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.

The spatio-temporal characteristics of eye movements vary according to the activity the user of a cartographic map is performing. In this paper, we use these eye movement characteristics to automatically detect the map user's activity, an approach with great potential in gaze-assistive map interfaces. A dataset of 587 eye movement recordings from 17 participants was used to train and cross-validate a support vector machine (SVM) classifier over 229 features. The classifier can distinguish 6 common map activities with an accuracy of approx. 78%.

Pedestrian navigation systems help us make a series of decisions that lead us to a destination. Most current pedestrian navigation systems communicate using map-based turn-by-turn instructions. This interaction mode suffers from ambiguity, its user's ability to match the instruction with the environment, and it requires a redirection of visual attention from the environment to the screen. In this paper we present GazeNav, a novel gaze-based approach for pedestrian navigation. GazeNav communicates the route to take based on the user's gaze at a decision point. We evaluate GazeNav against the map-based turn-by-turn instructions. Based on an experiment conducted in a virtual environment with 32 participants we found a significantly improved user experience of GazeNav, compared to map-based instructions, and showed the effectiveness of GazeNav as well as evidence for better local spatial learning. We provide a complete comparison of navigation efficiency and effectiveness between the two approaches.