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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 (http://blog.gdeltproject.org/gdelt-2-0-our-global-world-in-realtime/
) 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 visualizations.