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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<title>Where to Build Food Banks and Pantries: A Robust Machine Learning Approach</title>
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<h1 class="title is-1 publication-title">Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach</h1>
<div class="is-size-5 publication-authors">
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Gavin Ruan</span>
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<span class="author-block"> West Lafayette Jr./Sr. High School <br>West Lafayette, IN, USA</span>
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<!-- <h2 class="subtitle has-text-centered"> -->
<!-- Food insecurity is a major issue in the United States. This project aims to optimize the location of food banks to better serve the community. -->
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<h2 class="subtitle has-text-centered">
Food insecurity is a major issue in the United States. Food banks play a crucial role in addressing food insecurity in the United States, serving as vital links between food surplus and need. This project aims to optimize the location of food banks to better serve the community.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Food insecurity is an extremely pressing issue in the United States, with over 44 million food insecure people. Food banks and pantries are two of the most important methods utilized to fight food insecurity, with over 49 million people turning to food programs in 2022. By increasing accessibility to food pantries and banks, the burden of food insecurity on families can be significantly lessened as they gain reliable access to food and other vital resources. The aim of the project is to create a machine learning application that is able to quickly and effectively optimize food bank and pantry locations in a hierarchical manner such that the distances between houses and food banks and pantries are less than that of real banks and pantries.. The application works with two levels of hierarchy, with the first one being top level food banks and the second one being partnered food agencies and pantries. The application utilized a robust k-medoids algorithm in conjunction with a distance matrix generated by the Open Source Routing Machine routing engine. A sampled dataset comprising households from a government GIS survey was sampled and used for both the initial clustering and later comparisons. A total of 11 food banks and 176 pantries were generated by the application, to compare against an equal number of food banks and pantries. The application proved to be both effective and successful. An average of 2.63 miles was saved regarding the distances between houses and food banks on the first level. Across all areas, an average of 3.52 miles was saved on the second level regarding the distances between houses and their closest food pantries, with average savings ranging from 2.17 to 8 miles.
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<h2 class="title is-3">Indiana State, food bank level</h2>
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<h2 class="title is-3">Indiana State, food pantry level</h2>
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