Several years ago, I created a map of scientific
The attention this map obtained surpassed my wildest expectations; it
got published in the scientific and popular press all around the world!
I had mainly forgotten about it until I received an email that rekindled
my interest in this visualization and I thought it was high time to
revisit this visualization.
Unfortunately, scientific papers (and associated data) are closely
guarded and only a handful of firms have full access to them. I now work
in a very different field, so I lost
access to this dataset. But while perusing my Twitter feed, I came
across the very active feed of
Scimago Lab. Their social media
presence and their incredible interactive visualizations convinced me
that they might be interested in collaborating. I sent off an email to
their founder, Félix de Moya and, lo and behold, he was interested in
Read on for more maps and an overview of the methodology >>
After a bit of back and forth, I spent a weekend programming a tool to
draw large geographical graphs. The tool I used a couple of years ago
was riddled by projection bugs and was terribly inefficient (and the
source was lost when I reformatted an old hard disk), so a rewrite was
in order. The idea behind the tool is really simple. It loads up a graph
in memory and then goes through the graph edge by edge to draw every
edge following the curvature of the
earth and projecting
each line using the Plate
(overlappable on Google Maps) or Eckert
I find beautiful).
Click here to open this map in a new
I shared my tool with Félix and after a trying a good deal of different
settings, we were able to compute the following map using the Scopus
database (papers published between 2008 and 2012). This map shows the
collaboration networks between researchers in different cities. Apart
from its aesthetic qualities, the map is useful to illustrate some
interesting collaboration patterns. One of the most striking one is the
importance of Paris in French science. It seems that every researcher in
France collaborates with at least a researcher in Paris. Unsurprisingly,
the map also shows quite clearly that the location scientific
institutions follows the population density. Also, links between
countries and their old colonies are also very obvious.
These following maps were rendered with different color schemes. Click
on any map to enlarge.
Because the basic input for this tool is a large collaboration graph
between cities, we can also use network analytical tools and methods.
One of these methods, the Louvain
(available as a standalone tool or in Gephi) can identify communities in
social network graphs. In our case, it can identify different
collaboration patterns between cities; these patterns seem to follow
linguistic or old colonial lines:
My main preoccupation with this work was mostly aesthetic, but a lot of
interesting analytical work could be done. For example, one could
illustrate different fields (astronomy, biology, etc.), illustrate
different institutions or even animate collaboration patterns during a
specific time period, etc. If you think that this could be interesting
or useful, please contact Scimago Lab.
They'll be able to answer your questions or even produce custom maps.