Volume II-3/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 311-316, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 311-316, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 Aug 2015

20 Aug 2015

EXTRACTING AND COMPARING PLACES USING GEO-SOCIAL MEDIA

F. O. Ostermann1, H. Huang2, G. Andrienko3, N. Andrienko3, C. Capineri4, K. Farkas5, and R. S. Purves6 F. O. Ostermann et al.
  • 1Department of Geo-Information Processing (ITC), University of Twente, Enschede, the Netherlands
  • 2Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
  • 3Fraunhofer Institute IAIS, Sankt Augustin, Germany/City University London, London, UK
  • 4Dipartimento Scienze Sociali Politiche e Cognitive, Università di Siena, Siena, Italy - cristina.capineri@unisi.it
  • 5Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
  • 6Department of Geography, University of Zürich, Zürich, Switzerland

Keywords: User-generated Geographic Content, Volunteered Geographic Information, Geo-social Media, Semantic Similarity, Geographic Places

Abstract. Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.