Document Open Access Logo

Is Familiarity Reflected in the Spatial Knowledge Revealed by Sketch Maps?

Authors Markus Kattenbeck , Daniel R. Montello, Martin Raubal , Ioannis Giannopoulos

PDF
Thumbnail PDF

File

LIPIcs.COSIT.2024.6.pdf
  • Filesize: 1.72 MB
  • 18 pages

Document Identifiers

Author Details

Markus Kattenbeck
  • Research Division Geoinformation, Department of Geodesy and Geoinformation, TU Wien, Austria
Daniel R. Montello
  • Geography Department, University of California Santa Barbara, CA, USA
Martin Raubal
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland
Ioannis Giannopoulos
  • Research Division Geoinformation, Department of Geodesy and Geoinformation, TU Wien, Austria

Funding

  • Kattenbeck, Markus: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101026774.

Acknowledgements

We would like to thank our study participants for their time to support our research. We are also grateful to Owen Crosby and Mahala Randel for their very valuable assistance in analyzing the sketch maps.

Cite AsGet BibTex

Markus Kattenbeck, Daniel R. Montello, Martin Raubal, and Ioannis Giannopoulos. Is Familiarity Reflected in the Spatial Knowledge Revealed by Sketch Maps?. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 6:1-6:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.COSIT.2024.6

Abstract

Despite the frequent use of sketch maps in assessing environmental knowledge, it remains unclear how and to what degree familiarity impacts sketch map content. In the present study, we assess whether different levels of familiarity relate to differences in the content and spatial accuracy of environmental knowledge depicted in sketch maps drawn for the purpose of route instructions. To this end, we conduct a real-world wayfinding study with 91 participants, all of whom have to walk along a pre-defined route of approximately 2.3km length. Prior to the walk, we collect self-report familiarity ratings from participants for both a set of 15 landmarks and a set of areas we define as hexagons along the route. Once participants finished walking the route, they were asked to sketch a map of the route, specifically a sketch that would enable a person who had never walked the route to follow it. We found that participants unfamiliar with the areas along the route sketched fewer features than familiar people did. Contrary to our expectations, however, we found that landmarks were sketched or not regardless of participants' level of familiarity with the landmarks. We were also surprised that the level of familiarity was not correlated to the accuracy of the sketched order of features along the route, of the position of sketched features in relation to the route, nor to the metric locational accuracy of feature placement on the sketches. These results lead us to conclude that different aspects of feature salience influence whether the features are included on sketch maps, independent of familiarity. They also point to the influence of task context on the content of sketch maps, again independent of familiarity. We propose further studies to more fully explore these ideas.

Subject Classification

ACM Subject Classification
  • General and reference → Empirical studies
  • Applied computing → Psychology
Keywords
  • Familiarity
  • Spatial Knowledge
  • Sketch Maps

