Humans navigating in unfamiliar environments rely on wayfinding directions, either given by people familiar with the area, or communicated through maps or wayfinding services. The essential role of landmarks in human route communication is well-known. However, mapping the human ability to select landmarks ad hoc for route directions to a computational model was never tried before. Wayfinding services manage the problem by using pre-defined points of interest. These points are not automatically identified and they are not related to a specific route. In contrast, here a computational model is presented that selects salient features along a route where needed, e.g., at decision points. We propose measures to formally specify the salience of a feature. The observed values of these measures are subject to stochastical tests in order to identify the most salient features from datasets. The proposed model is implemented and checked for computability with a use case from the city of Vienna. It is also cross-checked with a human subject survey for landmarks along a given route. The survey provides evidence that the proposed model selects features that are strongly correlated to human concepts of landmarks. Hence, integrating the selected salient features in wayfinding directions will produce directions with lower cognitive workload and higher success rates as compared to directions based only on geometry or on geometry and static points of interest.

Abstract. Mobile maps show the current position of a map reader in their center. We will question this convention, which is in our opinion a relict from paper maps. We observe that, in contrast to paper maps, mobile maps have a narrower purpose and mobile devices have a different affordance and handling. This observation encourages thinking of a map design that allows an embodied experience of map reading. The latter is realized here by a shift of the current position of the map reader to the bottom of a mobile map. We expect that a map design closer to the bodily experience of a map reader reduces the cognitive workload of reading. In this paper we investigate the new design paradigm and its consequences. Furthermore, we present experiments of reaction times of map readers, to find evidence for the validity of our hypothesis.

Abstract. This chapter reviews a model of measuring the salience of a specific class of spatial features—façades of buildings—for adaptation to abilities and preferences of user groups of wayfinding services. The model was intentionally designed to be open for such adaptations, and we will report on ways, experiences, and limitations of doing so. We will prove the hypothesis that focalization, i.e., adaptation to different decision situations, can be sufficiently modelled by weights of predetermined salience measures to increase wayfinding success. The long term goal is to identify sets of weights for typical foci of user groups. 1