Civic and community planning today is able to draw from a wealth of mobility and location data generated by residents who both actively and passively participate in data collection. Mobility information can be gleaned from a number of sources, which are often collected and maintained by third-parties for uses in multiple, heterogeneous applications. However, when combined, these third-party, heterogeneous data can paint a rich picture of movement through the urban landscape, that could be an important source of information for community planners. However, the naive inclusion of heterogeneous data sources, such as through machine learning, presents a number of technical and social challenges that impact the privacy of city residents and raise important questions around fairness and social justice. The data is not uniformly distributed across communities, potentially leaving out important populations and biasing algorithmic decision making, and the highly detailed location information may leak information about residents that should be protected.

As part of this planning grant, this project will address basic research challenges associated with privacy as well as fairness in the application of heterogeneous data for community planning that take into account both resident participation and planner decision making. Including: (1) How do we both identify and mitigate potential biases in community planning that can arise from heterogeneous, third-party data sources? (2) How do we design privacy-aware algorithms that can merge disparate and incomplete data while providing meaningful results for community planning? (3) How do we combine principles of fairness and bias with privacy preserving data analysis methods? (4) How do we identify under-representation within data sources and explore factors that contribute to participation? (5) How do residents and community-planners understand privacy with respect to mobility data? Additionally, the project will seek to expand community partnership and outreach during the planning-grant process through community workshops to identify key aspects of the basic research and how the outcomes can be integrated into practice.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.