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  • 1.
    Brügger, Annina
    et al.
    Department of Geography, University of Zurich, Switzerland.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Fabrikant, Sara Irina
    Department of Geography, University of Zurich, Switzerland.
    Distributing Attention Between Environment and Navigation System to Increase Spatial Knowledge Acquisition During Assisted Wayfinding2018In: Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017) / [ed] Fogliaroni P., Ballatore A., Clementini E., Springer, 2018, p. 19-22Conference paper (Refereed)
    Abstract [en]

    Travelers happily follow the route instructions of their devices when navigating in an unknown environment. Navigation systems focus on route instructions to allow the user to efficiently reach a destination, but their increased use also has negative consequences. We argue that the limitation for spatial knowledge acquisition is grounded in the system’s design, primarily aimed at increasing navigation efficiency. Therefore, we empirically investigate how navigation systems could guide users’ attention to support spatial knowledge acquisition during efficient route following tasks.

  • 2.
    Brügger, Annina
    et al.
    University of Zurich, Switzerland.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Fabrikant, Sara Irina
    University of Zurich, Switzerland.
    How does navigation system behavior influence human behavior?2019In: Cognitive Research: Principles and Implications, E-ISSN 2365-7464, Vol. 4, no 5Article in journal (Refereed)
    Abstract [en]

    Navigation systems are ubiquitous tools to assist wayfinders of the mobile information society with various navigational tasks. Whenever such systems assist with self-localization and path planning, they reduce human effort for navigating. Automated navigation assistance benefits navigation performance, but research seems to show that it negatively affects attention to environment properties, spatial knowledge acquisition, and retention of spatial information. Very little is known about how to design navigation systems for pedestrian navigation that increase both navigation performance and spatial knowledge acquisition. To this end, we empirically tested participants (N = 64) using four different navigation system behaviors (between-subject design). Two cognitive processes with varying levels of automation, self-localization and allocation of attention, define navigation system behaviors: either the system automatically executes one of the processes (high level of automation), or the system leaves the decision of when and where to execute the process to the navigator (low level of automation). In two experimental phases, we applied a novel empirical framework for evaluating spatial knowledge acquisition in a real-world outdoor urban environment. First, participants followed a route assisted by a navigation system and, simultaneously, incidentally acquired spatial knowledge. Second, participants reversed the route using the spatial knowledge acquired during the assisted phase, this time without the aid of the navigation system. Results of the route-following phase did not reveal differences in navigation performance across groups using different navigation system behaviors. However, participants using systems with higher levels of automation seemed not to acquire enough spatial knowledge to reverse the route without navigation errors. Furthermore, employing novel methods to analyze mobile eye tracking data revealed distinct patterns of human gaze behavior over time and space. We thus can demonstrate how to increase spatial knowledge acquisition without harming navigation performance when using navigation systems, and how to influence human navigation behavior with varying navigation system behavior. Thus, we provide key findings for the design of intelligent automated navigation systems in real-world scenarios.

  • 3.
    Kübler, Isabella
    et al.
    Department of Geography, University of Zürich.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Fabrikant, Sara Irina
    Department of Geography, University of Zürich.
    How does the visualization of uncertainty influence decision making with hazard prediction maps?2017Other (Other academic)
    Abstract [en]

    A wealth of design strategies has been proposed by an interdisciplinary scientific community to visually communicate data uncertainty in maps, with the aim to support spatio-temporal decision-making under uncertainty (MacEachren et al., 2012). However, very few researchers have looked at whether and how uncertainty depictions might influence people’s reasoning processes and decision making outcomes, especially in problem contexts for which uncertainty truly matters, i.e., in life-threatening situations, or for dilemmatic decisions. We report on a map-based multi-criteria decision making study where participants (N=35) were asked to imagine purchasing a house shown on map stimuli inspired by Swiss National hazard prediction maps (SFOEN, 2016). These area-classed maps show the probability and intensity of natural disasters occurring in areas with varying danger levels in a pre-defined color scheme (i.e., red=high, blue=moderate, and yellow=low danger). Current hazard prediction maps do not depict prediction uncertainties, even though suggestions have been proposed in the cartographic literature (Kunz and Hurni, 2011). However, because there are uncertainties associated with the areal extent of the classed danger zones, we modified the zonal boundaries to show this locational uncertainty using the visual variables color value, focus, and texture, as suggested by prior empirical research (MacEachren, 2012). In a within-subject design, participants were repeatedly asked to decide which house they wished to buy, given varying house location characteristics, and respective purchase price information. The houses were depicted on a series of hazard prediction maps showing an area unknown to participants, with/without data uncertainty depicted. The maps showing uncertainty varied in the visual variables (i.e., color value|focus|texture) used to convey the locational uncertainty of the zonal boundaries. We recorded participants’ house selections, response times, and eye movements during the experiment. The task asked for participants’ preferences; there were no right or wrong answers. As hypothesized, our results show that participants’ decision making outcomes were indeed influenced by the depicted uncertainty information. Participants decided to buy different houses, as they weighted selection criteria differently, depending on whether uncertainty was shown on the map or not. We thus provide rare evidence on how uncertainty and the type of uncertainty visualization (i.e., varying color value, focus, or texture) can influence people’s reasoning to arrive at a complex, multi-criteria-based decision. We also find that participants’ individual differences with respect to their risk taking behavior tested with a standardised questionnaire influences their decision making. Risk takers underestimate the dangers of natural hazards when prediction uncertainties are depicted. With this unique study we are able to shed additional light on how people use visualized uncertainty information to make complex map-based decisions. Echoing Hegarty et al.'s (2016) findings, we again demonstrate that not only display design characteristics are relevant for map-based reasoning and decision making outcomes, but also the decision makers’ individual background, and the map-based decision-making task and context. References: Hegarty, M., Friedman, A., Boone, A.P., Barrett, T.J. (2016). Where Are You? The Effect of Uncertainty and Its Visual Representation on Location Judgments in GPS-Like Displays. Journal of Experimental Psychology, Applied, DOI: 10.1037/xap0000103. Kunz, M. and Hurni, L. (2011). How to Enhance Cartographic Visualisations of Natural Hazards Assessment Results. The Cartographic Journal, 48(1): 60-71. MacEachren, A. M., Roth, R. E., O'Brien, J., Li, B., Swingley, D., Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12): 2496-2505. Swiss Federal Office for the Environment (SFOEN). Gefahrenkarten, Intensitätskarten und Gefahrenhinweiskarten. (Natural Hazard Maps), http://www.bafu.admin.ch/naturgefahren/14186/14801/15746/ (not available in English, accessed Oct. 2016).

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