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Non-profit hunger relief organizations rely on the goodwill of donors for their in-kind cash, food donations and other supplies to alleviate hunger, reduce human suffering and save lives. However, these organizations struggle with changing demand and supply patterns, disruptions caused by very low donations even though they must make strategic distribution decisions. Food distribution forecasts based on times series models can be useful for these decisions. Yet, it is plausible that food distribution by hunger relief organizations (and demand by the people in need) are driven by certain underlying factors. In this research, we used Visual Analytics (VA) to study the effect of certain underlying factors on the forecast generated for food distribution to the aid recipients. Specifically, we used already tested forecasting techniques to predict the expected quantity of distributed food for the underlying factors identified.