Summary of Where to Build Food Banks and Pantries: a Two-level Machine Learning Approach, by Gavin Ruan and Ziqi Guo and Guang Lin
Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach
by Gavin Ruan, Ziqi Guo, Guang Lin
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed two-level optimization framework utilizes K-Medoids clustering and Open-Source Routing Machine engine to optimize food bank and pantry locations based on real road distances to houses and house blocks. The framework also considers median household income using a pseudo-weighted K-Medoids algorithm. Testing with California and Indiana data shows that the optimized locations are superior to existing ones, saving significant distance for households while incurring a marginal penalty on first-level food bank to food pantry distance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Food banks and pantries help families struggling with food insecurity, but some areas lack access to these vital resources. A team of researchers created an innovative way to find the best locations for food banks and pantries by using real distances from houses and considering factors like income. They tested this method with data from California and Indiana and found that it outperforms current locations, saving time and distance for those in need. |
Keywords
» Artificial intelligence » Clustering » Optimization
