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This pictorial summarizes a qualitative study of children's perceptions of mobility through their own cognitive map drawings of the journey from home to school. We engaged 27 sixth-grade students (11 to 12 year-old) from Lisbon, Portugal, and analyze their drawings extracting 12 different design recommendations. We use these recommendations to provide design suggestions in terms of i) existing functionalities of mapping applications (that can be re-used); ii) improvements for existing map and wayfinding systems; iii) children's original design suggestions. The qualitative study reported here provides contributions that could help promote sustainable mobility for children in particular, with regards to innovation but also autonomy and supervision by caregivers.

This paper presents Beanstalk, an interactive platform to assist communities in easily running systematic analysis of mobility patterns of tourists at their destinations, contributing in new ways in visualizing spatio-temporal mobility data for forecasting, tracking trends, detecting patterns and noticing anomalies. The approach takes advantage of a combination of passive Wi-Fi tracking and ground truth data provided by tourism authorities. By analyzing a large dataset for a medium sized European island, we provide evidence of the accuracy and effectiveness of this low-cost method in inferring topological characteristics of tourist behavior and relevant typologies of trip itineraries. This helps decision makers in the touristic sector to plan and manage actions geared towards improving the sustainability and competitiveness of their touristic regions. In particular, we argue that in a world where sensing data is becoming inexpensive, there is an opportunity to use this approach to deliver data back to local communities which are empowered to act and leverage this information.