  The world is witnessing a period of extreme growth and urbanization; cities
in the 21st century became nerve centers creating economic opportunities and
cultural values which make cities grow exponentially. With this rapid urban
population growth, city infrastructure is facing major problems, from the need
to scale urban systems to sustaining the quality of services for citizen at
scale. Understanding the dynamics of cities is critical towards informed
strategic urban planning. This paper showcases QuantifiedCity, a system aimed
at understanding the complex dynamics taking place in cities. Often, these
dynamics involve humans, services, and infrastructures and are observed in
different spaces: physical (IoT-based) sensing and human (social-based)
sensing. The main challenges the system strives to address are related to data
integration and fusion to enable an effective and semantically relevant data
grouping. This is achieved by considering the spatio-temporal space as a
blocking function for any data generated in the city. Our system consists of
three layer for data acquisition, data analysis, and data visualization; each
of which embeds a variety of modules to better achieve its purpose (e.g., data
crawling, data cleaning, topic modeling, sentiment analysis, named entity
recognition, event detection, time series analysis, etc.) End users can browse
the dynamics through three main dimensions: location, time, and event. For each
dimension, the system renders a set of map-centric widgets that summarize the
underlying related dynamics. This paper highlights the need for such a holistic
platform, identifies the strengths of the "Quantified City" concept, and
showcases a working demo through a real-life scenario.
