Predicting Food Distribution with Underlying Factors for a Hunger Relief Organization

ABSTRACT

Hunger relief organizations often estimate food demand using food distribution data. Leveraging Visual Analytics (VA) and historical data, we examine how underlying factors like unemployment, poverty rate, and median household income affect forecasts for aid recipients’ food demand. Our study reveals that incorporating these factors enhances forecast accuracy. Visual Analytics empowers decision-makers to integrate field knowledge with computational insights, enabling more informed decisions. This innovative approach presents a valuable tool for charitable organizations to strategically improve forecasting precision in the dynamic landscape of hunger relief.

KEYWORDS:

• Food distribution
• forecasting
• underlying factors
• visual analytics
• hunger relief

Acknowledgements

We would like to express our sincere appreciation to the leadership of the Food Bank of Central and Eastern North Carolina (FBCENC) for their valuable input to this project. This research was partially supported by NSF grant (#1718672) titled PFI: BIC - Flexible, equitable, efficient, and effective distribution (FEEED), NSF grant (#1735258) titled NRT: Improving strategies for hunger relief and food security using computational data science, and NSF grant (#2100855) titled:” EiR: Human Centered Visual Analytics for Evidence Based Decision Making in Humanitarian Relief.”.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the NSF [1718672], NSF (1735258) and NSF (2100855).