Nothing Special   »   [go: up one dir, main page]
Document Open Access Logo

Is Salience Robust? A Heterogeneity Analysis of Survey Ratings

Authors Markus Kattenbeck, Eva Nuhn, Sabine Timpf

PDF
Thumbnail PDF

File

LIPIcs.GISCIENCE.2018.7.pdf
  • Filesize: 497 kB
  • 16 pages

Document Identifiers

Author Details

Markus Kattenbeck
  • University Regensburg, Information Science, 93040 Regensburg, Germany
Eva Nuhn
  • University Augsburg, Geoinformatics Group, 86135 Augsburg, Germany
Sabine Timpf
  • University Augsburg, Geoinformatics Group, 86135 Augsburg, Germany

Cite AsGet BibTex

Markus Kattenbeck, Eva Nuhn, and Sabine Timpf. Is Salience Robust? A Heterogeneity Analysis of Survey Ratings. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.7

Abstract

Differing weights for salience subdimensions (e.g. visual or structural salience) have been suggested since the early days of salience models in GIScience. Up until now, however, it remains unclear whether weights found in studies are robust across environments, objects and observers. In this study we examine the robustness of a survey-based salience model. Based on ratings of N_{o}=720 objects by N_{p}=250 different participants collected in-situ in two different European cities (Regensburg and Augsburg) we conduct a heterogeneity analysis taking into account environment and sense of direction stratified by gender. We find, first, empirical evidence that our model is invariant across environments, i.e. the strength of the relationships between the subdimensions of salience does not differ significantly. The structural model coefficients found can, hence, be used to calculate values for overall salience across different environments. Second, we provide empirical evidence that invariance of our measurement model is partly not given with respect to both, gender and sense of direction. These compositional invariance problems are a strong indicator for personal aspects playing an important role.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Multivariate statistics
  • Human-centered computing → Personal digital assistants
  • Human-centered computing → Empirical studies in ubiquitous and mobile computing
Keywords
  • Salience Model
  • Measurement Invariance
  • Heterogeneity Analysis
  • PLS Path Modeling
  • Structural Equation Models

