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Understanding the dynamics of food banks' demand from food insecurity isessential in optimizing operational costs and equitable distribution of food,especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM)clustering is selected to extract patterns. The novelty is that GMM clusteringis applied to identify the possible causes of food insecurity in a givenregion, understanding the characteristics and structure of the food assistancenetwork in a particular region, and the clustering result is further utilizedto explore the patterns of uncertain food demand behavior and its significantimportance in inventory management and redistribution of surplus food therebydeveloping a two-stage hybrid food demand estimation model. Data obtained froma food bank network in Cleveland, Ohio, is used, and the clusters developed arestudied and visualized. The results reveal that this proposed framework canmake an in-depth identification of food accessibility and assistance patternsand provides better prediction accuracies of the leveraged statistical andmachine learning algorithms by utilizing the GMM clustering results. Also,implementing the proposed framework for case studies based on different levelsof planning led to practical results with remarkable ease and comfort intendedfor the respective planning team.

GMM Clustering for In-depth Food Accessibility Pattern Exploration and Prediction Model of Food Demand Behavior