How does the design of landmarks on a mobile map influence wayfinding experts’ spatial learning during a real-world navigation task?
Open accessAbstract
Humans increasingly rely on GPS-enabled mobile maps to navigate novel environments. However, this reliance can negatively affect spatial learning, which can be detrimental even for expert navigators such as search and rescue personnel. Landmark visualization has been shown to improve spatial learning in general populations by facilitating object identification between the map and the environment. How landmark visualization supports expert users’ spatial learning during map-assisted navigation is still an open research question. We thus conducted a real-world study with wayfinding experts in an unknown residential neighborhood. We aimed to assess how two different landmark visualization styles (abstract 2D vs. realistic 3D buildings) would affect experts’ spatial learning in a map-assisted navigation task during an emergency scenario. Using a between-subjects design, we asked Swiss military personnel to follow a given route using a mobile map, and to identify five task-relevant landmarks along the route. We recorded experts’ gaze behavior while navigating and examined their spatial learning after the navigation task. We found that experts’ spatial learning improved when they focused their visual attention on the environment, but the direction of attention between the map and the environment was not affected by the landmark visualization style. Further, there was no difference in spatial learning between the 2D and 3D groups. Contrary to previous research with general populations, this study suggests that the landmark visualization style does not enhance expert navigators’ navigation or spatial learning abilities, thus highlighting the need for population-specific mobile map design solutions.
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A-priori power analysis for a single-predictor multilevel linear regression model using parameters from a previous study indicated that a minimum of 40 participants, each completing 10 pointing task trials, would achieve 83% power.
“Using parameters estimated from own previous study (i.e., fixed effects beta coefficients and random effects’ variance; Kapaj et al., 2023), we found that a minimum of 40 participants, each completing 10 pointing task trials would achieve an 83% power (see data availability statement for study preregistration).”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Kapaj et al., 2023
Our results are in line with previous studies that the visualization style does not hinder wayfinders with high-spatial abilities.
“On the other hand, our results are in line with the findings of previous studies that the visualization style does not hinder wayfinders with high-spatial abilities (Hegarty et al., 2009; Kapaj et al., 2021, 2023; Lanini-Maggi et al., 2021; Thrash et al., 2019; Wilkening & Fabrikant, 2011).”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Kapaj et al., 2023
The lack of direct influence of landmark visualization styles on spatial learning measures can be attributed to depicting landmarks as 3D building models in both conditions on the mobile map providing the necessary visual information to facilitate basic spatial learning required to solve the navigation task.
“The lack of direct influence of landmark visualization styles on spatial learning measures can be attributed to the fact that depicting landmarks as 3D building models in both conditions on the mobile map could have provided the necessary visual information to facilitate the basic spatial learning required to solve the navigation task (Chrastil & Warren, 2012; Elias & Paelke, 2008; Kapaj et al., 2”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Kapaj et al., 2023
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- Authors
- Kapaj, A., Lanini-Maggi, S., Hilton, C., Cheng, B., & Fabrikant, S. I.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Kapaj, Armand
- Venue
- Cartography and Geographic Information Science
- Publisher
- Informa UK Limited
- Published
- 2023-03-04
- Volume
- 50
- Issue
- 2
- Pages
- 197-213
- ISSN
- 1523-0406 (ISSN-L)
- 1545-0465
- OpenAlex
- W4323360045
- Cited by
- 24
- References
- 76
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 9 · 2023: 3
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cartography and Geographic Information Science (publishedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- H2020 - EXCELLENT SCIENCE - European Research Council (Advanced Grant GeoViSense (No. 740426))
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Social Sciences › Geography, Planning and Development › Geography Education and Pedagogy
- Keywords
- mobile maps, spatial learning, wayfinding, gaze behavior, visual attention, emergency navigation, map design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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