it is perfectly sensible, I’m not going to do this here. But this background means, to my pleasure, that the visualisation was able to spark and inform many discussions (and met quite an audience). To my big surprise, it was even briefly featured in nation-wide\u00a0primetime news<\/a>, in a slightly reworked version.<\/p>\n

Conceptually<\/strong><\/h1>\n

Linked views<\/strong><\/h2>\n

The visualization is a linked view display because it combines three different views onto a dataset<\/strong>: On the left you see a barchart of population numbers in the Swiss cantons (green) and the ten biggest Swiss cities (purple). Bottom right there is an ordinary enough looking map of the same features. Top right there is a cartogram that uses distorted shapes to convey the number of people in each administrative subdivision.<\/p>\n

The interaction elements of the three individual views are linked: Irrespective over which one of the three views you hover your mouse pointer, the respective feature is highlighted in the two other \u2013 linked \u2013 views. I’d suggest this makes the exploration process easier by reducing cognitive load<\/strong>\u00a0by the amount that would be taken on for cognitively\u00a0linking features in different views.<\/p>\n

Cartograms<\/strong><\/h2>\n

The cartogram is probably the most interesting part of the linked view: A cartogram<\/a> is a map in which a variable is visualized by distorting the features in the map<\/strong>. In this case, the variable is population and the features are cantons and the ten biggest cities (in terms of population). The map beneath the cartogram solely serves as a reference \u2013 however, I would argue it is not negligible at all.<\/p>\n

Research<\/a> into<\/a> the<\/a> effectiveness<\/a> of<\/a> cartograms<\/a> usually compares them to thematic maps (choropleth maps with or without graduated symbols). Geographers find that the effectiveness of cartograms depends on<\/strong>:<\/p>\n