Human mobility: data analysis, theory and models

Like Columbus mistook America for India, we stepped into the era of misinformation mistaking it for the era of big data. Since the digital revolution in the early ’90s we started producing such a huge amount of data that we do not even know how to find our compass anymore. However, not all this data is available and accessible. In this thesis, by navigating through the seas of available, open and even purchased data on board of our knowledge of physics and complex systems, we try to draw some new routes and shortcuts to study human mobility in different con- texts, scales and applications.

We first introduce a simple method to treat Twitter data on the Venezuelan exodus to show how this data can consistently reproduce and uncover many different aspects of migration until now neglected. This method designs a safe route to solve many more open questions not yet explored due to the limitations of classic and other sources of migration data in many parts of the world.

The same type of data provides us reasonable footprints of human mobility, which lead us to an innovative shortcut in the way in which a specific type of urban mobility is treated so far. By hoisting the sails of theoretical physics, we can add a field theoretic description of commuting in worldwide cities, which simplifies the complexity of the description of urban mobility. By means of this new framework it is possible to tackle the well known aspect of policentricity of cities, drawing urban basins of attraction and reproducing them through a field theoretic ver- sion of the gravity model.

Following the same shortcut we get to discover something that has been only theorized so far in pedestrian dynamics: the navigation potential of evacuation. This potential has been used by many social interactions models, which have been used to study new and better policies to avoid cloggings and stampedes during evacuation drills, hence creating safer protocols for our buildings and public spaces.

In the middle of our navigation, we suddenly bump into a new epidemic and we perform a route change. By purchasing mobile and smartphones location datasets to find our compass and cope with noisy epidemic records, we are able to uncover the so called multi-seeding effect, which has been studied mostly theoretically. This allows us to backtrace and remap the epidemic spreading in Western Europe to specific epidemic hubs. By means of metapopulation models, we confirm our hypotheses on multiseeding using different contact network topologies. Our results allow to designate efficient policies like selective lockdowns and to better prepare healthcare systems of areas which are more exposed to mobility from epidemic and mobility hubs.

While the epidemic spreads in Europe, we spot the first cases on the American coasts. The same phenomenon we already saw can be observed at smaller scales in the United States, this time within cities at neighborhoods level. Here we need a high resolution Google dataset in order to see that cities mobility hierarchy leads the disease to spread faster than in sprawled urban areas. However, hierarchy also helps containment policies to bet- ter suppress the disease, whereas the same restrictions are less effective in non-hierarchical metropolitan areas. Some cities are more sensitive to disease spreading and they must be accurately monitored in order to avoid the rest of cities and country to get involved.

Finally, in order to suppress the disease it is very important to avoid the virus to board on long-range trips and infect new places. By means of smartphones location records, we mimic the spreading of viruses at even finer scales inside the busiest airport of Europe: Heathrow, London. By modeling the implementation of a spatial immunization system we are able to strongly reduce the outbreaks within the airport and the number of exported infections abroad. The same technique can be applied even in ordinary public buildings to create safer spaces for the everyday life in the post-Covid era.