This chapter advocates a paradigm shift from traditional spatial modeling to context-rich approaches in examining the food environment and its impact on dietary health. While previous studies focus heavily on spatial distribution—proximity, density, and variety of stores—the underlying contexts that shape food choices often remain overlooked. By extending beyond simple spatial proxies, we emphasize the importance of environmental contexts, including nutrition quality, store attributes, cultural relevance, and people’s perceptions, in influencing food behaviors. Emerging geospatial artificial intelligence (GeoAI) provides powerful tools to identify, interpret, and visualize these contextual factors. Drawing from a case study in Hartford, Connecticut, we illustrate how AI-based nutrition assessment of geotagged food images generates fine-grained environmental insights that transcend the limitations of traditional space-based models. This approach not only advances the conceptual understanding of how contextual factors guide food choices but also supports policy interventions, such as menu labeling, that are sensitive to the complex interplay between place and perception. Our findings highlight the need for integrative frameworks that combine spatial analysis with contextual intelligence, paving the way for more nuanced, human-centered inquiries into the link between the food environment and public health.