Paper by Caio Libânio Melo Jerônimo, Claudio E. C. Campelo, Cláudio de Souza Baptista: “The need to use online technologies that favor the understanding of city dynamics has grown, mainly due to the ease in obtaining the necessary data, which, in most cases, are gathered with no cost from social networks services. With such facility, the acquisition of georeferenced data has become easier, favoring the interest and feasibility in studying human mobility patterns, bringing new challenges for knowledge discovery in GIScience. This favorable scenario also encourages governments to make their data available for public access, increasing the possibilities for data scientist to analyze such data. This article presents an approach to extracting mobility metrics from Twitter messages and to analyzing their correlation with social, economic and demographic open data. The proposed model was evaluated using a dataset of georeferenced Twitter messages and a set of social indicators, both related to Greater London. The results revealed that social indicators related to employment conditions present higher correlation with the mobility metrics than any other social indicators investigated, suggesting that these social variables may be more relevant for studying mobility behaviors….(More)”.