Landscape Perception

  • Oliver Nakoinz
  • Daniel Knitter
Part of the Quantitative Archaeology and Archaeological Modelling book series (QAAM)


While most chapters deal with rather objective parameters that are independent from individuals, this chapter introduces a certain degree of subjectivity by addressing the perception of landscapes. Accordingly, we change our perspective from a distant scientific position to a viewpoint of the ancient observer. We distinguish between sensual and cognitive perception: the former concerns people can see in the landscape, while the latter is about what emerges in the mind. The first step is the analysis of visibility, which is still based upon environmental parameters, while the interpretation of the results strongly draws upon cultural conditions. Since categorising is an important cognitive technique, we have to deal with it, applying a fuzzy classification. Finally, we try to reconstruct a cognitive map, which is intended to map the topology of topographic objects in our mind. Cognitive maps of cultural landscapes are a kind of subjective view of the landscape, which involves transforming distances, angles and symbols. For the distortion of the background map, we need to apply a rubber sheet transformation.


Landscape perception Cognitive maps Fuzzy classification View-shed analysis Coordinate transformation Rubber sheeting 


  1. 1.
    Baxter, M. (2009). Archaeological data analysis and fuzzy clustering. Archaeometry, 51, 1035–1054.CrossRefGoogle Scholar
  2. 2.
    Bender, B. (1993). Introduction: Landscapes - meaning and action. In B. Bender (Ed.), Landscape: Politics and perspectives (pp. 1–18). Oxford: Berg.Google Scholar
  3. 3.
    Burleigh, T. J., & Schoenherr, J. R. (2015). A reappraisal of the uncanny valley: Categorical perception or frequency-based sensitization? Frontiers in Psychology, 5, 1488.CrossRefGoogle Scholar
  4. 4.
    Clarke, K. C. (1995). Analytical and computer cartography. Upper Saddle River: Prentice Hall.Google Scholar
  5. 5.
    Conolly, J., & Lake, M. (2006). Geographical information systems in archaeology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. 6.
    Estes, W. K. (1994). Classification and cognition. New York: Oxford University Press.CrossRefGoogle Scholar
  7. 7.
    Gillman, D. W. (1985). Triangulations for rubber-sheeting. In Auto-Carto Proceedings of the International Symposium on Computer-Assisted Cartography. Falls Church: American Society of Photogrammetry.Google Scholar
  8. 8.
    Hermon, S., & Nicculucci, F. (2002). Estimating subjectivity of typologists and typological classification with fuzzy logic. Archeologia e Calcolatori, 13, 217–232.Google Scholar
  9. 9.
    Ingold, T. (2000). The perception of the environment: Essays on livelihood, dwelling and skill. Routledge: London.CrossRefGoogle Scholar
  10. 10.
    Kruschke, J. K. (2005). Category learning. In K. Lamberts, & L. Goldstone (Eds.), Handbook of cognition (pp. 183–201). London: Sage.Google Scholar
  11. 11.
    Knitter, D., & Nakoinz, O. (in press): Point pattern analysis as tool for digital geoarchaeology – A case study of megalithic graves in Schleswig-Holstein, Germany. In C. Siart, M. Forbirger & O. Bubenzer (Eds.), Digital Geoarchaeology. New Techniques for Interdisciplinary Human-Environmental Research, Springer.Google Scholar
  12. 12.
    Lamberts, K., & Shanks, D. R. (Eds.). (1997). Knowledge, concepts and categories. Cambridge, Mass.: MIT Press.Google Scholar
  13. 13.
    van Leusen, M. (2004). Visibility and the landscape. An exploration of GIS modelling techniques? In K. Fischer Ausserer, W. Böner, M. Goriany & L. Karlhuber-Vöckl (Eds.), Entering the past: The e-way into the four dimensions of cultural heritage. Proceedings of the 31st CAA Conference, Vienna, Austria, April 2003. BAR International Series (Vol. 1227, pp. 1–15). Oxford: British Archaeological Reports.Google Scholar
  14. 14.
    Machálek, T., Cimler, R., Olševičová, K., & Danielisová, A. (2013). Fuzzy methods in land use modeling for archaeology. In H. Vojackova (Ed.), Proceedings of the 31st International Conference Mathematical Methods in Economics (pp. 552–557). Jihlava: College of Polytechnics Jihlava.Google Scholar
  15. 15.
    Meier, T. (2012). ‘Landscape’, ‘environment’ and a vision of interdisciplinarity. In S. Kluiving & E. Guttmann-Bond (Eds.), Landscape archaeology between art and science (pp. 503–514). Amsterdam: Amsterdam University Press.Google Scholar
  16. 16.
    Nakoinz, O. (2006). Kommunikation und Kontrolle zur Wikingerzeit in der Kieler Bucht - Ein Beitrag zur Methode der Sichtanalyse. Archéologie in Schleswig, 11, 95–103.Google Scholar
  17. 17.
    Nakoinz, O. (2012). Datierungskodierung und chronologische Inferenz - Techniken zum Umgang mit unscharfen chronologischen Informationen. Praehistorische Zeitschrift, 87, 189–207.CrossRefGoogle Scholar
  18. 18.
    Rášová, A. (2014). Fuzzy viewshed, probable viewshed, and their use in the analysis of prehistoric monuments placement in Western Slovakia. In J. Huerta, S. Schade & C. Granell (Eds.), Connecting a digital Europe through location and place (AGILE Digital Editions). Cham: Springer. Scholar
  19. 19.
    Shemyakin, F. N. (1961). Orientation in space. Psychological Science in the USSR, 1, 186–255.Google Scholar
  20. 20.
    Wheatley, D., & Gillings, M. (2002). Spatial technology and archaeology: The archaeological applications of GIS. London: CRC Press.CrossRefGoogle Scholar
  21. 21.
    Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
  22. 22.
    Zadeh, L. A. (2006). Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis, 51, 15–46.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Oliver Nakoinz
    • 1
  • Daniel Knitter
    • 2
  1. 1.University of KielKielGermany
  2. 2.Excellence Cluster TopoiFreie UniversitätBerlinGermany

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