Hello all! I’m Isis, a senior at IU, doing research in the Critical Food Studies lab. As a new lab attendant, I've been honored to work alongside incredible mentors and students who have introduced me to critical issues involving the food system, especially in Southern Indiana.   As Black History Month comes to an end, I want to share about a journal article I am helping to write. The “White Supremacy at the Bloomington Community Farmers Market” piece, coauthored by Dr. Angela Babb and Megan Betz, details issues pertaining to the local food and agriculture system in Southern Indiana, as well as the residents of Bloomington.
 
   The goal of our article is to discuss the presence of white supremacy within the local food system, beyond the specific and significant issues that arose with the Bloomington Farmers Market in 2018. The appearance of white supremacy is part of a larger, painful history of systemic racism, which has colonized the food system and facilitated even greater food insecurity. The introduction of hate groups, inspired by white supmacist beliefs, has left marginalized residents of Bloomington feeling unsafe.
 
   One of the topics we also plan to elaborate on within the paper includes nostalgia surrounding farmers markets and local food. Nostalgia in the food system continues generational knowledge, at times nostalgia removes historical relevance. Agriculture, and the food system within the United States, historically has been shaped by Indigenous and Black communities. After colonization, food began to be taken over by Europeans, who did not respect the land. Now, many of those who are leading farmers are typically descendants of those colonizers and settlers, taking away space and land opportunity from BlPOC (Black, Indeginous, People of Color).
 
 Other topics in the paper include public safety regarding the presence of hate groups affiliated with white supremacy, first amendment protections, and attention to community hate groups. Overall, the research for the article has involved readings to understand white supremacy in Southern Indiana, as well as coding content for Nvivo and retaining useful information for dismantling white supremacy in the local food system.
 
   Looking to find ways to unsubscribe from and dismantle white supremacy? Thanks to diligent community members such as Dr. Angela Babb, farmers markets with the purpose of equality and accessibility have created a safe space for marginalized community members to buy local food. Examples of these markets in the community include the Peoples Market and the Plant Truck.

by: Kamila Czebotar

​               I’ve been working with the Critical Food Studies lab since October of 2019; while most of my work at the lab has been assistive, from data digging to paper reviewing, I am also positioned between the critical food studies lab and the Sustainable Food Systems Science team. I am currently working with the “Complex Food Provisioning Strategies and Food Insecurity among Low-Income Older Americans” project. Our main research objectives are determining what food insecurity looks like in older Americans, how different times of the year (as well as changes throughout their lives) affect food security, and what food programs around local and rural south-central Indiana exist and how they might be improved.
         As part of this work, I was involved in attending focus groups in 4 different counties with service providers and seniors in the area, and from these groups I gained a better understanding of how food insecurity affects seniors. One major finding I have taken away from the focus groups is how physical barriers affect food insecurity, including personal health issues (like mobility or chronic diseases) as well as physical access (for example transportation); Other important issues came up, such as the financial accessibility of food and how living alone affects eating habits. Through all these and other obstacles, however, seniors in south central Indiana have adapted to these common food insecurity issues by supporting each other in number of ways, including sharing meals and rides, learning how to adapt to income differences through stretching food, sales, and the use of food pantries, meal sites, and SNAP. Lastly, we gained information on the problems and recommendations of local assistance programs, including the loss of meal sites, rules that restrict services, and critiques of the quality of foods offered in these programs.
           After attending these focus groups my work involved creating summaries and transcribing the audio for further analysis, and headway into the next phase of the project: coding the transcripts through Nvivo and working with a smaller team to compare findings and outline the main themes of the focus groups. As we have wrapped up the focus groups our next phase involved creating and sending out surveys to a larger population of older Americans in rural Indiana.
              In the midst of creating the survey, COVID-19 hit Indiana and our team decided to include questions relevant to circumstances before and during the COVID-19 crisis to better understand the impact the virus has on food security amongst seniors. We hope to gain more information through the survey answers as well as the post-survey interviews we plan to conduct this summer. While working with this team I have learned many new skills like qualitative analysis of transcripts, holding focus groups, and creating a survey; I hope to continue gaining skills as we further our research this summer and fall.

