Using Flickr for Characterizing the Environment: An Exploratory Analysis

Authors Shelan S. Jeawak, Christopher B. Jones, Steven Schockaert

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Shelan S. Jeawak
Christopher B. Jones
Steven Schockaert

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Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Using Flickr for Characterizing the Environment: An Exploratory Analysis. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


The photo-sharing website Flickr has become a valuable informal information source in disciplines such as geography and ecology. Some ecologists, for instance, have been manually analysing Flickr to obtain information that is more up-to-date than what is found in traditional sources. While several previous works have shown the potential of Flickr tags for characterizing places, it remains unclear to what extent such tags can be used to derive scientifically useful information for ecologists in an automated way. To obtain a clearer picture about the kinds of environmental features that can be modelled using Flickr tags, we consider the problem of predicting scenicness, species distribution, land cover, and several climate related features. Our focus is on comparing the predictive power of Flickr tags with that of structured data from more traditional sources. We find that, broadly speaking, Flickr tags perform comparably to the considered structured data sources, being sometimes better and sometimes worse. Most importantly, we find that combining Flickr tags with structured data sources consistently, and sometimes substantially, improves the results. This suggests that Flickr indeed provides information that is complementary to traditional sources.
  • Social media
  • Volunteered Geographic Information
  • Ecology


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  1. Vijay V. Barve. Discovering and developing primary biodiversity data from social networking sites. PhD thesis, University of Kansas, 2015. Google Scholar
  2. Ian D. Bishop and David W. Hulse. Prediction of scenic beauty using mapped data and geographic information systems. Landscape and urban planning, 30(1-2):59-70, 1994. Google Scholar
  3. Stefano Casalegno, Richard Inger, Caitlin DeSilvey, and Kevin J. Gaston. Spatial covariance between aesthetic value &other ecosystem services. PloS one, 8(6):e68437, 2013. Google Scholar
  4. Eduardo Cunha and Bruno Martins. Using one-class classifiers and multiple kernel learning for defining imprecise geographic regions. International Journal of Geographical Information Science, 28(11):2220-2241, 2014. Google Scholar
  5. Stefan Daume. Mining Twitter to monitor Invasive Alien Species - An analytical framework and sample information topologies. Ecological Informatics, 31:70-82, 2016. Google Scholar
  6. Janis L. Dickinson, Benjamin Zuckerberg, and David N. Bonter. Citizen science as an ecological research tool: Challenges and benefits. Annual Review of Ecology, Evolution, and Systematics, 41:149-172, Jan-12-2010 2010. Google Scholar
  7. Jacinto Estima, Cidália C Fonte, and Marco Painho. Comparative study of Land Use/Cover classification using Flickr photos, satellite imagery and Corine Land Cover database. In Proceedings of the 17th AGILE International Conference on Geographic Information Science, Castellon, Spain, pages 1-6, 2014. Google Scholar
  8. Jacinto Estima and Marco Painho. Photo based Volunteered Geographic Information initiatives: A comparative study of their suitability for helping quality control of Corine Land Cover. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 5(3):73-89, 2014. Google Scholar
  9. Steffen Fritz, Ian McCallum, C. Schill, C. Perger, L. See, D. Schepaschenko, M. van der Velde, F. Kraxner, and M. Obersteiner". Geo-wiki: An online platform for improving global land cover. Environmental Modelling &Software, 31:110-123, 2012. Google Scholar
  10. Gianfranco Gliozzo, Nathalie Pettorelli, and Mordechai Muki Haklay. Using crowdsourced imagery to detect cultural ecosystem services: a case study in South Wales, UK. Ecology and Society, 21(3), 2016. Google Scholar
  11. Michael F. Goodchild. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4):211-221, 2007. Google Scholar
  12. Christian Grothe and Jochen Schaab. Automated footprint generation from geotags with kernel density estimation and support vector machines. Spatial Cognition &Computation, 9(3):195-211, 2009. Google Scholar
  13. Livia Hollenstein and Ross Purves. Exploring place through user-generated content: Using flickr tags to describe city cores. Journal of Spatial Information Science, 1, 2010. Google Scholar
  14. Shoaib Jameel and Steven Schockaert. Entity embeddings with conceptual subspaces as a basis for plausible reasoning. In Proceedings of the 22nd European Conference on Artificial Intelligence, pages 1353-1361, 2016. Google Scholar
  15. Thorsten Joachims. Making large-scale SVM learning practical. Technical report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund, 1998. Google Scholar
  16. Daniel Leung and Shawn Newsam. Exploring geotagged images for land-use classification. In Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia, pages 3-8, 2012. Google Scholar
  17. Christopher S. Lowry and Michael N. Fienen. Crowdhydrology: Crowdsourcing hydrologic data and engaging citizen scientists. Ground Water, 51(1):151-156, 2013. Google Scholar
  18. Michael J. Paul, Abeed Sarker, John S. Brownstein, Azadeh Nikfarjam, Matthew Scotch, Karen L. Smith, and Graciela Gonzalez. Social media mining for public health monitoring and surveillance. In Pacific Symposium on Biocomputing, pages 468-79, 2016. Google Scholar
  19. Chad D. Pierskalla, Jinyang Deng, and Jason M. Siniscalchi. Examining the product and process of scenic beauty evaluations using moment-to-moment data and GIS: The case of Savannah, GA. Urban Forestry &Urban Greening, 19:212-222, 2016. Google Scholar
  20. Daniel R. Richards and Daniel A. Friess. A rapid indicator of cultural ecosystem service usage at a fine spatial scale: content analysis of social media photographs. Ecological Indicators, 53:187-195, 2015. Google Scholar
  21. Uta Schirpke, Erich Tasser, and Ulrike Tappeiner. Predicting scenic beauty of mountain regions. Landscape and Urban Planning, 111:1-12, 2013. Google Scholar
  22. Pavel Serdyukov, Vanessa Murdock, and Roelof Van Zwol. Placing Flickr photos on a map. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 484-491, 2009. Google Scholar
  23. S. Andrew Sheppard, Andrea Wiggins, and Loren Terveen. Capturing quality: retaining provenance for curated volunteer monitoring data. In Proceedings of the 17th ACM conference on Computer supported cooperative work &social computing, pages 1234-1245, 2014. Google Scholar
  24. B. Stadler, R. Purves, and M. Tomko. Exploring the relationship between land cover and subjective evaluation of scenic beauty through user generated content. In Proceedings of the 25th International Cartographic Conference, 2011. Google Scholar
  25. Patrizia Tenerelli, Urška Demšar, and Sandra Luque. Crowdsourcing indicators for cultural ecosystem services: a geographically weighted approach for mountain landscapes. Ecological Indicators, 64:237-248, 2016. Google Scholar
  26. Steven Van Canneyt, Steven Schockaert, and Bart Dhoedt. Discovering and characterizing places of interest using Flickr and Twitter. Hospitality, Travel, and Tourism: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, page 393, 2014. Google Scholar
  27. Jingya Wang, Mohammed Korayem, and David Crandall. Observing the natural world with Flickr. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 452-459, 2013. Google Scholar
  28. Haipeng Zhang, Mohammed Korayem, David J. Crandall, and Gretchen LeBuhn. Mining photo-sharing websites to study ecological phenomena. In Proceedings of the 21st international conference on World Wide Web, pages 749-758, 2012. Google Scholar
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