Study of links between people in urban areas based on mobility data for the city of São Paulo

Abstract

Our study explores the average degree and clustering of a complex mobility network designed to model and simulate the COVID-19 pandemic. To construct this network, we utilized mobility data collected in São Paulo, creating a network in which each node represents an individual, and each edge weight denotes the duration of contact between individuals during a typical day. By analyzing data from an Origin-Destination Research, we calculated the average degree and weighted clustering coefficient of the network for various minimum contact duration. We aimed to understand the effect of increasing minimum contact duration on network structure. Our findings indicate that networks with different minimum contact duration remained sparse, as the average degree of the generated graphs decreased.