In our world, there is an abundance of complex systems whose elements are highly interrelated. Among the most notable examples of these systems, we can find social networks, flocks of birds, and urban traffic. The phenomena arising from these systems require new interdisciplinary methods to unveil their fundamental nature, borrowing tools from Statistics, Information Theory, and Machine Learning.
My MSc thesis aims to apply these methods to Mobility and Traffic environments, focusing on the study of GeoSpatial data and the building of a mathematical model that efficiently describes most of its nature.
Citing Sir Francis Bacon in Novum Organum (1620):
‘Those who have handled sciences have been either men of experiment or men of dogmas. The men of experiment are like the ant, they collect and use; the reasoners resemble spiders, who make cobwebs out of their own substance. But the bee takes a middle course: it gathers its material from the flowers of the garden and of the field, but transforms and digests it by a power of its own.’