Smart City Gnosys
Smart city article details
| Title | Addressing Urban Food Insecurity Through Data-Driven And Community-Centric Smart City Frameworks |
|---|---|
| ID_Doc | 6413 |
| Authors | Zohrabi N.; Jones J.C.; Keegan B.; Adhikari S.; Verrelli B.C.; Abdelwahed S. |
| Year | 2025 |
| Published | IEEE Access |
| DOI | http://dx.doi.org/10.1109/ACCESS.2025.3589963 |
| Abstract | In the evolving landscape of smart city development, food insecurity remains a pressing and complex challenge. This paper presents an innovative interdisciplinary approach that integrates sensing technologies, data analytics, and community engagement to deliver holistic solutions to disparities in food access. Through a comprehensive analysis, we identify key contributing factors to food insecurity and develop a system-theoretic model to address challenges across the core components of the food system: production, processing, distribution, and consumption. Insights gathered from community advocates and organizations during a virtual workshop highlight the value of combining qualitative and quantitative data, reveal the disruptions and adaptations prompted by the COVID-19 pandemic, and underscore the ongoing importance of equity in food access initiatives. A case study from the Greater Richmond, Virginia Region illustrates the practical application 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, 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 uncover food system gaps and support data-informed, locally relevant interventions. © 2013 IEEE. |
| Author Keywords | Community-centric solutions; data collection and situation monitoring; food access equity; food insecurity; food system; interdisciplinary; predictive analytics; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58375  | 0.909 | Zohrabi N.; Linkous L.; Eini R.; Adhikari S.; Keegan B.; Jones J.C.; Gooden B.; Verrelli B.C.; Abdelwahed S. | Towards Sustainable Food Security: An Interdisciplinary Approach | Proceedings - 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021 (2021) |
| 29446 | 0.872 | Azmat F.; Safdar M.; Imran A.; Arshad M.; Shafiq A.; Afzaal M.; Naseer M.S.; Ahmed H.; Aijaz M.; Tahir S.; Islam F.; Zahra S.M. | How Sustainable Food Systems Shape Smart Cities | EAI/Springer Innovations in Communication and Computing, Part F3967 (2025) |
| 59563 | 0.87 | Hsu Y.-Y.; Han H. | Understanding Urban Food Systems: A Spatial And Temporal Analysis In Sydney, Australia | Cities, 155 (2024) |
| 59840 | 0.862 | Ferreira A.C.D.; Titotto S.L.M.C.; Akkari A.C.S. | Urban Agriculture 5.0: An Exploratory Approach To The Food System In A Super Smart Society | International Journal of Mathematical, Engineering and Management Sciences, 7, 4 (2022) |
| 1876 | 0.856 | Sanyal, S; Singh, VK; Xhafa, F; Sanyal, B; Mukhopadhyay, S | A Game Theoretic Framework For Surplus Food Distribution In Smart Cities And Beyond | APPLIED SCIENCES-BASEL, 11, 11 (2021) |
| 35500 | 0.853 | Amuthasurabi M.; Maaran C.T.; Vyas H.; Prakash R.K.; Arvindhan S.P.; Ashwin B. | Location And Weather-Based Prediction Of Yields Using Machine Learning Algorithms In Smart Cities | Deep Learning and Blockchain Technology for Smart and Sustainable Cities (2025) |