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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.

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  • 3. Harrie, Lars
    et al.
    Oucheikh, Rachid
    Nilsson, Åsa
    Oxenstierna, Andreas
    Cederholm, Pontus
    Wei, Lai
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Olsson, Perola
    Label Placement Challenges in City Wayfinding Map Production - Identification and Possible Solutions2022In: Journal of Geovisualization and Spatial Analysis, ISSN 2509-8810, E-ISSN 2509-8829, Vol. 6, no 1, article id 16Article in journal (Refereed)
    Abstract [en]

    Map label placement is an important task in map production, which needs to be automated since it is tedious and requires a significant amount of manual work. In this paper, we identify five cartographic labeling situations that present challenges by causing intensive manual work in map production of city wayfinding maps, e.g., label placement in high density areas, utilizing true label geometries in automated methods, and creating a good relationship between text labels and icons. We evaluate these challenges in an open source map labeling tool (QGIS), provide results from a preliminary study, and discuss if there are other techniques that could be applicable to solving these challenges. These techniques are based on quantified cartographic rules or on machine learning. We focus on deep learning for which we provide several examples of techniques from other application domains that might have a potential in map label placement. The aim of the paper is to explore those techniques and to recommend future practical studies for each of the identified five challenges in map production. We believe that targeting the revealed challenges using the proposed solutions will significantly raise the automation level for producing city wayfinding maps, thus, having a real, measurable impact on production time and costs.

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  • 4.
    Harrie, Lars
    et al.
    Physical Geography and Ecosystem Science, Lund University, Sweden.
    Touya, Guillaume
    LASTIG, Univ Gustave Eiffel, IGN-ENSG, France.
    Oucheikh, Rachid
    Physical Geography and Ecosystem Science, Lund University, Sweden.
    Ai, Tinghua
    School of Resource and Environmental Sciences, Wuhan University, China.
    Courtial, Azelle
    LASTIG, Univ Gustave Eiffel, IGN-ENSG, France.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Machine learning in cartography2024In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 51, no 1, p. 1-19Article in journal (Other academic)
    Abstract [en]

    Machine learning is increasingly used as a computing paradigm in cartographic research. In this extended editorial, we provide some background of the papers in the CaGIS special issue Machine Learning in Cartography with a special focus on pattern recognition in maps, cartographic generalization, style transfer, and map labeling. In addition, the paper includes a discussion about map encodings for machine learning applications and the possible need for explicit cartographic knowledge and procedural modeling in cartographic machine learning models.

  • 5.
    Kashian, Alireza
    et al.
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Rajabifard, Abbas
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Chen, Yiqun
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    OSM POI Analyzer: A Platform for Assessing Position of POIs in OpenStreetMap2017In: ISPRS Geospatial Week 2017 / [ed] D. Li, J. Gong, B. Yang, H. Wu, L. Wu, Z. Gui, X. Cheng, H. Wu, S. Li, R. Lindenbergh, J. Boehm, M. Rutzinger, W. Yao, M. Weinmann, Z. Kang, K. Khoshelham, M. Peter, L. Díaz-Vilariño, W. Shi, B. Lu, H. Abdulmuttalib, M. R. Delavar, T. Balz, B. Osmanoglu, F. Rocca, U. Sörgel, J. Zhang, P. Li, S. Du, L. Zhao, X. Lin, K. Qin, C. Kang, X. Li, C. Chen, R. Li, G. Qiao, H. Wu, and C. Heipke, 2017, Vol. XLII-2/W7, p. 497-504Conference paper (Refereed)
    Abstract [en]

     In recent years, more and increased participation in Volunteered Geographical Information (VGI) projects provides enough data coverage for most places around the world for ordinary mapping and navigation purposes, however, the positional credibility of contributed data becomes more and more important to bring a long-term trust in VGI data. Today, it is hard to draw a definite traditional boundary between the authoritative map producers and the public map consumers and we observe that more and more volunteers are joining crowdsourcing activities for collecting geodata, which might result in higher rates of man-made mistakes in open map projects such as OpenStreetMap. While there are some methods for monitoring the accuracy and consistency of the created data, there is still a lack of advanced systems to automatically discover misplaced objects on the map. One feature type which is contributed daily to OSM is Point of Interest (POI). In order to understand how likely it is that a newly added POI represents a genuine real-world feature scientific means to calculate a probability of such a POI existing at that specific position is needed. This paper reports on a new analytic tool which dives into OSM data and finds co-existence patterns between one specific POI and its surrounding objects such as roads, parks and buildings. The platform uses a distance-based classification technique to find relationships among objects and tries to identify the high-frequency association patterns among each category of objects. Using such method, for each newly added POI, a probabilistic score would be generated, and the low scored POIs can be highlighted for editors for a manual check. The same scoring method can be used for existing registered POIs to check if they are located correctly. For a sample study, this paper reports on the evaluation of 800 pre-registered ATMs in Paris with associated scores to understand how outliers and fake entries could be detected automatically.

