The conditions and context in which pedestrians are exposed to the risk of road accidents, air pollution or epidemy are important for improving safety. Spatial information on pedestrian mobility was difficult and expensive to collect so far. This is changing with the popularization of several apps either for guidance or self-monitoring. Produced Space-Time Trajectories (STT) perfectly match the concept of temporal geography, as to assess pedestrian exposure to the above risks. In this article, we propose a modeling approach of pedestrian STT. The abstract model provides a high semantic expressiveness through spatiotemporal queries and a high-level graph representation of the data. We depict a whole framework for collecting and analyzing GPS logs generated by pedestrians' devices, and discuss the system components. Among the targeted applications, a new generation of mobile applications can be developed that allow pedestrians to organize their daily activities and mobility, and minimize their exposure risks.