A longitudinal cohort study is a popular research method to observe a group of people over a prolonged period of time, e.g., to learn about their health, wellness, and social habits. Smartphones have become a very popular tool to perform such studies at a large scale. Location is an essential form of sensor data that can not only be used to monitor users' mobility and social interaction patterns, but also to identify places of personal significance, i.e., places where a user spends a significant amount of time, such as a user's home, workplace, and preferred social gathering places. However, continuously tracking a user's location can have significant impacts on the battery lifetime of a smartphone. Therefore, instead of frequent period location sensing, this paper identifies smartphone events that can be used to trigger location sensing at a much lower rate (and therefore more energy-efficiently), while still providing accurate location data. In this work, we demonstrate that this approach allows us to determine a user's significant places with an accuracy of 85%, while saving over 60% in computational and energy overheads.