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  • 6.
    Kashian, Alireza
    et al.
    University of Melbourne, Australia.
    Rajabifard, Abbas
    University of Melbourne, Australia.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Yiqun
    University of Melbourne, Australia.
    Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns2019In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, no 7, p. 1420-1443Article in journal (Refereed)
    Abstract [en]

    In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.

  • 7.
    Kübler, Isabella
    et al.
    Independent Scholar.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Fabrikant, Sara Irina
    Department of Geography and Digital Society Initiative, University of Z€urich.
    Against All Odds: Multicriteria Decision Making with Hazard Prediction Maps Depicting Uncertainty2020In: Annals of the Association of American Geographers, ISSN 0004-5608, E-ISSN 1467-8306, Vol. 110, no 3, p. 661-683Article in journal (Refereed)
    Abstract [en]

    We report on a multicriteria decision-making study where participants were asked to purchase a house shown on maps that include hazard prediction information. We find that participants decided to buy different houses, depending on whether uncertainty is shown on the map display and on the type of uncertainty visualization (i.e., varying color value, focus, or texture). We also find that participants’ individual differences with respect to their assessed risk-taking behavior influences their spatial decision making with maps. Risk-takers seem to underestimate the dangers of natural hazards when prediction uncertainties are depicted. We are thus able to shed additional light on how people use visualized uncertainty information to make complex map-based decisions. We can demonstrate that not only are design characteristics relevant for map-based reasoning and decision-making outcomes but so are the decision makers’ individual background and the map-based decision-making context.

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  • 8.
    Lidberg, William
    et al.
    Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden.
    Paul, Siddhartho Shekhar
    Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden.
    Westphal, Florian
    Dept. of Computing, School of Engineering, Jönköping Univ., Jönköping, Sweden.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lavesson, Niklas
    Dept. of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden.
    Melniks, Raitis
    Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ Salaspils, Latvia.
    Ivanovs, Janis
    Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ Salaspils, Latvia.
    Ciesielski, Mariusz
    Dept. of Geomatics, Forest Research Institute, Sękocin Stary, Raszyn, Poland.
    Leinonen, Antti
    Finnish Forest Centre, Kajaani, Finland.
    Ågren, Anneli M.
    Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden.
    Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning2023In: Journal of irrigation and drainage engineering, ISSN 0733-9437, E-ISSN 1943-4774, Vol. 149, no 3, article id 04022051Article in journal (Refereed)
    Abstract [en]

    Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

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  • 9.
    Rabe, Sven-Erik
    et al.
    Institute for Spatial and Landscape Planning, ETH Zurich, Switzerland.
    Gantenbein, Remo
    Department of Geography, University of Zurich, Switzerland.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Grêt-Regamey, Adrienne
    Institute for Spatial and Landscape Planning, ETH Zurich, Switzerland.
    Increasing the credibility of expert-based models with preference surveys: mapping recreation in the riverine zone2018In: Ecosystem Services, ISSN 2212-0416, E-ISSN 2212-0416, Vol. 31, p. 308-317Article in journal (Refereed)
    Abstract [en]

    Recreation is a basic human need and therefore must be considered in spatial planning, which requires spatially explicit mapping of the recreation suitability of a landscape. The current methods for this type of mapping have limitations: On one hand, widely used expert-based models for large scale suitability assessments often suffer from discrepancies between the mapped values from expert assessment and actual user preferences. On the other hand, elicitation of personal preferences of potential users is complex and time-consuming, and their applicability to larger scales is limited.

    In this paper, we demonstrate the development of a spatially explicit model for the recreation suitability of the riverine zone that integrates the preferences of the users with an expert-based modeling process. First, we conducted an analytic hierarchy process (AHP) with experts to generate four different model variants based on physical variables. These model variants differ in terms of the strength of the influence of the variables on the recreation suitability. Second, an online survey was used to gather data on user preferences for various river sections with regard to recreation. A comparison of the expert model results with the preferences of the potential users shows a clear correlation between one model variant and the users’ preferences. This result suggests that it is possible to elaborate an expert model which corresponds to the preferences of users.

