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A Crowdsourced Model of Landscape Preference

Authors Olga Chesnokova, Mario Nowak, Ross S. Purves

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Olga Chesnokova
Mario Nowak
Ross S. Purves

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Olga Chesnokova, Mario Nowak, and Ross S. Purves. A Crowdsourced Model of Landscape Preference. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 19:1-19:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.COSIT.2017.19

Abstract

The advent of new sources of spatial data and associated information (e.g. Volunteered Geographic Information (VGI)) allows us to explore non-expert conceptualisations of space, where the number of participants and spatial extent coverage encompassed can be much greater than is available through traditional empirical approaches. In this paper we explore such data through the prism of landscape preference or scenicness. VGI in the form of photographs is particularly suited to this task, and the volume of images has been suggested as a simple proxy for landscape preference. We propose another approach, which models landscape aesthetics based on the descriptions of some 220000 images collected in a large VGI project in the UK, and more than 1.5 million votes related to the perceived scenicness of these images collected in a crowdsourcing project. We use image descriptions to build features for a supervised machine learning algorithm. Features include the most frequent uni- and bigrams, adjectives, presence of verbs of perception and adjectives from the "Landscape Adjective Checklist". Our results include not only qualitative information relating terms to scenicness in the UK, but a model based on our features which can predict some 52% of the variation in scenicness, comparable to typical models using more traditional approaches. The most useful features are the 800 most frequent unigrams, presence of adjectives from the "Landscape Adjective Checklist" and a spatial weighting term.

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Keywords
  • VGI
  • crowdsourcing
  • semantics
  • landscape preference

