Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach

TL;DR

This paper presents a two-level machine learning framework that optimizes food bank and pantry locations using clustering and routing algorithms, improving accessibility and reducing travel distances for food-insecure households.

Contribution

It introduces a novel two-level optimization approach combining K-Medoids clustering and routing to enhance food pantry placement based on real road distances and socioeconomic factors.

Findings

Optimized locations outperform existing food bank and pantry placements.

Significant reduction in household travel distances to food aid sources.

Framework demonstrates adaptability to socioeconomic considerations.

Abstract

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.