A global empirical study on how street networks facilitate driving longer distances.

We simulated over 200 cities worldwide to investigate how the street network affects vehicle routes. We demonstrate that there is a ubiquitous super-linear relationship between time and distance when optimal route are chosen. More precisely, the average speed will be higher for longer trips when compared to shorter trips, showing that the street network makes driving further faster.

Tracing contacts to evaluate the transmission of COVID-19 from highly exposed individuals in public transportation.

We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city.

Human mobility in large cities as a proxy for crime.

We investigate at the subscale of the neighborhoods of a highly populated city the incidence of property crimes in terms of both the resident and the floating population. Our results show that a relevant allometric relation could only be observed between property crimes and floating population. More precisely, the evidence of a superlinear behavior indicates that a disproportional number of property crimes occurs in regions where an increased flow of people takes place in the city.