    We made the model results available for the planning and development of the riverine zone in the canton of Zurich. To this end, they were integrated in a decision support platform together with other planning-relevant information.

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  • 10.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Identifying Landmark Candidates Beyond Toy Examples: A Critical Discussion and Some Way Forward2017In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 31, no 2, p. 135-139Article in journal (Refereed)
    Abstract [en]

    Incorporating references to landmarks in navigation systems requires having data on potential landmarks in the first place. While there have been many approaches in the scientific literature for identifying landmark candidates, these have hardly been picked up in actual, running systems. One major obstacle for this to happen may be that most—if not all—approaches presented so far are not scalable due to their underlying data requirements. In this paper, I will critically discuss existing approaches in light of their scalability. I will then suggest a way forward to more scalable solutions by combining in a smart way aspects of different approaches.

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  • 11.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    The description of places in Biondo’s Italia Illustrata: outlining a quantitative analysis of their granularity and spatial relationships2022In: Walking through history: an interdisciplinary approach to Flavio Biondo’s spaces in the "Italia Illustrata" / [ed] Tanja Michalsky; Martin Thiering, Bibliotheca Hertziana – Max Planck Institute for Art History , 2022, Vol. 1Chapter in book (Other academic)
    Abstract [en]

    This chapter outlines a quantitative method for analyzing location descriptions. Generally, such a quantitative analysis allows for identifying predominant features of these descriptions and, given a sufficiently large corpus, may allow for statistical inferences. Here, an English translation of Biondo’s chapter on the region Latium is used as a small case study illustrating the method. The analyzed features include the frequency (distribution) of the level of granularity of those entities referred to in the descriptions, which spatial hierarchical structures emerge from referring to different entities on potentially different levels of granularity and the frequency of these structures, and which spatial relationships between entities dominate in the descriptions. The chapter presents results of this analysis and discusses them in detail, also pointing out limitations of the method. However, mainly the chapter is to be read as an introduction to this particular analysis approach, which may be used to complement other analyses and may offer insights not otherwise gained.

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  • 12.
    Richter, Kai-Florian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Devlin, Roisin
    Umeå University; Queen’s University Belfast, Belfast, Northern Ireland.
    La Grecca, Filippo
    Umeå University; Università degli Studi di Milano, Milano, Italy.
    Investigating Wayfinding under Inconsistent Information2020In: Spatial Cognition XII: Proceedings / [ed] Jurǵis Šķilters, Nora S. Newcombe, David Uttal, Switzerland: Springer Nature, 2020, p. 191-195Conference paper (Refereed)
    Abstract [en]

    Route instructions used in wayfinding studies are usually taken to be perfect, but in real life we often receive erroneous or am- biguous instructions. The present study investigates wayfinding behavior under such inconsistent instructions in a virtual reality setting. We find that women are more accurate than men, and that wayfinders seem to be more affected by incorrect landmark information than incorrect turn information.

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  • 13.
    Richter, Kai-Florian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Horned, Arvid
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Inside an autonomous car: some open issues and social implications2023In: CarSA Workshop: Publications / [ed] Franziska Babel; Philipp Hock; Sam Thellman; Tom Ziemke, Association for Computing Machinery (ACM), 2023Conference paper (Refereed)
    Abstract [en]

    Driving is a highly social activity with various interactions between the different actors involved in traffic. Autonomous vehicles will pose several challenges to the social fabric of traffic and, as most new technology, will lead to changes in human behavior. This will surely hold for interactions between autonomous vehicles and actors \emph{outside} the vehicle. However and importantly, autonomous vehicles will also alter existing and introduce new social situations and interactions for those \emph{inside} the vehicle, which appears to be an under-researched topic. This paper will focus on the passengers of autonomous vehicles. We will discuss some of the implications and expected changes in the relationship between a car and those inside it, and highlight some of the open issues of being enclosed and driven by a highly complex, largely black-box AI system on wheels.

  • 14.
    Richter, Kai-Florian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Horned, Arvid
    Umeå University.
    Karlsson, Kristoffer
    Umeå University.
    Icon-based Navigation2018In: Geospatial Technologies for All: short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science / [ed] Mansourian, A., Pilesjö, P., Harrie, L., von Lammeren, R, Lund: Lund University , 2018, article id 86Conference paper (Refereed)
    Abstract [en]

    Icon-based navigation uses a minimalist approach to mobile navigation assistance by offering navigators only icon displays representing landmark objects at waypoints along a route in an indoor environment. In this paper, we motivate this new concept and its usefulness, present a first prototype implementation exploring the concept, and results of an initial empirical evaluation. While results are not fully conclusive, they point to the potential of this kind of navigation assistance.