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.  

​Hello! My name is Marie and I’m currently a third year student studying Environmental and Sustainability Studies with a concentration in Sustainable Food Systems. I’m also working toward certificates in Geographic Information Systems and Global French Studies.
    Currently, I am researching institutional purchasing habits with my advisors, Jodee Ellett and Angela Babb. Last fall a survey was sent out through qualtrics to Indiana institutions such as K-12 schools, universities and colleges, hospitals, and correctional facilities. The survey included questions about how many people the institutions feed as well as how much money they spend annually. The survey then inquiries about local food purchasing and how often institutions are considering where their food is coming from. At this point, two rounds of the survey have been sent out and we are analyzing the results.
             In Indiana, 90% of food sales come from out of state purchases. That is to say, only 10% of the food that we eat was grown or processed in Indiana. The money that we are spending on food is not stimulating Indiana’s economy. This statistic is especially shocking considering over three fifths of the land in Indiana is agricultural land. The purchasing of food by K-12 schools, universities, hospitals, and other institutions is incredibly important. For example, more than one million lunches are served in Indiana K-12 schools each day. Considering this enormous purchasing power, a shift in where purchased food is grown would have a substantial impact.
              Oftentimes, institutions are unaware of how to source food locally. When you are concerned with feeding over 5,000 people each day, it seems a bit cumbersome to switch up where you source your food from. Hopefully this research highlights the opportunities for farmers and institutions to work together. Of course, this could be catalyzed through government policy. Considering this research is modeled after research done on institutional purchasing in Vermont, the possibility exists for Indiana institutions to be incentivized to prioritize purchasing local food. 

Co-director Angela Babb was awarded Outstanding Mentor of the Year by Sustain IU in May of this year. The award recognized her many years of researching sustainable food systems and guiding students. Angela is a critical food geographer whose work examines the political economy of hunger and the contemporary food movement. As a postdoctoral fellow, she has mentored four undergraduate students in Sustain IU's Sustainability Scholars program over the last two years. The award noted her constant support of students, offering help whenever needed, and her work on the campus's annual Real Food data analysis. 

by Matt Leetz
​I’ve been working in the Critical Food Studies Lab for the past two semesters, or my entire senior year.  Over this time, I’ve been building a mathematical spreadsheet model to analyze the presence and intensities of Food Desert regions in the Chicagoland Metropolitan Statistical Area, which covers the area of Chicago as well as its suburbs expanding into Indiana and Wisconsin.  I’m interested in utilizing geographic problem solving methods and applying them to human geography, in the hope that we can better understand how these problems come to be and aid in the quest towards food security for all Americans.  The end product of this project will be an interactive data model overlaid on a map of the area that highlights where food insecurity is most intense and provide qualitative information about the food vendors that do exist. 
            Last semester was spent collecting data to put into the model.  There are five matrices involved in the Maximal Covering Location Problem.  We are using the MCLP, introduced by Church and ReVelle in 1974, to solve a discrete (accessible by the network of roads) problem of coverage by locating food facilities within a maximum allowed distance to customers.  We set this as an arbitrary drive-time of 20 minutes, which we can increase or decrease as our independent variable.  The first matrix is a distance matrix, where we have calculated drive times between all of the Census blocks in the Chicagoland MSA.  This by far took the most time to prepare, as the workflow handed down to me involved manually querying each address, receiving a JSON file and extracting information from that into the format we needed.  Luckily, we were able to use ArcMap’s Network Analyst function instead, which cut the workload from ~400 hours to 20 minutes.  Next comes the population matrix.  I was able to collect population data from the US Census Bureau in the form of shapefiles, a filetype used by Geographic Information Software such as ArcMap and QGIS.  I was also able to find population centroid data, or the geographically weighted mean of the population of each Census block, and apply it to the shapefiles. 
            With those two out of the way, the other matrices needed are the Constraint matrix (maximum allowed drive time), the Assignment matrix (a way of applying the many internal constraints necessary for the model to function), and the Output matrix (a blank matrix growing impatient to be filled).  These were easy to create in Excel this semester, and I have since been cleaning and perfecting the model.  This model has nearly 2200 constraints (which I was able to create through a Python script) and 4.7 million variables, making it extremely taxing on computers.  We have a specialized computer in the Food Institute now dedicated to running this model as it was built, but at its current rate will take 3-5 months to finish the process.  I am now attacking this problem from an alternative route: using Matlab code to perform this process through Mixed Integer Non-Linear Programming, which we can then run on the IU supercomputing cluster through remote access. 

