You have just completed your registration at OpenAire.
Before you can login to the site, you will need to activate your account.
An e-mail will be sent to you with the proper instructions.
Important!
Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version
of the site upon release.
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.
Agnew, J., 1987. Place and Politics: The Geographical Mediation of State and Society. Boston and London: Allen and Unwin.
Agnew, J., 2011. Space and place. In: Agnew, J., Livingstone D. (eds.). The SAGE handbook of geographical knowledge, London, SAGE Publications Ltd., pp. 316-330.
Frémont, A., 1999. La region, espace vécu. Flammarion.
Journal of Spatial Information Science, 9, 1-36 Hecht, B., Hong, L., Suh B., Chi E. 2011. Tweets from Justin Bieber's Heart: The Dynamics of the Location Field in User Profiles. In Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 237-46. Vancouver, BC, Canada: ACM.
Huang, H., Gartner, G., Turdean, T., 2013. Social media data as a source for studying people's perception and knowledge of environments. MÖGG Mitteilungen der Österreichischen Geographischen Gesellschaft (Communications of Austrian Geographical Society), 155, pp. 291-302.
Li, T., Sen, S., Hecht, B., 2014. Leveraging Advances in Natural Language Processing to Better Understand Tobler's First Law of Geography. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM Press.
Ostermann, F.O., Tomko, M., Purves, R.S. 2013. User Evaluation of Automatically Generated Keywords and Toponyms for Geo-Referenced Images. Journal of the American Society for Information Science and Technology 64 (3): 480-99..
Purves, R.S., Edwardes, A., Wood, J., 2011. Describing Place through User Generated Content. First Monday, Volume 16, Number 9 - 5 September 2011.
Purves, R.S., Derungs, C., 2015. From space to place: placebased explorations of texts. International Journal of Humanities and Arts Computing, 9(1), pp. 74-94.
Sigurbjörnsson, B., van Zwol, R. 2008. Flickr tag recommendation based on collective knowledge. In WWW '08: Proceeding of the 17th International Conference on World Wide Web:327-336.
Steiger, E., Albuquerque, J., Zipf, A., 2015. An Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data. Transactions in GIS, doi:10.1111/tgis.12132.
Teobaldi, M., Capineri, C., 2014. Experiential tourism and city attractivness in Tuscany. Rivista Geografica Italiana,121, pp.259-274
Tversky, B., Hemenway, K., 1983. Categories of environmental scenes. Cognitive Psychology, 15, pp. 121-149.
Vockner, B., Richter, A., Mittlböck, M.. 2013. From Geoportals to Geographic Knowledge Portals. ISPRS International Journal of Geo-Information 2 (2): 256-75.