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  • 15.
    Richter, Kai-Florian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Scheider, Simon
    Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands.
    Current topics and challenges in geoAI2023In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 37, p. 11-16Article in journal (Refereed)
    Abstract [en]

    Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) data sources and advanced machine learning techniques, but also a shift in the kind of problems under investigation. In this article, we highlight some of these changes and identify current topics and challenges in geoAI.

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  • 16.
    Richter, Kai-Florian
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Scheider, Simon
    Department of Human Geography and Spatial Planning, University Utrecht, Princetonlaan 8a, Utrecht, Netherlands.
    Tuia, Devis
    Environmental Computational Science and Earth Observation laboratory, Ecole Polytechnique Fédérale de Lausanne, EPFL Valais Wallis, Rue de l’Industrie 17, Case postale 440, Sion, Switzerland.
    GeoAI as collaborative effort: interview with Devis Tuia2023In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 37, p. 99-105Article in journal (Refereed)
  • 17.
    Scheider, Simon
    et al.
    Department of Human Geography and Spatial Planning, Utrecht University, Princetonlaan 8a, Utrecht, Netherlands.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    GeoAI2023In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 37, p. 5-9Article in journal (Other academic)
  • 18.
    Scheider, Simon
    et al.
    Department of Human Geography and Spatial Planning, Utrecht University, Princetonlaan 8a, Utrecht, Netherlands.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Pragmatic geoAI: geographic information as externalized practice2023In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 37, p. 17-31Article in journal (Refereed)
    Abstract [en]

    Current artificial intelligence (AI) approaches to handle geographic information (GI) reveal a fatal blindness for the information practices of exactly those sciences whose methodological agendas are taken over with earth-shattering speed. At the same time, there is an apparent inability to remove the human from the loop, despite repeated efforts. Even though there is no question that deep learning has a large potential, for example, for automating classification methods in remote sensing or geocoding of text, current approaches to GeoAI frequently fail to deal with the pragmatic basis of spatial information, including the various practices of data generation, conceptualization and use according to some purpose. We argue that this failure is a direct consequence of a predominance of structuralist ideas about information. Structuralism is inherently blind for purposes of any spatial representation, and therefore fails to account for the intelligence required to deal with geographic information. A pragmatic turn in GeoAI is required to overcome this problem.

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  • 19.
    Scheider, Simon
    et al.
    Department of Human Geography and Spatial Planning, University Utrecht, Princetonlaan 8a, CB, Utrecht, Netherlands.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Janowicz, Krzysztof
    Department of Geography and Regional Research, Vienna University, Universitätsstraße 7, Vienna, Austria; Center for Spatial Studies, University of California, Santa Barbara, Ellison Hall, Santa Barbara, United States.
    GeoAI and beyond: interview with Krzysztof Janowicz2023In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 37, p. 91-97Article in journal (Refereed)
  • 20.
    Singh, Avinash
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Baranwal, Neha
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A Fuzzy Inference System for a Visually Grounded Robot State of Mind2020In: ECAI 2020 / [ed] De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A., Lang, J., IOS Press, 2020, p. 2402-2409Conference paper (Refereed)
    Abstract [en]

    In order for robots to interact with humans on real-world scenarios or objects, these robots need to construct a representa- tion (‘state of mind’) of these scenarios that a) are grounded in the robots’ perception and b) ideally should match human understand- ing and concepts. Using table-top settings as scenario, we propose a framework that generates a robot’s ’‘state of mind’ by extracting the objects on the table along with their properties (color, shape and texture) and spatial relations to each other. The scene as perceived by the robot is represented in a dynamic graph in which object at- tributes are encoded as fuzzy linguistic variables that match human spatial concepts. In particular, this paper details the construction of such graph representations by combining low-level neural network- based feature recognition and a high-level fuzzy inference system. Using fuzzy representations allows for easily adapting the robot’s original scene representation to deviations in properties or relations that emerge in language descriptions given by humans viewing the same scene. The framework is implemented on a Pepper humanoid robot and has been evaluated using a data set collected in-house.

