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Assessing Perceived Route Difficulty in Environments with Different Complexity (Short Paper)

Authors Arvid Horned , Zoe Falomir , Kai-Florian Richter

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ACM Subject Classification
  • Human-centered computing → Empirical studies in visualization
  • Human-centered computing → Human computer interaction (HCI)
  • Applied computing → Psychology
  • Applied computing → Cartography
Keywords
  • navigation complexity
  • perceived difficulty
  • route display
  • spatial cognition

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Abstract

Today, anyone feeling lost in a city or unsure about how to navigate can use navigation services to look up routes to where they want to go. Current research investigating these services has primarily focused on how to find an appropriate route and how to best support navigation along it, and not how routes and the maps they are presented on are perceived. What makes one route look more difficult to navigate than another? And how does experience with using navigation services and maps in daily life influence how difficult a route is perceived to be? We explored these questions in a survey study where participants rated the perceived difficulty of pedestrian routes in ten different cities. The results show that routes in more complex urban environments were perceived as more complex than routes in easier environments. At least partly, perceived difficulty seems to follow earlier conceptualizations of route complexity, but open questions remain regarding the interplay of environmental structure, route properties, and the map representation.

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Arvid Horned, Zoe Falomir, and Kai-Florian Richter. Assessing Perceived Route Difficulty in Environments with Different Complexity (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 29:1-29:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.COSIT.2024.29

Author Details

Arvid Horned
  • Department of Computing Science, Umeå University, Sweden
Zoe Falomir
  • Department of Computing Science, Umeå University, Sweden
Kai-Florian Richter
  • Department of Computing Science, Umeå University, Sweden

Funding

  • Falomir, Zoe: acknowledges the funding by the Wallenberg AI, Autonomous Systems and Software Program (WASP) awarded by the Knut and Alice Wallenberg Foundation, Sweden.

Acknowledgements

To the participants in this study and the reviewers, for their useful feedback.

Supplementary Materials

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