  With the increased focus on making cities "smarter", we see an upsurge in
investment in sensing technologies embedded in the urban infrastructure. The
deployment of GPS sensors aboard taxis and buses, smartcards replacing paper
tickets, and other similar initiatives have led to an abundance of data on
human mobility, generated at scale and available real-time. Further still,
users of social media platforms such as Twitter and LBSNs continue to
voluntarily share multimedia content revealing in-situ information on their
respective localities. The availability of such longitudinal multimodal data
not only allows for both the characterization of the dynamics of the city, but
also, in detecting anomalies, resulting from events (e.g., concerts) that
disrupt such dynamics, transiently. In this work, we investigate the
capabilities of such urban sensor modalities, both physical and social, in
detecting a variety of local events of varying intensities (e.g., concerts)
using statistical outlier detection techniques. We look at loading levels on
arriving bus stops, telecommunication records, and taxi trips, accrued via the
public APIs made available through the local transport authorities from
Singapore and New York City, and Twitter/Foursquare check-ins collected during
the same period, and evaluate against a set of events assimilated from multiple
event websites. In particular, we report on our early findings on (1) the
spatial impact evident via each modality (i.e., how far from the event venue is
the anomaly still present), and (2) the utility in combining decisions from the
collection of sensors using rudimentary fusion techniques.