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  • 21.
    Singh, Avinash
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Baranwal, Neha
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bensch, Suna
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Towards Verbal Explanations by Collaborating Robot Teams2019In: International Conference on Social Robotics (ICSR’19), Workshop Quality of Interaction in Socially Assistive Robots, Madrid, Spain, November 26-29, 2019, 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe ongoing work on understandable teams of robots collaborating to solve a common task while communicating their current, suggested or planned actions in natural language to human bystanders. We propose an algorithm for generating optimal sequences of partitions to be used by the robots to generate natural language utterances. These optimal sequences of partitions maintain the conversational principle of informativeness proposed by Grice.We further propose a method to incorporate policies, such as a uniform distribution of workload.

  • 22.
    Singh, Avinash
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Baranwal, Neha
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bensch, Suna
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Understandable teams of Pepper robots2020In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness: The PAAMS Collection / [ed] Yves Demazeau, Tom Holvoet, Juan M. Corchado, Stefania Costantini, Springer Nature, 2020, p. 439-442Conference paper (Refereed)
    Abstract [en]

    The term understandable robots refers to robots making their actions and intentions understandable (or explainable) to humans. To support understandability of a team of collaborating robots we use nat- ural language to let the robots verbalize what they do and plan to do. Our solution is based on Cooperating Distributed Grammar Systems for plan derivation and a Multi-agent algorithm for coordination of robot actions. We implemented and evaluated our solution on a team of three Pepper robots that work collaboratively to move an object on a table, thereby coordinating their capabilities and actions and verbalizing their actions and intentions. In a series of experiments, our solution not only success- ful demonstrated collaboration and task fulfilment, but also consider- able variation, both regarding actions and generated natural language utterances.

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  • 23.
    Singh, Avinash
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Baranwal, Neha
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bensch, Suna
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Verbal explanations by collaborating robot teams2021In: Paladyn - Journal of Behavioral Robotics, ISSN 2080-9778, E-ISSN 2081-4836, Vol. 12, no 1, p. 47-57Article in journal (Refereed)
    Abstract [en]

    In this article, we present work on collaborating robot teams that use verbal explanations of their actions and intentions in order to be more understandable to the human. For this, we introduce a mechanism that determines what information the robots should verbalize in accordance with Grice’s maxim of quantity, i.e., convey as much information as is required and no more or less. Our setup is a robot team collaborating to achieve a common goal while explaining in natural language what they are currently doing and what they intend to do. The proposed approach is implemented on three Pepper robots moving objects on a table. It is evaluated by human subjects answering a range of questions about the robots’ explanations, which are generated using either our proposed approach or two further approaches implemented for evaluation purposes. Overall, we find that our proposed approach leads to the most understanding of what the robots are doing. In addition, we further propose a method for incorporating policies driving the distribution of tasks among the robots, which may further support understandability.

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  • 24.
    Singh, Avinash Kumar
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Baranwal, Neha
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    An Empirical Review of Calibration Techniques for the Pepper Humanoid Robot's RGB and Depth Camera2020In: Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 2, Springer, 2020, Vol. 1038, p. 1026-1038Conference paper (Refereed)
    Abstract [en]

    This paper presents a comparative study of different calibration techniques to align the Pepper Humanoid Robot’s depth camera with respect to its RGB camera. Both cameras are placed at different locations and have different viewpoints and view angles. We kept the image resolution same for both cameras to avoid scaling issues and tried to retrieve the translation and rotation coefficients. We used an in-house dataset for conducting experiments. The dataset consists of both RGB and Depth images of single and multiple objects placed on the table. We used homography, fundamental matrix, and proposed translation estimation technique to fix the alignment issues. Root mean square error and error variance are used as a measurement to evaluate the efficacy of the system.

  • 25.
    Teimouri, Fateme
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Abstracting routes to their route-defining locations2022In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 91, article id 101732Article in journal (Refereed)
    Abstract [en]

    Today's navigation assistance systems provide turn-by-turn instructions, which only focus on the next decision to take without offering any larger context. Information presentation is uniform and disconnected, i.e., any local instruction is equally important and not linked to any other decisions or the overall route context. This differs from how people usually give instructions and hinders spatial learning. In order to (re-)establish this larger context, we present an approach to identifying those locations along a route that define its characteristics, termed route-defining locations. These are prominent, easily recognized locations, which help relating the route to an environment's overall structure and a navigator's existing knowledge about the environment. The approach allows for determining route-defining locations on different levels of detail. Thus, at the same time it offers a mechanism for simplifying (or abstracting) a route. In this paper, we particularly focus on the latter aspect, presenting in detail the approach for identifying route-defining locations. For a given route, we, first, simplify its shape to extract those turns along the route that characterize its overall shape. Then, we rank landmarks and streets along the route based on their prominence. Finally, we include in the simplified route those turns that correspond to the locations of the most prominent landmarks and streets. The result is a set of route-defining locations extracted from the route shape, and prominent landmarks and streets along the route. In an agent-based simulation, we then evaluate the approach's ability to abstract a route to its characteristics, i.e., the defining locations. Results show that, indeed, our approach is effective in that respect, but success depends on ‘matching’ abstraction to an agent's knowledge about the environment.

