Geo-tagging of tweets suffer from the often poor quality of geo-location for mobile phones. Still it is remarkable how the pattern of tweets follows main roads and clusters around train station hubs. Clearly real world transport hubs are also virtual world Internet hubs. The structures of flow of traffic and commerce that condition are real world also structure our cyberworld. That is the real world deeply structures the production of content in the Internet, carrying the mark of geo-social patterns within the production and consumption of data.
power law at large scales, with the vast majority of tweets coming from a few urban areas, but as far as we can see this tweeting is not scale invariant. That is when you zoom out you see tweets highly concentrated in a few locations. But as you zoom in on tweets this power law vanishes, and you see the tweets spreading out in a more random fashion.
Also as you get smaller and smaller scale random errors in the geo-tags coming from the devices themselves start to show. We find that often a phone will put someone up to 500 meters from their actual location. On machines using Wi Fi this error factor can become even higher.
So geo-tweeting shows power law relations on very large scales, covering entire cities or larger, but not on smaller scales. This makes it different than many other parts of the web with show power law relations on ever scale. For example there is a power law relationship of the popularity of blogs. A few blogs get vastly more hits than most. Within topics this also holds, with a few blogs dominating subjects. Even within blogs themselves you will almost always see a few pages that get the vast majority of views or links.
So on of the local impacts of Web 3.0 is likely to reduce the concentration of content at the local level.