Scale variance in geo-tagged tweets

Above shows the cluster of tweets in the middle of a work day in London against a black background, and the map below shows it will major features showing. Though seen from the level of nations tweets follow clear geographic and social patterns, as you look closer and closer at tweets the clustering become more random, with some clear rules.

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. 

Distribution of tweets for nations on the North Atlantic

But as you look closer and you see the cluster becoming more random and evenly within in a smaller space. That is to say geo-tweeting does follow a 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.

Distribution viewed at a close scale for London

There are probably a number of reasons for this. First and foremost there is a limit to power laws as you get smaller and smaller. Though it is possible that 90% of the tweeting for a nation could come from a single city, but it is highly unlikely that a single building or street could have 90% of the tweets fro a city. There are limits to how much you can compact or stack up humans.

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.