Understanding mobility in Rome by means of a multiplex network with data

Abstract

Complex networks provide a framework for modelling real-world systems. Based on a set of data on mobility by car between different urban areas of the city of Rome, we represent and analyze these mobility data coupled with urban public transport networks, augmenting the network nodes with data on commercial, economic, service and tourist activity in the city. In order to unravel the complex interdependencies of all these data, we propose a multiplex network consisting of four layers in which the nodes are defined by an urban grid subdividing the city into 1x1 km cells. Network centrality measures are then used to determine the most influential nodes or prominent areas of the city. In particular, we propose an adaptation of the APA centrality algorithm for multiplex networks. This adaptation of the algorithm for multiplex networks offers the possibility to assign the importance given to node data relative to the network topology in each layer when computing the centrality. This allows a wider control in studying the mobility network, particularly generating different centrality maps according to the choice of this control parameter in each layer. We carry out experiments and present the results of a study of the network centralities considering different choices of the parameter.