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Food banks are at the forefront of the battle against food insecurity which is a condition where households do not have adequate access to food. Traditionally speaking, food banks focus on distributing food to meet the needs. Recently, more food banks are shifting to supply adequate healthy food based on the populations they serve. However, the question remains whether a local food bank can find racial communities in need with dietary considerations. This study's purpose is to use data collected by a local food bank and create visualizations to aid strategic decision-making for the food bank to recognize racial communities with those who have dietary considerations. Results revealed nine out of sixteen counties in the service area of the local food bank have the highest number of African Americans allergic to shellfish compared to a few counties having the highest number of Whites, American Indians, and Hispanic/Latinos. Additionally, 53.7% of African Americans, 11.2% of Hispanics and Latinos, and 34.3% of Whites face lactose intolerance. Data shows that African Americans have the highest number of dietary considerations in most categories that are in several counties. The significance of this study supports a local food bank in finding dietary considerations within the areas they serve. Finding racial communities that face dietary considerations will aid the local food bank in making better strategic decisions on what types of food they should serve and where. Ultimately, the importance of this study is to combat food insecurity and hunger, so that members of the local food bank community can have dignity in knowing the food that will be given is valuable and not wasted.

 

Food insecurity, an economic and social condition where households have limited access to nutritious food, is a long-standing and growing problem in both the rural and urban areas of the United States. Food deserts refer to areas that do not have adequate food access to affordable and nutritious food. Food deserts can be characterized by availability, accessibility, accommodation, affordability, and acceptability (5A’s). This research investigates food deserts in Mecklenburg County, North Carolina, which includes the city of Charlotte. The food insecure population estimate in Mecklenburg County is about 15% which is higher than the national average of 11%. Using visual analytics, a combination of analytics and human factors, this study aims to examine how age, education, and income are related to food deserts in Mecklenburg County. Data gathered from US Census Bureau was used for analysis. Results indicated that education and food desert status, and age and food desert status in Mecklenburg County were related.

 

Food insecurity is defined as an individual or household’s inability or limited access to safe and nutritious food that every person in the household need for an active, healthy life. In this research, we apply visual analytics, the integration of data analytics and interactive visualization, to provide evidence-based decision-making for a local food bank to better understand the people and communities in its service area and improve the reach and impact of the food bank. We have identified the indicators of the need, rates of usage, and other factors related to the general accessibility of the food bank and its programs. Interactive dashboards were developed to allow decision-makers of the food bank to combine their field knowledge with the computing power to make evidence-based informed decisions in complex hunger relief operations.

 

Fleet maintenance is the process fleet manager utilizes to manage fleet and asset information from acquisition to disposal. It helps the companies reduce costs, improve efficiency and safety. Second Harvest of Metrolina Food Bank (SHMETROLINA) distributed over 70 million pounds of food and household items to approximately 800 partner agencies in 2019. With the critical need for transportation for distribution, a vehicle experiencing downtime will disrupt scheduled routes to partner agencies and increase repair costs for SHMETROLINA. This research developed an interactive dashboard using R shiny to help non-profit food bank fleet managers make informed decisions for effective fleet maintenance operations and support the food bank operations to meet hunger needs. The dashboard consists of the visualizations of the maintenance cost, mileage, and operational cost for individual and fleet vehicles from the data collected by the food bank. 

Our goal in this work is to build effective yet robust models to predict unreliable and inconsistent in-kind donations at both weekly and monthly levels for two food banks across coasts: the Food Bank of Central Eastern North Carolina in North Carolina and Los Angeles Regional Food Bank in California. We explore three factors: model, data length, and window type. For the model, we evaluate a series of classic time-series forecasting models against the state-of-the-art approaches such as Bayesian Structural Time Series modeling (BSTS) and deep learning models; for the data length, we vary training data from 2 weeks to 13 years; for the window type, we compare sliding vs. expanding. Our results show the effectiveness of different models heavily depends on the data length and the window type as well as characteristics of the food bank. Motivated by these findings, we investigate the effectiveness of employing an average of all predictions formed by considering all three factors at both monthly and weekly levels for both food banks. Our results show that this average of predictions significantly and consistently outperforms all classical models, deep learning, and BSTS for the donation prediction at both monthly and weekly levels for both food banks. 

Food banks are nonprofit hunger relief organizations that collect donations from donors and distribute food to local agencies that serve people in need. Donors consist of local supermarkets, manufacturers, and community organizations. The frequency, quantity, and type of food donated by each donor can vary each month. In this research, we propose a technique to identify the supply behavior of donors and cluster them based on these attributes. We then develop a predictive ensemble model to forecast the contribution of different donor clusters. Our study shows the necessary behavioral attributes to classify donors and the best way to cluster donor data to improve the prediction model. 

Hunger relief organizations are mostly non-profits that collect food from various sources and redirect them to the people in need. This is to combat the prevalent food insecurity affecting children, the unemployed, students, seniors and so on. Previous research has focused on the demand/donation side of food rescue operations, but the distribution or supply side - especially in reducing the uncertainty associated with food demand - has received significantly lower attention. In this study, we obtained data from a local hunger relief organization, specifically a food pantry to develop estimates of the demand they expect to receive in the future. To do this, we fit the growth of the food pantry client population to a logistic growth model to obtain a good fit. We then obtained data for frequency of visits to develop estimates of the number of visits expected in the future, using time series models. This will be combined with the allocation policy for food distribution to develop estimates of true demand. This study has merit for hunger relief organizations. It will aid decision making relative to food distribution, while also providing data for planning purposes. 

North Carolina is the third most hurricane-prone states in the US. In 2018, Hurricane Florence caused a lot of damages to households in North Carolina. The Food Bank of Central and Eastern North Carolina (FBCENC) serves 34 counties in North Carolina, and 22 of them were affected by Hurricane Florence. This research aims to investigate the impact of Hurricane Florence on the operations of FBCENC. We developed interactive dashboards to visualize food bank operational data and other relevant data and studied the trends and patterns of food distribution in three key stages: preparedness, response, and recovery. These dashboards enable food bank operations managers to explore and interact with the data with ease to explore the operational data at different stages, at different branch level, and on a different time scale (monthly, weekly, or daily). The impact on the operations of affected service areas vs. not affected areas could be investigated as well. The findings of this research will provide insight into how humanitarian relief agencies can better prepare for, respond to, and recover from the disruptions caused by hurricanes.