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.