A mobile trusted path system based on social network data

Social networks provide rich data sources for analyzing people journeys in urban environments. This paper introduces a trusted path system that helps users to find their routes based in two criteria: low crime rate and no theft report. These data are obtained from two complementary sources: geo-tagged tweets from the social network Twitter, and an official database given by the Police of Mexico City. Recommended paths are computed automatically from these data sources by a complementary application of social mining techniques, Bayes algorithm and an adaptation of the Dijkstra algorithm. This system can be also used to identify the probability that an event occurs in specific locations and times. A proof of concept of the system is illustrated through two example scenarios.