Although atlases and world maps have been depicting the world to various degrees of accuracy for centuries, it was not until the start of the 1960s that we started to see pictures of our planet as captured from space (ESA: Fifty Years of Earth Observation, 1957-2007). Today, satellite maps of the world are commonplace, in three dimensions as well as two; and it's not just our planet that has been mapped, either—Google Earth includes a Martian explorer as well as one for or our planet Earth.
The growth in adoption of communication technologies at a global scale within very recent history has brought with it a new breed of world map, based on data traces created by our various technological devices and internationally interconnected transportation systems. Here are a few recent examples of how data is helping us remap our world.
Photo data maps
Many of the photos uploaded to the flickr photosharing website have been "tagged" with geolocation data—that is, data describing the geographical location of the subject of the photograph as a pair of latitude and longitude co-ordinates. If we plot a point for each photo at its corresponding co-ordinates, we can generate quite detailed maps of heavily photographed areas, such as cities. By using a rule of thumb to decide on whether someone is resident of a particular city, or likely to just be visiting it, based on an analysis of the location and date based metadata associated with the photographs of each flickr user whos photos appeared in the dataset, Eric Fischer was able to further colour this map of London based on whether photos were taken by "tourists" or "locals".
(You can see the full 6137 x 6137 image over on Flickr.)
One thing I started to wonder was whether this sort of map would help me find popular beaches around the UK, and whether they match up to the Blue Flag beaches?!
Not content with mapping millions of flickr photos, Eric Fischer has recently turned his hand to visualising three billion tweets. Once again, Eric used a heuristic to categorise whether the people sending a tweet were visiting, or local to, the area a tweet was sent from at the time it was sent. Once again, it is the metadata that allows the points to be mapped and coloured.
As an island dweller, what I find particularly interesting about this map is how it represents the geography an island. As well as the major population centres, and the "traffic" lines correspond to major roads, we also see connective paths provide by ferries, such as the ones that cross the Solent to connect the Isle of Wight with Portsmouth, Southampton and Lymington.
By making use of another metadata field that reveals which mobile phone brand the Twitter app used to send each tweet was installed, Eric shows how the users of different mobile phone brands appear to congregate in different areas of a city.
Whilst this sort of information may be useful to marketers, it might also provide social scientists with new tools for analysing socio-economic or socio-cultural trends.
Note: when generating maps like these, we need to beware of sampling bias and the fact that the results may all come to resemble population density maps, as web comic xkcd reminds us.
As the previous maps show, messages sent from communication systems used whilst we are travelling may provide a way of mapping out transportation systems. As the following video, created by Jer Throp and described Just Landed: Processing, Twitter, MetaCarta & Hidden Data, shows, if we sample tweets associated with the phrase "just landed", we can start to map out common air traffic routes...
We can compare the effectiveness of this form of human instrumentation of the international air travel network with data grabbed from actual plane tracking datasets. This next video shows how the Icelandic volcano eruption in April 2010 put a halt much of the domestic European air traffic, as well as how the system came back to life as the dust cloud dissipated.
How different might a map of Tweets from the same period regarding the mention of "flight" or "airport" have looked?
For a more recent data powered map of international flights, and a critique of it from several different perpectives, see Global flight-path maps: Five interpretations.
As you start to look more at these data created maps, you many notice that there is a dominant aesthetic in the way that many of them are presented. I suspect that in many cases this may be a result of people generating maps using similar tools, or taking the idea of a particular visualisation applied to one data set and then applying it to another. Imagine my surprise then when I first saw this sequence of maps, and wondered what dataset had been used to generate them...
...because they aren't highlighted data points of the sort we might typically imagine - they're points of light, captured from space by the Suomi NPP satellite in April and October 2012: NASA-NOAA Satellite Reveals New Views of Earth at Night
Episode 5 of Click: A Route 66 of the future explores how mapping and mining of data has enriched the lives of tourists. Listen to it on the BBC World Service on Tuesday 2 July at 7:30pm.