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References

  1. J. Appleton. Prospect and Refuges Revisited. In J. L. Nasar, editor, Environmental Aesthetics: Theory, Research and Applications, pages 27-44. Cambridge University Press, 1988. Google Scholar
  2. J. A. Benfield, P. A. Bell, L. J. Troup, and N. C. Soderstrom. Aesthetic and affective effects of vocal and traffic noise on natural landscape assessment. Journal of Environmental Psychology, 30(1):103-111, 2010. URL: http://dx.doi.org/10.1016/j.jenvp.2009.10.002.
  3. S. Casalegno, R. Inger, C. DeSilvey, and K. J. Gaston. Spatial Covariance between Aesthetic Value & Other Ecosystem Services. PLOS ONE, 8(6), 2013. URL: http://dx.doi.org/10.1371/journal.pone.0068437.
  4. A. Criminisi, J. Shotton, and E. Konukoglu. Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends® in Computer Graphics and Vision, 7(2-3):81-227, 2012. Google Scholar
  5. A. Dunkel. Visualizing the perceived environment using crowdsourced photo geodata. Landscape and Urban Planning, 142:173-186, 2015. URL: http://dx.doi.org/10.1016/j.landurbplan.2015.02.022.
  6. A. J. Edwardes and R. S. Purves. A theoretical grounding for semantic descriptions of place. Proceedings of the 7th international conference on Web and wireless geographical information systems, pages 106-120, 2007. URL: http://dx.doi.org/10.1007/978-3-540-76925-5_8.
  7. S. Elwood, M. F. Goodchild, and D. Z. Sui. Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3):571-590, 2012. URL: http://dx.doi.org/10.1080/00045608.2011.595657.
  8. P. Fisher and D. J. Unwin. Re-presenting Geographical Information Systems. In Fisher, Peter and Unwin, David J., editor, Re-presenting GIS, pages 1-14. Wiley Sons London, London, 2005. Google Scholar
  9. S. Frank, Ch. Fürst, A. Witt, L. Koschke, and F. Makeschin. Making use of the ecosystem services concept in regional planning-trade-offs from reducing water erosion. Landscape Ecology, pages 1-15, 2014. URL: http://dx.doi.org/10.1007/s10980-014-9992-3.
  10. F. Girardin, J. Blat, F. Calabrese, F. Dal Fiore, and C. Ratti. Digital footprinting: Uncovering tourists with user-generated content. IEEE Pervasive Computing, 7(4):36-44, 2008. URL: http://dx.doi.org/10.1109/MPRV.2008.71.
  11. G. Gliozzo, N. Pettorelli, and M. Haklay. Using crowdsourced imagery to detect cultural ecosystem services: a case study in South Wales, UK. Ecology and Society, 21(3), 2016. URL: http://dx.doi.org/10.5751/ES-08436-210306.
  12. M. F. Goodchild. Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4):211-221, 2007. URL: http://dx.doi.org/10.1007/s10708-007-9111-y.
  13. M. Haklay. Why is participation inequality important? In C. Capineri, M. Haklay, H. Huang, V. Antoniou, J. Kettunen, F. Ostermann, and R. Purves, editors, European Handbook of Crowdsourced Geographic Information, pages 35-44. Ubiquity Press, London, 2016. Google Scholar
  14. V. Hatzivassiloglou and J. M. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. Proceedings of the 18th conference on Computational linguistics-Volume 1, pages 299-305, 2000. URL: http://dx.doi.org/10.3115/990820.990864.
  15. M. Hunziker, M. Buchecker, and T. Hartig. Space and Place – Two Aspects of the Human-landscape Relationship. Challenges for Landscape Research, pages 47-62, 2007. URL: http://dx.doi.org/10.1007/978-1-4020-4436-6_5.
  16. A. Jenkins, A. Croitoru, A. T. Crooks, and A. Stefanidis. Crowdsourcing a Collective Sense of Place. Plos One, 11(4):e0152932, 2016. URL: http://dx.doi.org/10.1371/journal.pone.0152932.
  17. X. Junge, B. Schüpbach, T. Walter, B. Schmid, and P. Lindemann-Matthies. Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landscape and Urban Planning, 133:67-77, 2015. URL: http://dx.doi.org/10.1016/j.landurbplan.2014.09.010.
  18. R. Kaplan and S. Kaplan. The experience of nature: a psychological perspective. Cambridge University Press, 1989. Google Scholar
  19. A. Lothian. Landscape and the philosophy of aesthetics: Is landscape quality inherent in the landscape or in the eye of the beholder? Landscape and Urban Planning, 44(4):177-198, 1999. URL: http://dx.doi.org/10.1016/S0169-2046(99)00019-5.
  20. A. Lothian. Scenic perceptions of the visual effects of wind farms on South Australian landscapes. Geographical Research, 46(2):196-207, 2008. URL: http://dx.doi.org/10.1111/j.1745-5871.2008.00510.x.
  21. Ch. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Massachusetts, 1999. URL: http://dx.doi.org/10.1145/601858.601867.
  22. D. M. Mark, A. G. Turk, N. Burenhult, and D. Stea, editors. Landscape in language. Transdisciplinary perspectives. John Benjamins, Amsterdam/Philadelphia, 2011. Google Scholar
  23. J. Mascaro, G. P. Asner, D. E. Knapp, T. Kennedy-Bowdoin, R. E. Martin, Ch. Anderson, M. Higgins, and K. D. Chadwick. A tale of two "Forests": Random Forest machine learning aids tropical Forest carbon mapping. PLoS ONE, 9(1):12-16, 2014. URL: http://dx.doi.org/10.1371/journal.pone.0085993.
  24. J. L. Nasar. Environmental Aesthetics: Theory, research and Application. Cambridge edition, 1992. Google Scholar
  25. J. F. Palmer. Using spatial metrics to predict scenic perception in a changing landscape: Dennis, Massachusetts. Landscape and Urban Planning, 69(2-3):201-218, 2004. URL: http://dx.doi.org/10.1016/j.landurbplan.2003.08.010.
  26. B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. Empirical Methods in Natural Language Processing (EMNLP), 10(July):79-86, 2002. URL: http://dx.doi.org/10.3115/1118693.1118704.
  27. T. Plieninger, S. Dijks, E. Oteros-Rozas, and C. Bieling. Assessing, mapping, and quantifying cultural ecosystem services at community level. Land Use Policy, 33:118-129, 2013. URL: http://dx.doi.org/10.1016/j.landusepol.2012.12.013.
  28. R. S. Purves, A. J. Edwardes, and J. Wood. Describing place through user generated content. First Monday. Peer-reviewed journal on the internet, 16(9), 2011. URL: http://dx.doi.org/10.5210/fm.v16i9.3710.
  29. T. Rattenbury and M. Naaman. Methods for extracting place semantics from Flickr tags. ACM Transactions on the Web, 3(1):1-30, 2009. URL: http://dx.doi.org/10.1145/1462148.1462149.
  30. D. R. Richards and D. 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. URL: http://dx.doi.org/10.1016/j.ecolind.2015.01.034.
  31. U. Schirpke, E. Tasser, and U. Tappeiner. Predicting scenic beauty of mountain regions. Landscape and Urban Planning, 111(1):1-12, 2013. URL: http://dx.doi.org/10.1016/j.landurbplan.2012.11.010.
  32. C. I. Seresinhe, H. S. Moat, and T. Preis. Quantifying scenic areas using crowdsourced data. Environment and Planning B: Urban Analytics and City Science, page 026581351668730, 2017. URL: http://dx.doi.org/10.1177/0265813516687302.
  33. C. I. Seresinhe, T. Preis, and H. S. Moat. Quantifying the Impact of Scenic Environments on Health. Scientific Reports, 5(Article number 16899):1-9, 2015. URL: http://dx.doi.org/10.1038/srep16899.
  34. B. Smith and D. M. Mark. Geographical categories: an ontological investigation. International Journal of Geographical Information Science, 15(7):591-612, 2001. URL: http://dx.doi.org/10.1080/13658810110061199.
  35. 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, Paris, 2011. URL: http://dx.doi.org/10.5167/uzh-52945.
  36. P. Tenerelli, U. Demšar, and S. Luque. Crowdsourcing indicators for cultural ecosystem services: A geographically weighted approach for mountain landscapes. Ecological Indicators, 64:237-248, 2016. URL: http://dx.doi.org/10.1016/j.ecolind.2015.12.042.
  37. B. T. van Zanten, D. B. van Berkel, R. K. Meetemeyer, J. W. Smith, K. F. Tieskens, and P. H. Verburg. Continental scale quatification of landscape values using social media data. Proceedings of the National Academy of Sciences, pages 1-7, 2016. URL: http://dx.doi.org/10.1073/pnas.1614158113.
  38. B. T. van Zanten, P. H. Verburg, M. J. Koetse, and P. J. H. van Beukering. Preferences for European agrarian landscapes: A meta-analysis of case studies. Landscape and Urban Planning, 132:89-101, 2014. URL: http://dx.doi.org/10.1016/j.landurbplan.2014.08.012.
  39. A. Viberg. The verbs of perception: a typological study. Linguistics, 21(1):123-162, 2009. URL: http://dx.doi.org/10.1515/ling.1983.21.1.123.
  40. B. Yang and T. J. Brown. A Cross-Cultural Comparison of Preferences for Landscape Styles and Landscape Elements. Environment and Behavior, 24(4):471-507, 1992. URL: http://dx.doi.org/10.1177/0013916592244003.
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