The contribution presents a computational modeling approach to geographic knowledge processing in mind. Geographic knowledge is assumed to be stored in a piecemeal manner. Spatial knowledge fragments form a hierarchical structure of lean knowledge. An actual mental image representation is constructed when needed to perform a specific task. In this construction process missing information is complemented to create a determinate mental image. -- First, the artificial intelligence perspective taken is elaborated. After a short review of conceptions on mental processing of spatial knowledge from psychology and artificial intelligence we outline the model MIRAGE. The internal structure and the operating of the model is elaborated using an exemplary scenario. Problems in constructing mental images from given pieces of knowledge are demonstrated and discussed. The paper concludes with a discussion of the approach with respect to its modeling objective. We point to further research questions and to potential applications.

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.

Schematic maps are effective tools for representing information about the physical environment; they depict specific information in an abstract way. This study concentrates on spatial aspects of the physical environment such as branching points and connecting roads, which play a paramount role in the schematization of wayfinding maps. Representative classes of branchingpoints are identified and organized in a taxonomy. The use of prototypical branching points and connecting road types is empirically evaluated in the schematization of maps. The role played by the different functions according to which the map is classified is assessed, and main strategies applied during the schematization process are identified. Implications for navigational tasks are presented.

In the past, many methods of spatial analysis have been developed for a better understanding and modelling of real-world phenomena. However there is still a need for exploration of new analytical techniques for modelling urban spaces. Space syntax models the spatial configurations of urban spaces by using a connectivity graph representation. Such a configuration of space identifies patterns that can be used to study urban structures and human behaviours. This paper proposes methodological and practical evaluations of the potential of the space syntax approach within GIS. We present the main principles that are the basis of space syntax, in addition to methodological perspectives for a closer integration with GIS, which should be of use for many GIS applications, such as urban planning and design.

Navigation is a fundamental human activity and an integral part of everyday life. Humans use their knowledge about previous experiences with geographic space to find their way. Considerable research in the areas of cognitive science, psychology, and artificial intelligence has been carried out to examine the means by which humans navigate. Humans need to abstract from reality and form concepts of space in order to reason about space or carry out tasks e.g., navigating. Concepts are used to organize space and to structure the perception of reality. They depend strongly on the knowledge of the human and on the task at hand. Humans may use multiple levels of abstraction, representing different parts of geographic space, to carry out a single navigation task. Although descriptions of spatial concepts already exist, they are defined in natural language and, therefore, lack the formality to be implemented. Conceptual modeling is a design technique that models the concepts of an application (e.g., highway navigation) on a highly abstracted level. Conceptual models provide the possibility to communicate spatial concepts in a formal and unambiguous way. For large and complex applications in Geographic Information Systems, conceptual modeling is a promising

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