An algorithm for the selection of route dependent orientation information

Landmarks are important features of spatial cognition and are naturally included in human route descriptions. In the past algorithms were developed to select the most salient landmarks at decision points and automatically incorporate them in route instructions. Moreover, it was shown that human route descriptions contain a significant amount of orientation information, which support the users to orient themselves regarding known environmental information, and it was shown that orientation information support the acquisition of survey knowledge. Thus, there is a need to extend the landmarks selection to automatically select orientation information. In this work, we present an algorithm for the computational selection of route dependent orientation information, which extends previous algorithms and includes a salience calculation of orientation information for any location along the route. We implemented the algorithm and demonstrate the functionality based on OpenStreetMap data

Analysis of the types and relations of features that people include in route sketch maps

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesGeographic information makes our lives easier through devices such as GPS or drones. Many technical improvements are coming up every day, but the interaction human-computer is sometimes limited. In particular, in the process of moving from the digital world to reality, how people understand this information. In this paper, I investigated what are the different features and relations of features that people include in sketch maps when they are asked to give route directions and what are the reasons for these differences. The research is based on people´s spatial knowledge using data collected among users sketch maps and spatial strategies tests. Our results show that there are differences in the features drawn and that they are related mostly due to environmental reasons. Not relevant differences are found in the spatial relation of features. From the results, it is extracted valuable information that can benefit future researchers and the creation of new navigation devices