June 01, 2010

The Geotaggers' World Atlas (and cyberscapes, too!)

Having just stumbled across another amazing visualization of geotagged photographs, we figured we'd go ahead and share more of the stuff we've been looking at these days. The following map comes from Eric Fischer's The Geotaggers' World Atlas on Flickr, which, you guessed it, maps geotagged Flickr photos. What's so unique about Fischer's series of maps is that he focuses on how fast the photographer was moving when they took the picture by comparing time and date stamps on geotagged photos.

Geotagged Flickr Photos in San Francisco by Eric Fischer

In his maps, black lines indicate walking speed (less than 7mph), while red lines approximate bicycling speed (less than 19mph), blue is for motor vehicles on normal roads (less than 43mph) and green indicates freeways or rapid transit. Based on the repetitive tracing, it's possible to see the places within each city that have been photographed and geotagged most frequently. So how might these concentrations of geotagged Flickr photos compare to our maps of urban cyberscapes around the world?

All User-Generated Google Maps Content in San Francisco
Although the purpose and scale of these two visualizations are different, they both show a roughly similar concentration of user-generated content (in either Flickr or Google Maps) around Market Street in San Francisco. Since Fischer did this exercise for 50 different cities around the world, some of which we've already mapped using our own method, the comparisons between the two can go on and on.

Let us know if you find anything else interesting!

1 comment:

  1. Hi all,

    Just wanted to let you know that the creator of these images has made the underlying data available for the geo-afflicted, you can download it here:

    http://sfgeo.dyndns.org

    -- look under "atlas", they are ordered according to the flickr set linked above. You can also download PostGIS dumps of NY and SF and a script for formatting Eric's records into WKT under "contrib", and some other interesting similar data from a public transit agency in San Francisco.

    This is an unusually fun dataset and I hope people dig into it

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