Metrics

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

Supplementary Materials

References

  1. Roger S. Bivand, Edzer Pebesma, and Virgilio Gomez-Rubio. Applied spatial data analysis with R, Second edition. Springer, NY, 2013. URL: https://asdar-book.org/.
  2. Szu-Miao Chen, Yi-Shin Deng, Sheng-Fen Chien, and Hsiao-Chen You. Enhance User Experience Moving in Campus through Understanding Human Spatial Cognition. In A. Marcus, editor, DUXU 2014, Part III, pages 265-272. Springer, 2014. URL: https://doi.org/10.1007/978-3-319-07635-5_26.
  3. Saif Haq and Craig Zimring. Just down the road a piece: The development of topological knowledge of building layouts. Environment and Behavior, 35(1):132-160, January 2003. URL: https://doi.org/10.1177/0013916502238868.
  4. W. Andrew Harrell, Jeffrey W. Bowlby, and Deana Hall-hoffarth. Directing Wayfinders With Maps: The Effects of Gender, Age, Route Complexity, and Familiarity With the Environment. The Journal of Social Psychology, 140(2):169-178, April 2000. URL: https://doi.org/10.1080/00224540009600456.
  5. Mary Hegarty, Anthony E Richardson, Daniel R Montello, Kristin Lovelace, and Ilavanil Subbiah. Development of a self-report measure of environmental spatial ability. Intelligence, 30(5):425-447, 2002. Google Scholar
  6. Mark Horan. What students see: Sketch maps as tools for assessing knowledge of libraries. Journal of Academic Librarianship, 25(3):187-201, 1999. URL: https://doi.org/10.1016/s0099-1333(99)80198-0.
  7. Haosheng Huang, Manuela Schmidt, and Georg Gartner. Spatial Knowledge Acquisition with Mobile Maps, Augmented Reality and Voice in the Context of GPS-based Pedestrian Navigation: Results from a Field Test. Cartography and Geographic Information Science, 39(2):107-116, 2012. URL: https://doi.org/10.1559/15230406392107.
  8. Kateřina Hátlová and Martin Hanus. A systematic review into factors influencing sketch map quality. ISPRS International Journal of Geo-Information, 9(4), 2020. URL: https://doi.org/10.3390/ijgi9040271.
  9. Fatemeh Imani and Marzieh Tabaeianb. Recreating mental image with the aid of cognitive maps and its role in environmental perception. Procedia - Social and Behavioral Sciences, 32:53-62, 2012. URL: https://doi.org/10.1016/j.sbspro.2012.01.010.
  10. Toru Ishikawa and Daniel R. Montello. Spatial knowledge acquisition from direct experience in the environment: Individual differences in the development of metric knowledge and the integration of separately learned places. Cognitive Psychology, 52(2):93-129, 2006. URL: https://doi.org/10.1016/j.cogpsych.2005.08.003.
  11. Markus Kattenbeck. Empirically Measuring Salience of Objects for Use in Pedestrian Navigation. PhD thesis, University of Regensburg, 2016. URL: https://doi.org/10.1007/s13218-016-0482-4.
  12. Markus Kattenbeck. How subdimensions of salience influence each other. comparing models based on empirical data. In Eliseo Clementini, Maureen Donnelly, Yuan May, Christian Kray, Paolo Fogliaroni, and Andrea Ballatore, editors, Proceedings of the 13th International Conference on Spatial Information Theory (COSIT 2017), pages 1-10, 2017. URL: https://doi.org/10.4230/LIPIcs.COSIT.2017.10.
  13. Markus Kattenbeck, Eva Nuhn, and Sabine Timpf. Is salience robust? A heterogeneity analysis of survey ratings. In Stephan Winter, Amy L. Griffin, and Monika Sester, editors, Proceedings of the 10th International Conference on Geographic Information Science (GIScience 2018), volume 114, pages 7:1-7:16. Dagstuhl Publishing, Germany, 2018. URL: https://doi.org/10.4230/LIPIcs.GIScience.2018.7.
  14. Shelley R. Kelsey. Impact of landmarks on wayfinding and spatial knowledge. PhD thesis, Carleton University, 2009. URL: https://arxiv.org/abs/arXiv:1011.1669v3.
  15. Rober M. Kitchin. Methodological Convergence in Cognitive Mapping Research: Investigating Configurational Knowledge. Journal of Environmental Psychology, 16(3):163-185, 1996. URL: https://doi.org/10.1006/jevp.1996.0015.
