The focus of this project is on creating new techniques for understanding population analytics over a space of interest, e.g., a shopping mall, a busy street, or an entire city. Knowledge of population behavior important for many applications. For instance, knowledge of which are the busy corners of city sidewalk can provide city planners with input on where to invest city resources. Knowledge of where people congregate in a shopping mall allows officials to plan where to provide useful services, e.g., information kiosks, floor plans, and more. The process of gathering population analytics today is tedious -- some stores and shops use manual people counters to track how many persons are entering wireless technologies. The technical contributions of this project are two-fold. First, it is attempting to reduce the complexity of determining location of people by reducing the number of infrastructure points needed. Second, automated approaches to population analytics are fraught with privacy concerns, and this project is examining techniques that mitigate such concerns. Personnel involved in this project will be trained in significant technical skills across a broad set of domains including wireless technologies, privacy techniques, and machine learning. To demonstrate the feasibility of this project, the PI team is deploying a version of the system in an urban downtown area of Madison, WI. The team is collaborating with a number of local partners -- the city of Madison, the University of Wisconsin Bookstore, 5NINES (a local Internet Service Provider), and a few local participants. Together they are entering this technology demonstration as part of the Global City Teams Challenge being hosted by NSF and NIST.