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I thought many of you would like this new geographic network visualization
adapting the Google Arms Globe Visualization to display a different kind of
dynamic georeferenced spatial network: co-occurrences of geography in the
global news media.  In essence, it looks at local news coverage from
throughout the world over the last three months, live-translating it from
65 languages, coupled with city/landmark-level fulltext geocoding, to look
at macro-level patterns in how the global news media as a whole  groups
countries together in its coverage.  In other words, for all news media
from anywhere in the world monitored by GDELT, everytime a location in
South Africa is mentioned in a given week, what are all of the other
countries mentioned in that coverage in rank order?  Only countries which
cooccur at least 10% of the time in a given week are shown to reduce the
network to the strongest connections.

I thought this might be of interest to many of you from both the standpoint
of the kinds of networks that can be computed from global news media for
media studies research through GDELT ( and
for the use of the Google Arms Globe Visualization for high-density
time-varying georeferenced networks.

We've also been doing a lot of work lately on more traditional non-network
spatial mapping of topics, entities, and languages:

But, I thought that the georeferenced network component of the
visualization, and the ability to visualize change over time and the
ability to interactively select a single node of the network to visualize
at a time was a powerful way of thinking of these kinds of network

Kalev Leetaru

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