Twitterlandschaft (german for Twitterlandscape) is a touchtable-based, interactive information visualisation, which explores the digital closeness between different cities (respectively their inhabitants) by utilizing Twitter data. This student project was created together with Gero Fallisch under lecturer Till Nagel at University of Applied Sciences Potsdam (Interface Design degree programme).
The visualisation consists of two different views: a world map view and a map of the world from the point of view of a selected city.
Any geocoded Twitter activity is shown live on the world map view. A flashing city name, mapped on its geographical position, signals the sending of a geocoded Tweet from that location. Places of high Twitter activity are displayed permanently by a black bordered circle. The size of the circle is unrelated to the size of the location in the real world, it displays the level of Twitter activity at this location. To prevent overlap, bigger circles (high Twitter activity locations) might move smaller circles (lower Twitter activity locations) off their real geographical position. If Twitter activity of a location exceeds a certain threshold, this location is marked blue.
Tapping of a white circle displays the associated city name and tapping of a blue marked circle switches the map to the point of view of the associated city.
Switching from the world map to the city view moves the map so that the selected city is in the centre of the screen. Places which are connected to the selected city, move towards it according to the virtual closeness. The strength of the virtual connection or closeness (which is the amount of Tweets a place receives from the selected city) is represented by a light brown border that is drawn around the location circles. Places without any connection to the selected city vanish from the map. Rolling lines show live Tweets from the selected city to other locations. The above image shows the Twitter landscape from the view of Singapore.
Questions which we asked ourselves and tried to answer with this visualisation were:
- Which big/important places are not represented through Twitter?
- Which small/unknown places become important through Twitter?
- Which geographically close places are far apart or completely
- disconnected in the Twitter landscape (and vice versa)?