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  • 26.
    Teimouri, Fateme
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Enhancing wayfinding and route learning: a human-centered study of route-defining locations algorithm2024In: Behaviour & Information Technology, ISSN 0144-929XArticle in journal (Other academic)
    Abstract [en]

    Navigation services play a crucial role in everyday wayfinding. One of their key features is the turn-by-turn guidance, enabling wayfinders to navigate efficiently between locations. However, over-reliance on turn-by-turn guidance can hinder wayfinders' ability to perceive their surroundings and develop spatial awareness, negatively impacting user experience. Previous research has addressed these issues by modifying instructions, user interaction, or both. However, the applicability of these methods beyond specific studies remains unclear. This paper presents a human-centred study by evaluating an algorithm that automatically generates navigation instructions. The instructions are generated by adding route-defining locations to ‘standard’ turn-by-turn instructions to enhance wayfinder's spatial knowledge acquisition, and improve user experience. Route-defining locations, identified using a previously proposed algorithm, serve as anchors for the wayfinder to form a spatial understanding of the environment. The study involved 36 participants divided into two groups: one received ‘standard’ turn-by-turn (TBT) instructions, while the other received instructions that included route-defining locations (RDL). Participants navigated in an unfamiliar virtual environment, and their wayfinding and route learning were compared between the two groups. Results show that RDL instructions led to better wayfinding and route learning performance compared to TBT instructions, suggesting the potential to improve navigation systems and user experiences.

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  • 27.
    Teimouri, Fateme
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    'Straight? What straight?' Investigating navigation instructions’ applicability2023In: Journal of Location Based Services, ISSN 1748-9725, E-ISSN 1748-9733, Vol. 17, no 1, p. 1-25Article in journal (Refereed)
    Abstract [en]

    Anecdotal evidence shows that, sometimes, the instructions generated by a navigation service do not seem to match with how a wayfinder understands the given wayfinding situation. Such issues may make processing instructions harder for the wayfinder, and it may be a potential source of wayfinding errors and dangerous behaviour. These mismatches may be caused by several issues, ranging from errors in the base data to issues with the instruction generating system – the service’s inference system. In this work, we focus on the latter. We empirically investigate how much people agree with navigation instructions usefully describing a given wayfinding situation. To this end, we collected both quantitative (ratings) and qualitative data (comments and alternative instructions). Quantitative analysis supports the assumption that, sometimes, instructions just do not seem to fit. Qualitative analysis points to two main sources for the mismatches: 1) the language used in the instructions; 2) how the navigation service represents and reasons about a wayfinding situation.

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  • 28.
    Teimouri, Fateme
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    You Are Not Alone: Path Search Models, Traffic, and Social Costs2020In: 11th International Conference on Geographic Information Science  - Part I / [ed] Krzysztof Janowicz, Judith A. Verstegen, Schloss Dagstuhl–Leibniz-Zentrum für Informatik , 2020, Vol. 177, p. 14:1-14:16, article id 14Conference paper (Refereed)
    Abstract [en]

    Existing cognitively motivated path search models ignore that we are hardly ever alone when navigating through an environment. They neither account for traffic nor for the social costs that being routed through certain areas may incur. In this paper, we analyse the effects of “not being alone” on different path search models, in particular on fastest paths and least complex paths. We find a significant effect of aiming to avoid traffic on social costs, but interestingly only minor effects on path complexity when minimizing either traffic load or social costs. Further, we find that ignoring traffic in path search leads to significantly increased average traffic load for all tested models. We also present results of a combined model that accounts for complexity, traffic, and social costs at the same time. Overall, this research provides important insights into the behavior of path search models when optimizing for different aspects, and explores some ways of mitigating unwanted effects.

