Urban Heartbeat: From Modelling to Applications

Sensors and actuators are finding their way into our lives and our surroundings at a very fast pace. These heterogeneous sensors deployed in the environment can prove to be useful in providing insights into the behavior and trends of the environment. In this work, we capture a part of that knowledge and propose a novel concept called Urban Heartbeat using data captured by various sensors that essentially identify periodic activities in the environment. The Urban Heartbeat can be leveraged to identify when an unexpected event has occurred or is about to occur to more effectively prepare the citizens. We first develop techniques to find couplings between sensors using multiple operators, in cases when direct measurement of a parameter is not possible. Next, we define an algorithm that can be used to find quasi-periodic patterns from time series data that has spatiotemporal deviations. We then introduce the notion of Urban Heartbeat, which leverages data from heterogeneous sensors to identify the normal heartbeat of the environment. The Urban Heartbeat can be used not only to differentiate between normal and abnormal trends thereby giving us the ability to detect anomalies but also in making predictions about the user or the environment behavior. We also show how we build heartbeat for a lab environment, learn useful information about the users and offer predictions about their behavior in the lab.