Digital Urban Sensing: A Multi-layered Approach

Studies of human mobility increasingly rely on digital sensing, the large-scale recording of human activity facilitated by digital technologies. Questions of variability and population representativity, however, in patterns seen from these sources, remain major challenges for interpreting any outcomes gleaned from these records. The present research explores these questions by providing a comparison of the spatial and temporal activity distributions seen from taxi, subway and Citi Bike trips, mobile app records, geo-tagged Twitter data as well as 311 service requests in the five boroughs of New York City. The comparison reveals substantially different spatial and temporal patterns amongst these datasets, emphasizing limitations in the capacity of individual datasets to represent urban dynamics in their entirety. We further provide interpretations on these differences by decomposing the spatial distributions with working-residential balance and different propensities for demographic groups to use different activities. Nevertheless, the differences also highlight the opportunity to leverage the plurality to create multi-layered models of urban dynamics. We demonstrate the capacity of such models to advance urban zoning and socio-economic modeling - two common applications of digital urban sensing.