Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach
This work addresses food insecurity in the U.S. by enhancing the efficiency of aid distribution, though it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of optimizing food bank and pantry locations to improve accessibility for food-insecure families, resulting in proposed locations that are superior to existing ones and save significant distance for households, with a marginal penalty on food bank to pantry distance.
Over 44 million Americans currently suffer from food insecurity, of whom 13 million are children. Across the United States, thousands of food banks and pantries serve as vital sources of food and other forms of aid for food insecure families. By optimizing food bank and pantry locations, food would become more accessible to families who desperately require it. In this work, we introduce a novel two-level optimization framework, which utilizes the K-Medoids clustering algorithm in conjunction with the Open-Source Routing Machine engine, to optimize food bank and pantry locations based on real road distances to houses and house blocks. Our proposed framework also has the adaptability to factor in considerations such as median household income using a pseudo-weighted K-Medoids algorithm. Testing conducted with California and Indiana household data, as well as comparisons with real food bank and pantry locations showed that interestingly, our proposed framework yields food pantry locations superior to those of real existing ones and saves significant distance for households, while there is a marginal penalty on the first level food bank to food pantry distance. Overall, we believe that the second-level benefits of this framework far outweigh any drawbacks and yield a net benefit result.