A data-driven world requires a data-aware society. At DRIBIA, we want to contribute to this vision by sharing our expertise. That is why we organize courses, seminars, and educative experiences for professionals, citizens and students alike. We want people to understand the hidden power, but also the limitations and dangers, that lie behind apparently abstract numbers. We want to transform users of technology into actors empowered to code, analyze and take decisions backed on quantitative evidence.
We are experts in delivering citizen science experiences to the society through the concept of «pop-up» experiments. We organize participatory science mobility experiments through mobile apps to raise awareness on the importance of data sharing open practices for research.X
Using our experience on presenting research results in a dynamic at international conferences, as well as our university teaching expertise, we provide on-demand, specialized courses of data analysis and data modelling for companies willing to introduce their professionals (regardless of the level) to quantitative related concepts in a simple yet consistent way.
We love doing research, and sharing our insights with the society. We work hard to transfer technology and discoveries from academia to the business sector. We offer our services to private and public research institutions alike and we love publishing the results of these collaborations to enrich knowledge in data analysis and visualization related areas.
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. We consider six real networks and find that many important local and global structural properties of these networks are closely reproduced by random graphs with fixed properties. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate random graphs.X
Understanding human mobility is of vital importance for fields that draw policies from the activities of humans in space. Typically only a subsample of the population of interest is available in the data, giving a possibly incomplete picture of the entire system under study. To solve this, we have developed a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, based on an analysis of a large dataset of Taxi displacements in NY city.X
Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared. Using data on millions of taxi trips in several cities, we find a scaling law that can be theoretically derived with a simple model that predicts the potential for ride sharing in any city.X
We describe an example of a scalable experiment on human mobility at a public, outdoor fair in Barcelona (Spain) with crowsourced data. Participants were tracked while wandering through activity stands attracting their attention. We develop a general modelling framework which allows us to test the influence of two distinct types of ingredients on their mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.X