Data fusion for city life event detection

The automatic detection of events happening in urban areas from mobile phones' and social networks' datasets is an important problem that would enable novel services ranging from city management and emergency response, to social and entertainment applications. In this work we present a simple yet effective method for discovering events from spatio-temporal datasets, based on statistical anomaly detection. Our approach can combine multiple sources of information to improve results. We also present a method to automatically generate a keyword-based description of the events being detected. We run experiments in two cities with data coming from a mobile phone operator (call detail records-CDRs) and from Twitter. We show that this method gives interesting results in terms of precision and recall. We analyze the parameters of our approach and discuss its strengths and weaknesses.