Spatial information acquisition happens in large part through the visual sense. Studying visual attention and its connection to cognitive processes has been the interest of many research efforts in spatial cognition over the years. Recent technological developments have led to an increasing popularity of eye-tracking methodology for investigating research questions related to spatial cognition, geographic information science (GIScience) and cartography. At the same time, eye trackers can nowadays be used as an input device for (cognitively engineered) user interfaces to geographic information. We provide an overview of the most recent literature advancing and utilizing eye-tracking methodology in these fields, introduce the research articles in this Special Issue, and discuss challenges and opportunities for future research.

Ongoing urbanisation processes invoke immense construction activities, for which citizens often participate in planning. Yet, imagining planned buildings based on visual representations is a highly demanding task. While traditional methods, such as construction spans, 2D, or 3D visualisation often fail to offer a complete picture, we propose Augmented Reality (AR) as a more adequate tool. We first present an evaluation of the suitability of AR compared to construction spans for a future building and assess which degree of abstraction of AR is most effective, as well as difficulty of interpreting them correctly. In a between-subjects field study we compare construction spans and a prototype AR application including three levels of detail (LOD) of the same building project. Participants solve two estimation tasks using the construction spans and six estimation tasks using the AR application, before answering a questionnaire on the different visualisation methods. We find participants are confident about the potential of AR, but no significant differences between the different LOD groups in subjective assessment. Results suggest that previous knowledge (e.g. in GIS) may have a positive impact on dimension estimation performance. Also, details, such as façade elements or windows, could facilitate estimation tasks because they allow inferences about a building’s size.

This work investigates the potential of providing commercial aviation flight instructors with an eye tracking enhanced observation system to support the training process. During training, instructors must deal with many parallel tasks, such as operating the flight simulator, acting as air traffic controllers, observing the pilots and taking notes. This can cause instructors to miss relevant information that is crucial for debriefing the pilots. To support instructors, the instructor ASsistant SYSTem (iASSYST) was developed. It includes video, audio, simulator and eye tracking recordings. iASSYST was evaluated in a study involving 7 instructors. The results show that with iASSYST, instructors were able to support their observations of errors, find new errors, determine that some previously identified errors were not errors, and to reclassify the types of errors that they had originally identified. Instructors agreed that eye tracking can help identifying causes of pilot error. Practitioner Summary: This paper introduces an instructor assistant system, which is evaluated in a user study involving 7 airline flight instructors. The system can be used by airline flight instructors to complement their observations, as a basis for discussions with pilots during debriefing, and by airline pilots to improve their flight performance.

Location-based services (LBS) provide more useful, intelligent assistance to users by adapting to their geographic context. For some services that context goes beyond a location and includes further spatial parameters, such as the user’s orientation or field of view. Here, we introduce Gaze-Informed LBS (GAIN-LBS), a novel type of LBS that takes into account the user’s viewing direction. Such a system could, for instance, provide audio information about the specific building a tourist is looking at from a vantage point. To determine the viewing direction relative to the environment, we record the gaze direction relative to the user’s head with a mobile eye tracker. Image data from the tracker’s forward-looking camera serve as input to determine the orientation of the head w.r.t. the surrounding scene, using computer vision methods that allow one to estimate the relative transformation between the camera and a known view of the scene in real-time and without the need for artificial markers or additional sensors. We focus on how to map the point of regard of a user to a reference system, for which the objects of interest are known in advance. In an experimental validation on three real city panoramas, we confirm that the approach can cope with head movements of varying speed, including fast rotations up to to 63 degrees per second. We further demonstrate the feasibility of GAIN-LBS for tourist assistance with a proof-of-concept experiment in which a tourist explores a city panorama, where the approach achieved a recall that reaches over 99%. Finally, a GAIN-LBS can provide objective and qualitative ways of examining the gaze of a user based on what the user is currently looking at.