Addressing Urban Food Insecurity Through Data-Driven and Community-Centric Smart City Frameworks

In the evolving landscape of smart city development, addressing food insecurity remains a critical challenge. This paper presents an innovative interdisciplinary approach that combines sensing technologies, data analytics, and community engagement to deliver holistic solutions to food access disparities. Through a comprehensive analysis, we first identify key factors that contribute to food insecurity, and then present a system-theoretic model to address challenges in the fundamental components of the food system: production, processing, distribution, and consumption. We also share insights from a virtual workshop with community advocates and organizations, highlighting promising practices in data collection, experiences during the COVID-19 pandemic, and the ongoing need for equity in food access efforts. Finally, we present a case study from the Greater Richmond Region, Virginia that further exemplifies the potential of geospatial data and predictive analytics. This work enables the identification of food deserts at a granular scale and provides estimates of household food service demand. By integrating data on grocery store locations, urban road networks, demographic indicators, and public transit accessibility, we apply predictive modeling techniques that can be adapted to other urban areas. Our findings demonstrate the power of interdisciplinary research combined with community-centered strategies to drive sustainable and equitable improvements in food access within smart city frameworks. This study bridges technological innovation—including predictive analytics, geospatial modeling, and system-theoretic tools—with lived community insights to identify food system gaps and support data-informed, locally relevant interventions.