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Recent advances in large-scale human mobility datasets have opened new opportunities to improve public health through data-driven strategies, advanced computational methods, and interdisciplinary approaches. A key focus in epidemiological research is the estimation and analysis of social contact patterns, representing the frequency and nature of interactions among different demographic groups. These patterns are vital for modeling disease transmission, evaluating public health interventions, and guiding resource allocation. However, obtaining accurate and representative contact data remains a major challenge. In this paper, we propose a novel, scalable framework for generating and analyzing large-scale social contact datasets derived from foot-traffic data. Our approach integrates statistical modeling to estimate demographic distributions, such as age groups, at millions of points of interest (POIs) across the United States and globally, including restaurants, stores, hospitals, and schools. This framework enables actionable insights to inform public health strategies and improve population health outcomes. Moreover, the resulting datasets have broad cross-sector utility, supporting applications in strategic business planning, resource distribution, and personalized marketing and advertising.