  16. Jakub Krukar, Antonia van Eek, and Angela Schwering. Task-dependent sketch maps. Spatial Cognition and Computation, 23(4):263-292, 2023. URL: https://doi.org/10.1080/13875868.2023.2170802.
  17. Vladimir I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8):707-710, 1966. Google Scholar
  18. Hengshan Li, Tyler Thrash, Christoph Hölscher, and Victor R. Schinazi. The effect of crowdedness on human wayfinding and locomotion in a multi-level virtual shopping mall. Journal of Environmental Psychology, 65:101320:1-101320:9, 2019. URL: https://doi.org/10.1016/j.jenvp.2019.101320.
  19. Xiao Li, X.-Q. Wu, Z.-H. Yin, and Jie Shen. The Influence of Spatial Familiarity on the Landmark Salience Sensibility in Pedestrian Navigation Environment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7(2W7):83-89, September 2017. URL: https://doi.org/10.5194/isprs-archives-XLII-2-W7-83-2017.
  20. Kevin Lynch. The Image of the City. The M.I.T. Press, Cambridge, MA and London, England, 1960. Google Scholar
  21. Erminielda Mainardi Peron, Maria Rosa Baroni, Remo Job, and Paola Salmaso. Effects of familiarity in recalling interiors and external places. Journal of Environmental Psychology, 10(3):255-271, 1990. URL: https://doi.org/10.1016/S0272-4944(05)80098-2.
  22. Dominique Makowski, Mattan S. Ben-Shachar, Indrajeet Patil, and Daniel Lüdecke. Methods and algorithms for correlation analysis in R. Journal of Open Source Software, 5(51):2306, 2020. URL: https://doi.org/10.21105/joss.02306.
  23. Dominique Makowski, Brenton M. Wiernik, Indrajeet Patil, Daniel Lüdecke, and Mattan S. Ben-Shachar. correlation: Methods for correlation analysis, October 2022. Version 0.8.3. URL: https://CRAN.R-project.org/package=correlation.
  24. Charu Manivannan, Jakub Krukar, and Angela Schwering. Spatial generalization in sketch maps: A systematic classification. Journal of Environmental Psychology, 83, 2022. URL: https://doi.org/10.1016/j.jenvp.2022.101851.
  25. Niamh A. Merriman, Jan Ondřej, Eugenie Roudaia, Carol O'Sullivan, and Fiona N. Newell. Familiar environments enhance object and spatial memory in both younger and older adults. Experimental Brain Research, 234(6):1555-1574, 2016. URL: https://doi.org/10.1007/s00221-016-4557-0.
  26. Matthew Molmer. Spatial orientation and familiarity in a small-scale real environment using PC-based virtual environment technology. Technical report, Naval Postgraduate School Monterey CA, 2005. URL: https://core.ac.uk/download/pdf/36695787.pdf.
  27. Veronica Muffato and Chiara Meneghetti. Knowledge of familiar environments: Assessing modalities and individual visuo-spatial factors. Journal of Environmental Psychology, 67:101387:1-101387:9, 2020. URL: https://doi.org/10.1016/j.jenvp.2020.101387.
  28. Raffaella Nori, Laura Piccardi, Andrea Maialetti, Mirco Goro, Andrea Rossetti, Ornella Argento, and Cecilia Guariglia. No gender differences in egocentric and allocentric environmental transformation after compensating for male advantage by manipulating familiarity. Frontiers in Neuroscience, 12:1-9, 2018. URL: https://doi.org/10.3389/fnins.2018.00204.
  29. Clemens Nothegger, Stephan Winter, and Martin Raubal. Selection of salient features for route directions. Spatial Cognition and Computation, 4(2):113-136, 2004. Google Scholar
  30. The pandas development team. pandas-dev/pandas: Pandas, June 2020. URL: https://doi.org/10.5281/zenodo.3509134.
  31. Edzer Pebesma. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, 10(1):439-446, 2018. URL: https://doi.org/10.32614/RJ-2018-009.
  32. Edzer Pebesma and Roger Bivand. Spatial Data Science: With applications in R. Chapman and Hall/CRC, 2023. URL: https://doi.org/10.1201/9780429459016.
  33. Edzer J. Pebesma and Roger S. Bivand. Classes and methods for spatial data in R. R News, 5(2):9-13, November 2005. URL: https://CRAN.R-project.org/doc/Rnews/.