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Donald Appleyard. Why buildings are known: A predictive tool for architects and planners. Environment and Behavior, 1(2):131-156, 1969. Google Scholar
  2. Kenneth A. Bollen. Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models. Management Information Systems Quarterly, 35(2):359-372, 2011. Google Scholar
  3. Gary Burnett. "Turn right at the Traffic Lights": The Requirement for Landmarks in Vehicle Navigation Systems. Journal of Navigation, 53(3):499-510, 2000. Google Scholar
  4. David Caduff and Sabine Timpf. On the assessment of landmark salience for human navigation. Cognitive processing, 9(4):249-267, 2008. Google Scholar
  5. Edgar Chan, Oliver Baumann, Mark Bellgrove, and Jason Mattingley. From objects to landmarks: The function of visual location information in spatial navigation. Frontiers in Psychology, 3:304, 2012. Google Scholar
  6. Emanuele Coluccia and Giorgia Louse. Gender differences in spatial orientation: A review. Journal of Environmental Psychology, 24(3):329-340, 2004. Google Scholar
  7. Theo K. Dijkstra and Jörg Henseler. Consistent Partial Least Squares Path Modeling. Management Information Systems Quarterly, 39(2):297-316, 2015. Google Scholar
  8. Matt Duckham, Stephan Winter, and Michelle Robinson. Including landmarks in routing instructions. Journal of location based services, 4(1):28-52, 2010. Google Scholar
  9. Birgit Elias. Extracting landmarks with data mining methods. In W Kuhn, M Worboys, and S Timpf, editors, Spatial Information Theory, Proceedings: Foundations of Geographic Information Science, volume 2825 of LNCS, pages 375-389, 2003. Google Scholar
  10. Claes G. Fornell and F. L. Bookstein. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19:440-452, 1982. Google Scholar
  11. Jana Götze and Johan Boye. Learning landmark salience models from users’ route instructions. Journal of Location Based Services, 10(1):47-63, 2016. Google Scholar
  12. Joseph F. Hair, William C. Black, Barry J. Babin, and Rolph E. Anderson. Multivariate Data Analysis. A Global Perspective. Person Education, 7th edition, 2010. Google Scholar
  13. Joseph F. Hair, Christian M. Ringle, and Marko Sarstedt. Pls-sem: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2):139-151, 2011. Google Scholar
  14. Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Los Angeles et al., 2014. Google Scholar
  15. Jörg Henseler. On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1):107-120, 2010. Google Scholar
  16. Jörg Henseler, Christian M. Ringle, and Marko Sarstedt. Testing Measurement Invariance of Composites Using PLS. International Marketing Review, 33(3):405-431, 2016. Google Scholar
  17. F. Joseph F. Hair Jr., Joseph, G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Los Angeles et al., 2 edition, 2018. Google Scholar
  18. 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, pages 3:1-3:10, New York, NY, USA, 2015. Google Scholar
  19. Markus Kattenbeck. Empirically Measuring Salience of Objects for Use in Pedestrian Navigation. Dissertation, Chair for Information Science, University of Regensburg, 2016. URL: http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:bvb:355-epub-341450.
  20. Markus Kattenbeck. How subdimensions of salience influence each other. comparing models based on empirical data. In E. Clementini, M. Donnelly, M. Yuan, Ch. Kray, P. Fogliaroni, and A. Ballatore, editors, 13th International Conference on Spatial Information Theory (COSIT 2017), pages 10:1-10:13. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2017. Google Scholar
  21. David A. Kenny. Correlation and Causality. John Wiley &Sons, 1979. Google Scholar
  22. Pyry Kettunen, Katja Irvankoski, Christina M. Krause, and L. Tiina. Sarjakoski. Landmarks in nature to support wayfinding: the effects of seasons and experimental methods. Cognitive Processing, 14(3):245-253, Aug 2013. Google Scholar
  23. Alexander Klippel and Stephan Winter. Structural salience of landmarks for route directions. In Anthony G. Cohn and David M. Mark, editors, Spatial Information Theory. COSIT 2005. LNCS vol 3693, pages 347-362. Springer Berlin Heidelberg, 2005. Google Scholar
  24. Carol A. Lawton, Stephanie I. Charleston, and Amy S. Zieles. Individual- and gender-related differences in indoor wayfinding. Environment and Behavior, 28(2):204-219, 1996. Google Scholar
  25. Kevin Lynch. The image of the city. MIT press, 1960. Google Scholar
  26. Andrew J. May and Tracy Ross. Presence and quality of navigational landmarks: Effect on driver performance and implications for design. Human Factors, 48(2):346-361, 2006. Google Scholar
  27. Stefan Münzer and Christoph Hölscher. Entwicklung und Validierung eines Fragebogens zu räumlichen Strategien. Diagnostica, 57(3):111-125, 2011. Google Scholar
  28. Stefan Münzer and Christoph Hölscher. Standardized norm data for three self-report scales on egocentric and allocentric environmental spatial strategies. Data in Brief, 8:803-811, 2016. 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. Eva Nuhn and Sabine Timpf. A multidimensional model for selecting personalised landmarks. Journal of Location Based Services, 11(3-4):1-28, 2017. Google Scholar
  31. Luciana Picucci, Alessandro O. Caffò, and Andrea Bosco. Besides navigation accuracy: Gender differences in strategy selection and level of spatial confidence. Journal of Environmental Psychology, 31(4):430-438, 2011. Google Scholar
  32. Teriitutea Quesnot and Stéphane Roche. Quantifying the significance of semantic landmarks in familiar and unfamiliar environments. In Sara Irina Fabrikant, Martin Raubal, Michaela Bertolotto, Clare Davies, Scott Freundschuh, and Scott Bell, editors, Spatial Information Theory, volume 9368 of LNCS, pages 468-489. Springer, 2015. Google Scholar
  33. Martin Raubal and Stephan Winter. Enriching wayfinding instructions with local landmarks. In Max J. Egenhofer and David M. Mark, editors, Geographic Information Science. GIScience 2002. LNCS vol 2478, pages 243-259. Springer, 2002. Google Scholar
  34. Kai-Florian Richter and Stephan Winter. Landmarks. GIScience for Intelligent Services. Springer International Publishing, 2014. Google Scholar
  35. Christian M. Ringle, Sven Wende, and Jan-Michael Becker. SmartPLS 3, 2015. Retrieved from URL: http://www.smartpls.com.
  36. Eleanor Rosch, Carolyn B Mervis, Wayne D Gray, David M Johnson, and Penny Boyes-Braem. Basic objects in natural categories. Cognitive Psychology, 8(3):382-439, 1976. Google Scholar
  37. Florian Röser, Antje Krumnack, Kai Hamburger, and Markus Knauff. A four factor model of landmark salience-a new approach. In Proceedings of the 11th International Conference on Cognitive Modeling (ICCM), pages 82-87, 2012. Google Scholar
  38. Ahmed Sameer and Braj Bhushan. Effect of landmark type on route memory in unfamiliar homogenous environment. Psychological Studies, 62(2):152-159, Jun 2017. Google Scholar
  39. Molly E. Sorrows and Stephen C. Hirtle. The nature of landmarks for real and electronic spaces. In Christian Freksa and David M. Mark, editors, Spatial Information Theory. Cognitive and Computational Foundations of Geographic Information Science. COSIT 1999. LNCS vol 1661, pages 37-50. Springer, 1999. Google Scholar
  40. Lynn A. Streeter and Dan Vitello. A profile of drivers’ map-reading abilities. Human Factors, pages 223-239, 1986. Google Scholar
  41. Rul von Stülpnagel and Julia Frankenstein. Configurational salience of landmarks: an analysis of sketch maps using space syntax. Cognitive Processing, 16(1):437-441, Sep 2015. Google Scholar
  42. Stephan Winter. Route Adaptive Selection of Salient Features. In Werner Kuhn, Michael Worboys, and Sabine Timpf, editors, Spatial Information Theory. Foundations of Geographic Information Science, LNCS, pages 349-361. Springer, Berlin / Heidelberg, 2003. Google Scholar
  43. Kathryn Wochinger and Deborah Boehm-Davis. Navigational preference and driver acceptance of advanced traveler information systems. Ergonomics and safety of intelligent driver interfaces, pages 345-362, 1997. Google Scholar
  44. Herman Ole Andreas Wold. Soft Modelling: The Basic Design and Some Extensions. In K. G. Jöreskog and H. Wold, editors, Systems under indirect observation. Causality, structure, prediction, part II, pages 1-54. North-Holland, Amsterdam, 1982. Google Scholar
  45. Herman Ole Andreas Wold. Partial Least Squares Regression. In S. Kotz and N. L. Johnson, editors, Encyclopedia of Statistical Sciences, pages 581-591. John Wiley, New York, 1985. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact