Among the data mining tasks, the extraction of patterns that show relevant spatial and temporal dependencies among data is one of the most useful in order to deal with a wide range of fields of application. In this thesis, we leveraged on an existing data mining algorithm specifically designed to handle spatio-temporal events to extract association rules. Such rules are applied in a predictive context to a real-case scenario, a station-based bike sharing system. More specifically, we take into account the analysis of years of historical data about a bike-sharing service based in San Francisco, from which we want to extract patterns useful to deploy an associative classifier.