Contextual data characterize distinct regions of the city allowing to differentiate them according to security, entertainment, services, among others. Using contextual data to suggest routes helps to understand new aspects of a city that can change users perceptions of different routes. The impact of each type of contextual data may vary according to the user's profile, which is not taken into account in most of the systems proposed by the literature. In addition, it is necessary to consider the behavior of contextual data which changes according to the type of data. To mitigate the problems mentioned above, a route suggestion system with space-time risk is proposed called GIN. The system consists of three modules, namely: identification of contextual windows, context mapping, and route personalization. In addition, a strategy to decrease the number of route requests is proposed to improve the system scalability. The evaluation results show that the system adapts to sensitive changes in the user's profile. In addition, positive results were obtained by using the behavior of contextual data to avoid unnecessary requests. This allowed for a reduction of up to 50\% of requests made to the system.