How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data

Author Markus Kattenbeck



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Markus Kattenbeck

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Markus Kattenbeck. How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 10:1-10:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.COSIT.2017.10

Abstract

Theories about salience of landmarks in GIScience have been evolving for about 15 years. This paper empirically analyses hypotheses about the way different subdimensions (visual, structural, and cognitive aspects, as well as prototypicality and visibility in advance) of salience have an impact on each other. The analysis is based on empirical data acquired by means of an in-situ survey (360 objects, 112 participants). It consists of two parts: First, a theory-based structural model is assessed using variance-based Structural Equation Modeling. The results achieved are, second, corroborated by a data-driven approach, i.e. a tree-augmented naive Bayesian network is learned. This network is used as a structural model input for further analyses. The results clearly indicate that the subdimensions of salience influence each other.
Keywords
  • Salience models
  • consistent PLS-SEM Analysis
  • Bayesian Networks

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References

  1. Donald Appleyard. Why Buildings Are Known: A Predictive Tool for Architects and Planners. Environment and Behavior, 1(1):131-156, 1969. Google Scholar
  2. Sierra A. Bainter and Kenneth A. Bollen. Interpretational Confounding or Confounded Interpretations of Causal Indicators? Measurement: Interdisciplinary Research and Perspectives, 12(4):125-140, 2014. URL: http://dx.doi.org/10.1080/15366367.2014.968503.
  3. Ceylan Z. Balaban, Florian Röser, and Kai Hamburger. The effect of emotions and emotionally laden landmarks on wayfinding. In P. Bello, M. Guarani, M. McShane, and B. Scasselati, editors, Proceedings of the 36th Annual Conference of the Cognitive Science Society, Austin, Texas, pages 3315-3320. Cognitive Science Society, 2014. Google Scholar
  4. Nicola J. Bidwell, Christopher Lueg, and Jeff Axup. The territory is the map: designing navigational aids. In Proceedings of the 6th ACM SIGCHI New Zealand Chapter’s International Conference on Computer-human Interaction: Making CHI Natural, CHINZ'05, pages 91-100, New York, NY, USA, 2005. ACM. Google Scholar
  5. David Caduff. Assessing Landmark Salience for Human Navigation. PhD thesis, Mathematisch-naturwissenschaftliche Fakultät der Universität Zürich, Zürich, 2007. Google Scholar
  6. David Caduff and Sabine Timpf. On the assessment of landmark salience for human navigation. Cognitive Processing, 9(4):249-267, 2008. URL: http://dx.doi.org/10.1007/s10339-007-0199-2.
  7. C. Cassel, P. Hackl, and A. H. Westlund. Robustness of partial least squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26(4):435-446, 1999. Google Scholar
  8. Composite Modeling GmbH &Co. KG. ADANCO 2.0, 2015. last access on May 25th, 2017. URL: http://www.composite-modeling.com/.
  9. Clare Davies and Davies Peebles. Spaces or Scenes: Map-based Orientation in Urban Environments. Spatial Cognition &Computation, 10(2-3):135-156, 2010. Google Scholar
  10. Robert F. DeVellis. Scale Development. Theory and Applications. SAGE Publications, Thousand Oaks, CA et al., 3rd edition, 2012. Google Scholar
  11. A. Diamantopoulos and J. A. Siguaw. Formative Versus Reflective Indicators in Organizational Measure Development. British Journal of Management, 17(4):263-282, 2006. Google Scholar
  12. Theo K. Dijkstra and Jörg Henseler. Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics &Data Analysis, 81:10-23, 2015. Google Scholar
  13. Theo K. Dijkstra and Jörg Henseler. Consistent Partial Least Squares Path Modeling. Management Information Systems Quarterly, 39(2):297-316, 2015. Google Scholar
  14. Eibe Frank, Mark A. Hall, and Ian H. Witten. The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques". Morgan Kaufmann, 4th edition, 2016. Google Scholar
  15. Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine Learning, 29:131-163, 1997. Google Scholar
  16. David Gefen, Detmar Straub, and Marie-Claude Boudreau. Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4:Article 7, 2000. Google Scholar
  17. Joseph F. Hair, William C. Black, Barry J. Babin, and Rolph E. Anderson. Multivariate Data Analysis. A Global Perspective. Global Edition. Person Education, Upper Saddle River, NJ, 7th edition, 2010. Google Scholar
  18. Jörg Henseler, Geoffrey Hubona, and Pauline Ash Ray. Using PLS path modeling in new technology research: updated guidelines. Industrial Managment &Data Systems, 116(1):2-20, 2016. Google Scholar
  19. Jörg Henseler, Christian M. Ringle, and Marko Sarstedt. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1):115-135, 2015. Google Scholar
  20. L. Itti. Visual salience [rev 72776]. Scholarpedia, 2(9):3327, 2007. Google Scholar
  21. Cheryl B. Jarvis, Scott B. MacKenzie, and Phil M. Podsakoff. A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. Journal of Consumer Research, 30:199-218, 2003. Google Scholar
  22. Liangxiao Jiang, Harry Zhang, Zhihua Cai, and Jiang Su. Learning tree augmented naive bayes for ranking. In Database Systems for Advanced Applications, pages 688-698. Springer Nature, 2005. Google Scholar
  23. Karl Jöreskog. Simultaneous Factor Analysis in Several Populations. Psychometrika, 36:409-426, 1971. Google Scholar
  24. Markus Kattenbeck. Empirically Measuring Salience of Objects for Use in Pedestrian Navigation. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS'15, pages 3:1-3:10, New York, NY, USA, 2015. ACM. Google Scholar
  25. Markus Kattenbeck. Empirically Measuring Salience of Objects for Use in Pedestrian Navigation. Dissertation, Lehrstuhl für Informationswissenschaft, Universität Regensburg, 2016. URL: http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:bvb:355-epub-341450.
  26. Alexander Klippel and Stephan Winter. Structural salience of landmarks for route directions. In Proceedings of the 2005 International Conference on Spatial Information Theory, COSIT'05, pages 347-362. Springer-Verlag Berlin / Heidelberg, 2005. Google Scholar
  27. Jared Miller and Laura Carlson. Selecting landmarks in novel environments. Psychonomic Bulletin &Review, 18:184-191, 2011. Google Scholar
  28. Yaqing Niu, Rebecca M. Todd, Matthew Kyan, and Adam K. Anderson. Visual and Emotional Salience Influence Eye Movements. ACM Trans. Appl. Percept., 9(3):13:1-13:18, 2012. Google Scholar
  29. Clemens Nothegger, Stephan Winter, and Martin Raubal. Selection of Salient Features for Route Directions. Spatial Cognition &Computation, 4(2):113-136, 2004. Google Scholar
  30. Teriitutea Quesnot and Stéphane Roche. Quantifying the Significance of Semantic Landmarks in Familiar and Unfamiliar Environments. In SaraIrina Fabrikant, Martin Raubal, Michela Bertolotto, Clare Davies, Scott Freundschuh, and Scott Bell, editors, Spatial Information Theory, volume 9368 of Lecture Notes in Computer Science, pages 468-489. Springer International Publishing, 2015. Google Scholar
  31. Martin Raubal and Stephan Winter. Enriching Wayfinding Instructions with Local Landmarks. In Max Egenhofer and David Mark, editors, Geographic Information Science, Lecture Notes in Computer Science, pages 243-259. Springer, Berlin / Heidelberg, 2002. Google Scholar
  32. Kai-Florian Richter. Prospects and Challenges of Landmarks in Navigation Services. In Martin Raubal, David M. Mark, and Andrew U. Frank, editors, Cognitive and Linguistic Aspects of Geographic Space. New Perspectives on Geographic Information Research, Lecture Notes in Geoinformation and Cartography, pages 83-97. Springer, Heidelberg et al., 2013. Google Scholar
  33. Kai-Florian Richter and Stephan Winter. Landmarks. GIScience for Intelligent Services. Springer International Publishing, 2014. Google Scholar
  34. E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes-Braem. Basic Objects in Natural Categories. Cognitive Psychology, pages 382-439, 1976. Google Scholar
  35. Florian Röser. The cognitive observer-based landmark-preference model - What is the ideal landmark position at an intersection? Fachbereich 06: Psychologie und Sportwissenschaften, Justus-Liebig-Universität Giessen, 2015. last access May 2nd, 2016. URL: http://geb.uni-giessen.de/geb/volltexte/2015/11640/pdf/RoeserFlorian_2015_07_15.pdf.
  36. Molly Sorrows and Stephen Hirtle. The Nature of Landmarks for Real and Electronic Spaces. In Christian Freksa and David Mark, editors, Spatial Information Theory. Cognitive and Computational Foundations of Geographic Information Science, Lecture Notes in Computer Science, pages 37-50. Springer, Berlin / Heidelberg, 1999. Google Scholar
  37. Aaron B. Taylor, David P. MacKinnon, and Jenn-Yun Tein. Tests of the Three-Path Mediated Effect. Organizational Research Methods, 11(2):241-269, 2008. Google Scholar
  38. The Inkscape Team. Inkscape 0.91, 2016. last access May 25th, 2017. URL: https://inkscape.org/de/.
  39. Stephan Winter. Route Adaptive Selection of Salient Features. In Walter Kuhn, Michael Worboys, and Sabine Timpf, editors, Spatial Information Theory. Foundations of Geographic Information Science, Lecture Notes in Computer Science, pages 349-361. Springer, Berlin / Heidelberg, 2003. Google Scholar
  40. Stephan Winter, Martin Raubal, and Clemens Nothegger. Focalizing Measures of Salience for Wayfinding. In L. Meng, Zipf A., and T. Reichenbacher, editors, Map-based Mobile Services: Theories, Methods, and Design Implementations, pages 125-139. Springer Geosciences, 2005. Google Scholar
  41. Thomas Wolbers and Mary Hegarty. What determines our navigational abilities? Trends in Cognitive Sciences, 14(3):138-146, 2010. Google Scholar
  42. Herman Ole Andreas Wold. Path models with latent variables: The NIPALS approach. In H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, and V. Capecchi, editors, Quantitative sociology: International perspectives on mathematical and statistical modeling, pages 307-357. Academic Press, New York, 1975. Google Scholar
  43. J. M. Wolfe and J. M. Horowitz. What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5:1-7, 2004. Google Scholar
  44. Wei Wen Wu. Linking Bayesian networks and PLS path modeling for causal analysis. Expert Systems with Applications: An International Journal, 37(1):134-139, 2010. Google Scholar
  45. Xinshu Zhao, John G. Lynch Jr., and Qimei Chen. Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. The Journal of Consumer Research, 37(2):197-206, 2010. Google Scholar