2 research outputs found
Twitter Activity Timeline as a Signature of Urban Neighborhood
Modern cities are complex systems, evolving at a fast pace. Thus, many urban
planning, political, and economic decisions require a deep and up-to-date
understanding of the local context of urban neighborhoods. This study shows
that the structure of openly available social media records, such as Twitter,
offers a possibility for building a unique dynamic signature of urban
neighborhood function, and, therefore, might be used as an efficient and simple
decision support tool. Considering New York City as an example, we investigate
how Twitter data can be used to decompose the urban landscape into
self-defining zones, aligned with the functional properties of individual
neighborhoods and their social and economic characteristics. We further explore
the potential of these data for detecting events and evaluating their impact
over time and space. This approach paves a way to a methodology for immediate
quantification of the impact of urban development programs and the estimation
of socioeconomic statistics at a finer spatial-temporal scale, thus allowing
urban policy-makers to track neighborhood transformations and foresee
undesirable changes in order to take early action before official statistics
would be available
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.Comment: 17 pages, 12 figure