My work in the Critical Food Studies Lab has revolved around understanding food deserts in Central Indiana. A large part of my work is “tool-based”, meaning I apply many different Geographic Information Systems (GIS) techniques in order to discover and illuminate trends not easily appreciated within large datasets. Additionally, my work revolves around applying mathematical principles to the challenge of food deserts. Overall my work is data based, with large importance being placed on the interpretation and manipulation of data.

Much of the work I performed this fall was concerned with data mining and database building. Geographically, my research location consists of a two-county ring surrounding Marion County, Indiana.  In these counties, census tract data were collected concerning population sizes and each census tracts geographically weighted center. From the population data, a “W-Matrix” was built to address the population size of each census tract. From the centroid data, a “D-Matrix” was built to analyze the time it would take to drive from each centroid to every other centroid. In order to create the D-Matrix, I used a network analysis tool in ArcMap (the program I use to perform my GIS tasks). In ArcMap, there is a series of network analysis tools that perform a variety of functions.  As the name suggests, each network analysis function is dependent on a “network” of sorts in order to perform each said analysis. For my work, I used a “network” of streets and centerlines consisting of every street and centerline in North America.

For my analysis, I elected to use an OD cost matrix. This tool is ideal for the D-Matrix due to the values it accumulates. The OD cost matrix accumulates both time and distance values between two points. The OD cost matrix draws “lines” based on the network given accumulating data on the distance and time it would take to traverse these “lines”. The lines form a matrix but they are in actuality routes that carry data, very similar to the routes seen in Google Maps for example. In ArcMap, all constraints were lifted with the exception of what vehicle I would use. Each time was calculated using a single occupancy vehicle. And so through performing the OD cost matrix a network of lines was created. A line is produced traveling from each centroid to every other centroid. With a total of 530 census tracts, the OD cost matrix consisted of 280,900 lines. Now that’s a lot of lines! From those lines, I extracted all the time data and created a time database within Excel. From the time database, all the data is synthesized into the D-Matrix.  The D-Matrix is rather large; keep in mind its 530 by 530 census tracts, a total of 280,900 data points. In order to synthesize the D-Matrix in a quick and easy manner, I used an OFFSET function within Excel. I also tried to build and use a macro for this but found the OFFSET function to be simpler and much more user-friendly. For those unfamiliar with Excel or the OFFSET function, it is a tool that moves columns or rows of data into a matrix. Rather than copying and pasting 530 columns of data, the OFFSET formula will do it quickly and easily. If you are having trouble moving columns of data into a matrix, much like I was, here is an example of the formula I ended up creating: =OFFSET(drv_tms!$F$2:$F$279842,COLUMN()-COLUMN($D$4)+((ROW()-ROW($D$4))*(ROWS(drv_tms!$F$2:$F$279842)/529)),0,1,1) From the D-Matrix I then created an “S-Matrix” which is concerned with various time limits or constraints The S-Matrix is a binary analysis of the D-Matrix. It analyzes the time it takes to travel from each centroid under certain time limits. The time limits I used for the S-Matrix were 5, 10, 15, 20, 25, 30, and 35 minutes. Again the S-Matrix is binary, meaning, for example, with the 5-minute time constraint, if the travel time was under 5 minutes a “1” was used to represent that line. If the travel time was over 5 minutes a 0 was used. Binary for this example is just true and false, 1 being true and 0 being false. From these three matrices, my next step is to create covering models and running said models through solver.