Collective human mobility experiments for urban spaces: data and models using complex systems science.

Enric Sanmarti

The study of human mobility in the context of complex systems science has gained popularity over the last years. Recently, there has been a surge in the number of data sets related to human mobility thanks to the availability of massive digital traces of human whereabouts and the development of mobile GPS technology. Our group has developed several methodologies that contribute to this field in a novel manner through participatory and collective experiments à la carte within the so-called citizen science practices. Citizen science is a new movement where amateur or nonprofessional scientists or any citizen take part in research and with which we are able to better respond to specific research questions raised by citizenry. Large volunteer networks allow scientists to accomplish tasks that would be too expensive or time consuming to accomplish through other means. Bee-Path, a mobile application co-developed by our group, is able to track the user by means of mobile GPS technology, is a successful example of the application of such approaches. It has been implemented repeatedly in open-space fairs to obtain data about the movement of the visitors of the event. More recently, we introduced Bee-Path in the barcelonian neighbourhood of les Corts, to learn about the mechanisms that rule the movement of its neighbours, the most frequented locations and the utilization of streets, among others. Aside from the valuable scientific results obtained with this methodology, the citizens that have participated in the experiments will also obtain a reward related to their neighbourhood. Additionally, we developed a public experiment of human mobility with the collaboration of a cultural center of the city. The movement of the visitors in an exhibition room of the CCCB in Barcelona was tracked by six infrared depth camera sensors in order to study their movement pattern, specially focusing on where they are more likely to stop and for how long.