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  • 29.
    Teimouri, Fateme
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hochmair, Hartwig Henry
    Geomatics Sciences, University of Florida, Gainesville, FL, USA.
    Analysis of route choice based on path characteristics using Geolife GPS trajectories2023In: Journal of Location Based Services, ISSN 1748-9725, E-ISSN 1748-9733, Vol. 17, no 3, p. 271-297Article in journal (Refereed)
    Abstract [en]

    Navigation services are essential for daily navigation, providing turn-by-turn instructions to help wayfinders reach their destinations. These services often differ from the heuristics wayfinders use, resulting in a poor user experience. Researchers have attempted to address this issue by developing algorithms that find less complex routes, by integrating prominent locations along the route to make wayfinding easier and to improve a wayfinder’s knowledge about the environment. These approaches, however, have taken a bottom-up approach, involving a limited number of participants navigating in real or virtual environments which may limit generalisability of results. In this study, we took a top-down approach by analysing a large dataset of GPS-based trips in the real world. Using the Geolife dataset, we analysed individual heuristics for route selection in terms of complexity and prominent locations, and found that wayfinders prefer less complex routes, such as routes that require fewer turns or involve simpler intersections. Additionally, we found that wayfinders choose routes with fewer prominent locations, such as routes that bypass well-known landmarks or busy commercial areas. These findings suggest that simplicity and ease of use are prioritized when selecting a route, while overly complex routes or areas with many points of interest are avoided.

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  • 30.
    Tewari, Maitreyee
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bensch, Suna
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Modelling Grice's Maxim of Quantity as Informativeness for Short Text2020Conference paper (Other academic)
    Abstract [en]

    Grice's Cooperative Principle (CP) is one of the early theories about good communication. We propose a novel formalisation of one of the sub-components of CP, namely the maxim of quantity (MoQ). We interpret MoQ as informativeness and assume it has an intrinsic relationship to syntactic cohesion. Cohesion establishes a syntactic relationship between elements in a segment of text, and is modelled using syntactic dependency relations between words of the segment.A corpora of 1600 navigation instructions provide the setting for the two proposed metrics: syntactic cohesion for every single segment, and informativeness over all the segments of an instruction. Using a human-subject survey, the metric is evaluated qualitatively and quantitatively. Evaluation results are promising and show a close relationship between the proposed metric scores and the ratings of the participants. Overall, our work indicates that the informativeness of instructions can be captured using a simple syntactic measure.

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  • 31.
    Thrash, Tyler
    et al.
    University of Zürich, Switzerland.
    Lanini-Maggi, Sara
    University of Zürich, Switzerland.
    Fabrikant, Sara I.
    University of Zürich, Switzerland.
    Bertel, Sven
    Hochschule Flensburg, Germany.
    Brügger, Annina
    University of Zürich, Switzerland.
    Credé, Sascha
    University of Zürich, Switzerland.
    Do, Cao Tri
    University of Zürich / ETH Zürich, Switzerland.
    Gartner, Georg
    TU Wien, Austria.
    Huang, Haosheng
    University of Zürich, Switzerland.
    Münzer, Stefan
    Universität Mannheim, Germany.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    The Future of Geographic Information Displays from GIScience, Cartographic, and Cognitive Science Perspectives2019In: 14th International Conference on Spatial Information Theory / [ed] Sabine Timpf, Christoph Schlieder, Markus Kattenbeck, Bernd Ludwig and Kathleen Stewart, Dagstuhl, Germany, 2019, Vol. 142, p. 19:1-19:11Conference paper (Refereed)
    Abstract [en]

    With the development of modern geovisual analytics tools, several researchers have emphasized the importance of understanding users' cognitive, perceptual, and affective tendencies for supporting spatial decisions with geographic information displays (GIDs). However, most recent technological developments have focused on support for navigation in terms of efficiency and effectiveness while neglecting the importance of spatial learning. In the present paper, we will envision the future of GIDs that also support spatial learning in the context of large-scale navigation. Specifically, we will illustrate the manner in which GIDs have been (in the past) and might be (in the future) designed to be context-responsive, personalized, and supportive for active spatial learning from three different perspectives (i.e., GIScience, cartography, and cognitive science). We will also explain why this approach is essential for preventing the technological infantilizing of society (i.e., the reduction of our capacity to make decisions without technological assistance). Although these issues are common to nearly all emerging digital technologies, we argue that these issues become especially relevant in consideration of a person's current and future locations.

