Little data, big data, and food insecurity: An integrated approach

A steady increase in the prevalence of chronic diseases like obesity and diabetes has been a major issue in healthcare; and one of the reasons that led to that increase is food insecurity. In 2014, it has been estimated that about 14.3% of Americans were food insecure. In recent times, there has been an increased interest in improving accessibility to healthy and nutritious food amongst low-income households, especially amongst children. Improving access to food for low-income households and equity of distribution is a major concern to food banks across the country. We propose systematic analytics based technique integrating little data and big data to address this problem. We use the Central Pennsylvania Food Bank as a case study to analyze the relationship between the demand for resources from the food bank, the physical location of the people affected and the demographics within the 27 counties in Central Pennsylvania. We also use US Census data and USDA's research on food deserts. We conclude by presenting results about the impact of different possible factors affecting food insecurity and their importance in improving accessibility to food and equity of distribution of resources.