  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.
