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Description

Food insecurity remains a critical global challenge, and food banks play an essential role in addressing this issue by ensuring food reaches those in need. However, operational inefficiencies, inequitable distribution, and a lack of data driven decision-making often hinder their effectiveness. This research develops an advanced visualization framework that transforms raw data into actionable insights, enabling food banks to optimize resource allocation, enhance equity, and improve operational efficiency. By integrating demographic, socio-economic, and geographic data, this study introduces interactive dashboards that provide real-time intelligence on food preferences, service area coverage, and allocation disparities. The framework employs intuitive navigation, standardized visual hierarchies, and structured categorization to enhance usability for decision-makers. Furthermore, census tract- level mapping and heatmaps reveal spatial trends, helping identify underserved communities and gaps in food distribution strategies. Beyond operational improvements, this work establishes a scalable, replicable model for humanitarian logistics, demonstrating how data visualization can bridge the gap between analytics and real-world impact. Future work will focus on developing a standardized visualization framework that serves as a go-to reference for food banks, policymakers, and humanitarian organizations, enabling them to adapt and customize data-driven insights for their specific needs

Publication Date

4-1-2025

Keywords

Food Insecurity, Food Banks, Visualization, Neighbor Food Preference

Transforming Food Bank Operations with Data Visualization: A Study on Neighbor Food Preferences

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