Invisible Urban Social Networks Made Visible

Our virtual social networks are bits of data flying through cyberspace. We see them via our apps and Web interfaces. But what would they look like if you geo-tagged them and visualized the connections over a virtual map? Christian Marc Schmidt and Liangjie Xia examined these connections within the urban space of the greater New York City area using Twitter and Flickr data.

Marc explains the Invisible Cities project like this:

By revealing the social networks present within the urban environment, Invisible Cities describes a new kind of city—a city of the mind. It displays geocoded activity from online services such as Twitter and Flickr, both in real-time and in aggregate. Real-time activity is represented as individual nodes that appear whenever a message or image is posted. Aggregate activity is reflected in the underlying terrain: over time, the landscape warps as data is accrued, creating hills and valleys representing areas with high and low densities of data.

In the piece, nodes are connected by narrative threads, based on themes emerging from the overlaid information. These pathways create dense meta-networks of meaning, blanketing the terrain and connecting disparate areas of the city.

Invisible Cities maps information from one realm—online social networks—to another: an immersive, three dimensional space. In doing so, the piece creates a parallel experience to the physical urban environment. The interplay between the aggregate and the real-time recreates the kind of dynamics present within the physical world, where the city is both a vessel for and a product of human activity. It is ultimately a parallel city of intersections, discovery, and memory, and a medium for experiencing the physical environment anew.

I was fascinated by the mapping of a virtual social network within an urban city combined with the overlay of quotes and line connections. The “mountains” within the city also caught my eye — which part of your community would tweet and/or use Flickr the most and least?

[Via @utopiah]

Please let me know what you think....