Understanding human mobility has many applications in diverse areas, including spread of diseases,[2][3] mobile viruses,[4] city planning,[5][6][7] traffic engineering,[8][9] financial market forecasting,[10] and nowcasting of economic well-being.
These datasets are anonymized by the phone companies so as to hide and protect the identity of actual users.
As an example of liabilities that might happen, New York City released 173 million individual taxi trips.
[18] This made it possible for hackers to completely de-anonymize the dataset, and even some were able to extract detailed information about specific passengers and celebrities, including their origin and destination and how much they tipped.
[18][19] At the large scale, when the behaviour is modelled over a period of relatively long duration (e.g. more than one day), human mobility can be described by three major components: Brockmann,[20] by analysing banknotes, found that the probability of travel distance follows a scale-free random walk known as Lévy flight of form
In brownian motion, the distribution of trip distances are govern by a bell-shaped curve, which means that the next trip is of a roughly predictable size, the average, where in Lévy flight it might be an order of magnitude larger than the average.
The third component models the fact that humans tend to visit some locations more often than what would have happened under a random scenario.
For example, home or workplace or favorite restaurants are visited much more than many other places in a user's radius of gyration.
This means that although there is a great variance in type of users and the distances that each of them travel, the overall characteristic of them is highly predictable.
These long-distance travels are made using air transportation systems and it has been shown that "network topology, traffic structure, and individual mobility patterns are all essential for accurate predictions of disease spreading".
[21] On a smaller spatial scale the regularity of human movement patterns and its temporal structure should be taken into account in models of infectious disease spread.
[25] Cellphone viruses that are transmitted via bluetooth are greatly dependent on the human interaction and movements.
[22] In Transportation Planning, leveraging the characteristics of human movement, such as tendency to travel short distances with few but regular bursts of long-distance trips, novel improvements have been made to Trip distribution models, specifically to Gravity model of migration[26]