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  • 32.
    Winter, Stephan
    et al.
    The University of Melbourne.
    Tomko, Martin
    The University of Melbourne.
    Vasardani, Maria
    RMIT.
    Richter, Kai-Florian
    Umeå University.
    Khoshelham, Kouroush
    The University of Melbourne.
    Kalantari, Mohsen
    The University of Melbourne.
    Infrastructure-Independent Indoor Localization and Navigation2019In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 52, no 3, article id 61Article in journal (Refereed)
    Abstract [en]

    In the absence of any global positioning infrastructure for indoor environments, research on supporting human indoor localization and navigation trails decades behind research on outdoor localization and navigation. The major barrier to broader progress has been the dependency of indoor positioning on environment-specific infrastructure and resulting tailored technical solutions. Combined with the fragmentation and compartmentalization of indoor environments, this poses significant challenges to widespread adoption of indoor location-based services. This article puts aside all approaches of infrastructure-based support for human indoor localization and navigation and instead reviews technical concepts that are independent of sensors embedded in the environment. The reviewed concepts rely on a mobile computing platform with sensing capability and a human interaction interface (“smartphone”). This platform may or may not carry a stored map of the environment, but does not require in situ internet access. In this regard, the presented approaches are more challenging than any localization and navigation solutions specific to a particular, infrastructure-equipped indoor space, since they are not adapted to local context, and they may lack some of the accuracy achievable with those tailored solutions. However, only these approaches have the potential to be universally applicable.

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  • 33.
    Zhou, Zhiyong
    et al.
    Department of Geography, University of Zurich, Zurich, Switzerland.
    Weibel, Robert
    Department of Geography, University of Zurich, Zurich, Switzerland; University Research, Priority Program (URPP) Language and Space, University of Zurich, Zurich, Switzerland .
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Huang, Haosheng
    Department of Geography, Ghent University, Ghent, Belgium.
    HiVG: A hierarchical indoor visibility-based graph for navigation guidance in multi-storey buildings2022In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 93, article id 101751Article in journal (Refereed)
    Abstract [en]

    A hierarchical data model is needed in mobile navigation systems to generate route instructions on multiple levels of detail (LODs), thereby adapting to users’ various information needs during navigation. In complex multi-storey indoor environments, existing hierarchical data models mainly rely on logical graphs that represent indoor cellular spaces as nodes and adjacency as edges. Due to the lack of precise geometry, however, they have limited capability to support the accurate computation of walking distance and directions, which are essential in route instructions. This article proposes a hierarchical indoor visibility-based graph (HiVG) for navigation guidance in multi-storey buildings and presents a HiVG generation algorithm. The algorithm’s input is an indoor visibility graph (iVG) in which the orientations of nodes to corridor areas are represented. In the algorithm, first the functions of edges in indoor route instructions are identified, after which an edge function-based graph partitioning iteration is performed to generate each level of the HiVG. Experiments with three buildings of different geometric configurations demonstrate the potential of our HiVG generation algorithm. Furthermore, the conducted case studies show that the proposed HiVG is appropriate for generating indoor route instructions on multiple LODs.

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  • 34.
    Zhou, Zhiyong
    et al.
    Department of Geography, University of Zurich, Zurich, Switzerland.
    Weibel, Robert
    Department of Geography, University of Zurich, Zurich, Switzerland.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Huang, Haosheng
    Department of Geography, Ghent University, Ghent, Belgium.
    Towards a hierarchical indoor data model from a route perspective2021In: Proceedings of the 16th International Conference on Location Based Services / [ed] Anahid Basiri, Georg Gartner, Haosheng Huang, ICA Commission on Location Based Services; University of Glasgow , 2021, p. 1-4Conference paper (Refereed)
    Abstract [en]

    In mobile navigation systems, an appropriate level of detail of the route instructions provided is important for navigation users to understand, memorise, and follow routes. However, few existing indoor navigation systems are capable of providing route instructions with multiple levels of detail. To close this gap, it is critical to model indoor environments with multiple granularities for route instructions to be generated on varying levels of detail. We propose a hierarchical model for route instructions in multi-storey buildings by allowing for representing actions (i.e., turning left or right, and going straight) in conceptualising route instructions. As a proof of concept, a case study is being conducted to present the feasibility of the proposed hierarchical model.

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  • 35.
    Çöltekin, Arzu
    et al.
    Department of Geography, University of Zurich.
    Francelet, Rebecca
    Department of Geography, University of Zurich.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Thoresen, John
    Laboratory of Behavioural Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL).
    Fabrikant, Sara Irina
    Department of Geography, University of Zurich.
    The effects of visual realism, spatial abilities, and competition on performance in map-based route learning in men2018In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 45, no 4, p. 339-353Article in journal (Refereed)
    Abstract [en]

    We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.

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