As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit . These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones . While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission . Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies . This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models . Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses . Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders . Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.