  34. QGIS Development Team. QGIS Geographic Information System. QGIS Association, 2024. URL: https://www.qgis.org.
  35. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2022. URL: https://www.R-project.org/.
  36. Martin Raubal and Stephan Winter. Enriching Wayfinding Instructions with Local Landmarks. In Max Egenhofer and David Mark, editors, Proceedings of the Second International Conference on Geographic Information Science (GIScience 2002), pages 243-259. Springer, 2002. Google Scholar
  37. 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, pages 83-97. Springer, Heidelberg et al., 2013. Google Scholar
  38. Angela Schwering, Jakub Krukar, Charu Manivannan, Malumbo Chipofya, and Sahib Jan. Generalized, Inaccurate, Incomplete: How to Comprehensively Analyze Sketch Maps Beyond Their Metric Correctness. In Toru Ishikawa, Sara Fabrikant, and Stephan Winter, editors, Proceedings of the 15th International Conference on Spatial Information Theory (COSIT 2022), pages 8:1-8:15. Dagstuhl Publishing, Germany, 2022. URL: https://doi.org/10.4230/LIPIcs.COSIT.2022.8.
  39. Angela Schwering, Jia Wang, Malumbo Chipofya, Sahib Jan, Rui Li, and Klaus Broelemann. SketchMapia: Qualitative Representations for the Alignment of Sketch and Metric Maps. Spatial Cognition and Computation, 14(3):220-254, 2014. URL: https://doi.org/10.1080/13875868.2014.917378.
  40. Nicola Sloan, Bruce Doran, Francis Markham, and Kristen Pammer. Does base map size and imagery matter in sketch mapping? Applied Geography, 71:24-31, 2016. URL: https://doi.org/10.1016/j.apgeog.2016.04.001.
  41. Holm Sture. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2):65-70, 1979. Google Scholar
  42. David H. Uttal, Alinda Friedman, Linda Liu Hand, and Christopher Warren. Learning fine-grained and category information in navigable real-world space. Memory and Cognition, 38(8):1026-1040, 2010. URL: https://doi.org/10.3758/MC.38.8.1026.
  43. Guido Van Rossum and Fred L. Drake. Python 3 Reference Manual. CreateSpace, Scotts Valley, CA, 2009. Google Scholar
  44. Rul von Stülpnagel and Melanie C. Steffens. Can active navigation be as good as driving? A comparison of spatial memory in drivers and backseat drivers. Journal of Experimental Psychology: Applied, 18(2):162-177, 2012. URL: https://doi.org/10.1037/a0027133.
  45. Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt and Jarrod Millman, editors, Proceedings of the 9th Python in Science Conference, pages 56-61, 2010. URL: https://doi.org/10.25080/Majora-92bf1922-00a.
  46. Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. URL: https://ggplot2.tidyverse.org.
  47. Hadley Wickham, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D'Agostino McGowan, Romain François, Garrett Grolemund, Alex Hayes, Lionel Henry, Jim Hester, Max Kuhn, Thomas Lin Pedersen, Evan Miller, Stephan Milton Bache, Kirill Müller, Jeroen Ooms, David Robinson, Dana Paige Seidel, Vitalie Spinu, Kohske Takahashi, Davis Vaughan, Claus Wilke, Kara Woo, and Hiroaki Yutani. Welcome to the tidyverse. Journal of Open Source Software, 4(43):1686, 2019. URL: https://doi.org/10.21105/joss.01686.
  48. Yuji Yoshimura, Shan He, Gary Hack, Takehiko Nagakura, and Carlo Ratti. Quantifying Memories: Mapping Urban Perception. Mobile Networks and Applications, 25(4):1275-1286, August 2020. URL: https://doi.org/10.1007/s11036-020-01536-0.
  49. Ying Zhang. Analysis Of Space, Cognition And Pedestrian Movement. PhD thesis, University of Oklahoma, Graduate College, 2019. Google Scholar
  50. Zhiyong Zhou, Robert Weibel, Cheng Fu, Stephan Winter, and Haosheng Huang. Indoor landmark selection for route communication: the influence of route-givers' social roles and receivers' familiarity with the environment. Spatial Cognition & Computation, 21(4):257-289, 2021. URL: https://doi.org/10.1080/13875868.2021.1959595.
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