Nowadays, there are a mature set of tools and techniques for data analytic, which help Data Scientist to extract knowledge from raw heterogeneous data. Nonetheless, there is still a lack spatio-temporal historical dataset allowing to study everyday life phenomena, such as vehicular congestion, press influence, the effect of politicians comments on stock exchange markets, the relation between food prices evolution and temperatures or rainfall, social structure resilience against extreme climate events, among others. Unfortunately, there are few datasets combining different sources of urban data in order to carry out studies of phenomena occurring in cities (i.e., Urban Analytics). To solve this problem, we have implemented a Web crawler platform for gathering a different kind of available public datasets.