The multilayer map of global risk

Recently, I read a very interesting report from the World Economic Forum about global risks. This Wikipedia article summarizes its scope quite well. Among many other things, the report identifies the global risks that could play a critical role in the next future, suggesting a network mapping the relationships between such risks (e.g. food crisis, failure of national governance, etc.) and global trends (e.g. climate change, urbanization, etc.).

Of course this map is very interesting from the point of view of network modeling. In fact, we might think that global risks and trends are two set of disjoint nodes, representing two distinct levels of a bipartite network. But the map is even richer, because the report distinguishes each global risk within larger categories, such as Economic Risk, Geopolitical Risk, etc. Therefore, for each category risk we can build a layer of networked global risks. Each risk is assigned to just one layer, therefore layers have disjoint sets of nodes, interconnected each other: they form an interdependent network of risks.

At this point, you should be familiar with how to import network data and manipulate graphical parameters. In the following I will just show the settings used to reach the final visualization.

GlobalRisk1.png

We will work with a network of 6 layers and 42 nodes, including both global risks and global trends. The chosen model is, of course, an interdependent network.

Instead of a classical layered visualization, I opt for a three-dimensional edge-colored one, because we are interested in reading useful information from this very small network (instead, a layered visualization is useful for other purposes):

GlobalRisk2.png

Let us tune color and size of nodes and edges:

GlobalRisk3.png

GlobalRisk4.png

GlobalRisk5.png

GlobalRisk6.png

GlobalRisk7.png

We are ready to render our interdependent network of global risks and trends! It looks like this:

GlobalRisk8.png

The different layers, encoding categorization of global risks, are evidently forming clusters. They are interconnected each other and, furthermore, each risk is interconnected to one or more global trends (the single nodes that are distributed on the map).

Not the best map ever, but it seems to provide enough useful information about the bipartite interdependent structure of global risks and trends.

 

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