Hi! My name is Oliver Allen, but most people call me Mel. I’m a second year student studying Computer Science and Math with a minor in Art History, and I also play marimba when I’m not studying or writing programs. I started working at the Critical Food Studies Lab in January of 2019 doing basic statistics, but now I am focused on applying my skills in computer science and machine learning to analyze data surrounding food justice and sustainability.
I started the semester by working with Lab Director Angela Babb to create a dataset of average food consumption and nutrient profiles to assist in analysis of the Thrifty Food Plan calculation. The Thrifty Food Plan is the basis of how the USDA allocates Supplemental Nutrition Assistance Program (SNAP) benefits to over 42 million people annually, but the latest released calculation was done with data from 2001, which is nearly 20 years old. In order to perform a relevant analysis of the Thrifty Food Plan calculation we needed an updated dataset of food consumption and relevant nutrient profiles for each of the foods in the USDA Dietary Guidelines for Americans. I had to approximate the dataset used by the USDA with something built from publically available data, so I ended up downloading all of the responses to the National Health and Nutrition Examination Survey (NHANES) for 2015-2016 and working with the data in R to separate it into each of the age-sex groups specified by the Thrifty Food Plan. Each row was a food group (for example provolone cheese, roast beef, Dippin’ Dots, etc.) and there were two columns representing national consumption data and then information on the food’s nutrient profiles. Eventually we had a dataset that could at least in part approximate the one used for the Thrifty Food Plan calculation and facilitate further analysis of the USDA’s model.
In the latter half of the semester I worked to apply machine learning techniques to data about behavior surrounding switching to a sustainable diet. I used data from a survey given by the Sustainable Food Systems Science research group at IU to approximately 500 heads of households in Indiana, and I implemented a machine learning model to predict behavior around switching to a sustainable diet from demographic data. I also used this model to analyze which demographic features are most important when deciding whether a person will switch to a sustainable diet or not, or whether they will switch gradually over time versus switching quickly due to an event or crisis. I built additional models for each of these problems to further analyze the influence of each feature in the prediction, and I hope to continue looking into applications of machine learning with data around food security and sustainability to gain insight into why people make the choices they do surrounding food and diet.
I started working with the Critical Food Studies Lab in January 2019 and since then I have been involved in several different projects including gap year student Belen Roger’s Food Choice Project. This year, I have been focused on my Senior Thesis in Geography which combines historical and geographical qualitative research to uncover how spaces have evolved within the food industry to persistently devalue labor. It questions how individuals within this industry navigate living on a wage that is not equal with the cost of living in Bloomington, Indiana. With the food service sector employing 10% of Bloomington, Indiana’s working population and the mean hourly wage being $10.78, it is critical to analyze how this population depends upon social benefit programs and how they work through the Cliff Effect Phenomenon.
I conduct interviews of Bloomington residents working in the food service sector to determine how they navigate cliff effects within this specific work environment. Additionally, I analyze the growth of this sector through space and time in Bloomington with the help of historic maps and city directories from the 1950s to the 2000s to observe how it matches with the rise of the cliff effect of poverty as social benefits were instituted. I utilize one local tool, Indiana’s Self-Sufficiency Standard, and MIT’s living wage calculator in order to question how implementing a living wage that considers geographical variation of poverty thresholds would tackle ‘cliffs’ of poverty for those in the food industry in Bloomington, Indiana. I tie this question into urban development patterns in Bloomington regarding the locations of fast food in order to glean information about the value of food and labor in a mid-size, midwestern town.
The lab has played an integral part to my motivation in this endeavor and has helped me think critically through problems I have come across in my research. I was able to send out my literature review and receive helpful feedback on gaps I may have not included, and they gave constructive feedback on ways to help improve the overall flow of the paper. It is encouraging and exciting to gather each week to share progress on our projects or hiccups we have come across and, in the end, it sharpens all the lab members involved. I know the skills I have learned through my time in the Critical Food Studies Lab will be crucial to the line of work I enter into and it will also open doors for important work in the future.